JP3185559B2 - Surface defect inspection equipment - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an apparatus for inspecting a surface defect of an object to be inspected, for example, a surface defect such as unevenness of a painted surface of an automobile body.

[0002]

2. Description of the Related Art As a conventional surface defect inspection apparatus, for example, JP-A-2-73139 and JP-A-5-45142 are known.
For example, there is one disclosed in Japanese Patent No. 45144. These show a predetermined light and dark fringe (stripe) pattern on the surface to be inspected, and when there is a defect such as unevenness on the surface to be inspected, a received light image having a difference in brightness (brightness) and a change in brightness (brightness) due to the defect. Is differentiated to detect a defect on the surface of the surface to be inspected. Further, in the above conventional example, there is a defect that is not a defect such as a processed part on the surface to be inspected, and when it is present in a received image, the luminance level at the defect and the luminance level at a non-defect part such as the processed part are determined. There is described a technique for distinguishing a defect from other parts by utilizing the difference between the two.

[0003]

However, the conventional surface defect inspection apparatus as described above has the following problems. That is, in the conventional surface defect inspection apparatus,
This is to detect a defect appearing black in a bright portion of a stripe pattern, and cannot detect a defect existing in a dark portion of a stripe. As a result, only the bright portion in one screen is inspected, so that the area that can be inspected in one screen is narrowed, and when inspecting a wide inspection surface, the inspection must be repeated many times. was there.

[0004] For example, in the coating of an automobile body, what is generally called a surface defect is a projection on the coating surface resulting from the application of dust and the like, for example, having a diameter of 0.5 mm or more. The thickness is about 2 mm and the thickness is about several tens of μm. Since the height (thickness) of the projections of this degree is relatively large although the diameter is small, the irregular reflection angle of light becomes large and is easily noticeable. On the other hand, there are irregularities that do not become defects. That is, since the concentration of the paint is not strictly constant due to the eddy convection generated in the process of evaporating the paint solvent, the thickness of the coating film is extremely small (low).
Irregularities occur periodically. In this unevenness, for example, the interval between the peaks is about 1 to 10 mm, and the height of the unevenness is about several μm. Such extremely thin irregularities are of such a degree that they are usually not noticed and do not become defects. However, it may look like "yuzu" or "orange" depending on the degree of light, so it is called "yuzu skin" or "orange skin". In the conventional example as described above, since the luminance change is emphasized and detected, extremely thin irregularities that do not become a defect like the above-mentioned “Yuzu skin” are also detected, and this may be erroneously determined as a defect. For example, in Japanese Unexamined Patent Application Publication No. 2-73139, since the defect detection process is performed using illumination having a narrow stripe pattern (1.5 mm or less), the above-mentioned “yuzu skin” is formed on the surface to be inspected.
When the stripes are formed, the stripe itself and its boundary (line) are greatly disturbed. For example, a black portion of the stripe becomes an isolated point, and an erroneous detection occurs when the width of the stripe on the image is not constant. Cheap. As described above, in the related art, there is a problem that extremely thin unevenness that does not become a defect such as so-called “yuzu skin” may be erroneously detected as a defect.

The present invention has been made to solve the problems of the prior art as described above, and a first object of the present invention is to provide a surface defect inspection apparatus having a wide area that can be inspected on one screen. A second object is to provide a surface defect inspection apparatus capable of performing more precise defect detection without erroneously detecting extremely thin irregularities that do not become defects such as so-called "yuzu skin" as defects. is there.

[0006]

In order to achieve the above object, the present invention is configured as described in the appended claims. FIG. 1 is a diagram corresponding to the claims of the present invention, in which (a) corresponds to claim 1, and a broken line portion in the figure corresponds to claim 2. In FIG. 1A, 10
Reference numeral 0 denotes a surface to be inspected, for example, a painted surface. Also, 10
Reference numeral 1 denotes an illuminating unit for projecting a predetermined light and dark pattern on the surface to be inspected, and is, for example, an illuminating device shown in FIG. Also,
An imaging unit 102 captures an image of a surface to be inspected and converts the light / dark pattern into image data of an electric signal.
It is a video camera such as a D camera. Reference numeral 103 denotes a boundary region identification unit that processes the image data obtained by the imaging unit 102 to identify a boundary region between a light portion and a dark portion of the light-dark pattern, and 104 denotes a high frequency region among the frequency components in the image data. And an image enhancement means 105 for extracting only an area whose level is equal to or more than a predetermined value, and a defect 105 for detecting, as a defect, a portion which is not the boundary area identified by the boundary area identification means 103 among the areas extracted by the image enhancement means 104. It is a detecting means. These 103-105
Is composed of, for example, a computer. The above configuration corresponds to, for example, an embodiment described later with reference to FIGS.

Next, as shown by a broken line in FIG. 1A, a boundary area processing means 106 for performing processing for enlarging the boundary area obtained by the boundary area identification means 103.
Is inserted between the boundary area identifying means 103 and the defect detecting means 105 of the first aspect. This configuration corresponds to, for example, an embodiment shown in FIGS. According to a third aspect of the present invention, the boundary area processing means enlarges the boundary area by a predetermined value determined in accordance with the roughness of the surface to be inspected. Alternatively, as set forth in claim 4, the boundary area processing means enlarges the boundary area by a value corresponding to the roughness of the surface to be inspected obtained from the image data obtained by the imaging means. Alternatively, as described in claim 5, the boundary region processing means performs a process of temporarily expanding the boundary region and then reducing the boundary region.

[0008] According to a sixth aspect of the present invention, the image enhancing means is configured to calculate a difference between an original image of the image data obtained by the imaging means and an image obtained by performing a predetermined smoothing process on the original image. The absolute value is obtained. Alternatively, as described in claim 7, the image enhancing means obtains an absolute value of a result obtained by subjecting an original image of the image data obtained by the imaging means to a normal function and then performing a second derivative. .

The boundary area identifying means differentiates an image obtained by performing a smoothing process on an original image of the image data obtained by the imaging means, and obtains an absolute value of the result. Is what you want. According to the ninth aspect of the present invention, the image obtained by the boundary area recognition unit includes:
Either a smoothing process set based on the luminance histogram of the original image or the roughness of the surface to be inspected is performed, or the differential coefficient in the boundary area recognizing means is set based on the luminance histogram of the original image.

The defect detecting means determines a difference between the result of the boundary area recognizing means and the result of the image enhancing means, and determines the result by a predetermined threshold value. It is something to be valued. Further, the defect detecting means adjusts the binarization threshold based on a luminance histogram of an original image or roughness of a surface to be inspected.

Next, FIG. 1B corresponds to claim 12, and the broken line portion in the figure corresponds to claim 13. In FIG. 1B, reference numeral 100 denotes a surface to be inspected, for example, a painted surface. Reference numeral 101 denotes an illuminating means for projecting a predetermined light / dark pattern on the surface to be inspected.
The lighting device shown in FIG. Reference numeral 102 denotes an imaging unit that captures an image of the surface to be inspected and converts the light / dark pattern into image data of an electric signal, for example, a video camera such as a CCD camera. Reference numeral 107 denotes a smoothing unit for smoothing the image data obtained by the image pickup unit; 108, an emphasis unit for extracting a portion having a change in luminance in the image data of the light and dark pattern smoothed by the smoothing unit; Binarizing means 110 for binarizing the result of the emphasizing means 110 calculates each of the regions obtained by the binarizing means to thereby calculate the area of each part having a change in luminance existing in one screen. And an area determination processing means for detecting a portion whose area is equal to or smaller than a predetermined value as a defect. These one
The parts 07 to 110 are constituted by a computer, for example. The above configuration corresponds to, for example, an embodiment described later with reference to FIGS.

Next, as shown by a broken line in FIG. 1 (b), a value portion corresponding to a portion where the luminance changes in the binary signal obtained by the binarization means 109 is defined. An area processing means 111 for performing a process of enlarging an area is inserted between the binarization means 109 and the defect detection means 110 of the twelfth aspect. According to a fourteenth aspect of the present invention, the area processing means enlarges an area of a value part corresponding to the part having the luminance change by a predetermined value predetermined according to the roughness of the surface to be inspected. It is. Alternatively, as described in claim 15, the region processing means is a value part corresponding to a part having the luminance change by a value corresponding to the roughness of the surface to be inspected obtained from the image data obtained by the imaging means. Is to expand the area of the image. According to a sixteenth aspect of the present invention, the area processing means performs a process of temporarily enlarging an area of a value part corresponding to a part having a luminance change and then reducing the area.

[0013] According to a seventeenth aspect of the present invention, the area determining means performs an area determining process after removing a predetermined area in contact with a peripheral boundary of a screen in the binarized image. This configuration corresponds to, for example, the embodiment described with reference to FIG. Alternatively, as set forth in claim 18, the area determination means performs the area determination after setting one pixel in contact with the peripheral boundary of the screen in the binarized image to the same value as the dark portion of the stripe. .
This configuration corresponds to, for example, the embodiment described with reference to FIG.

[0014] According to a nineteenth aspect of the present invention, the illuminating means projects a pattern consisting of parallel stripes and lines obliquely crossing the stripes as a light and dark pattern. This configuration corresponds to, for example, the embodiment described with reference to FIG. Further, as described in claim 20, the illumination means projects a stripe-like light-dark pattern on the surface to be inspected. This configuration corresponds to, for example, an embodiment shown in FIGS. Alternatively, the illuminating means projects a grid-like light and dark pattern on the surface to be inspected. This configuration corresponds to, for example, an embodiment of FIG. 14 described later. Also, as described in claim 22,
The illuminating means is such that the width, that is, the interval between the light and dark portions of the light and dark pattern projected on the surface to be inspected is set to a sufficiently large value compared to the size of a defect expected for the purpose of inspection. It is. According to a twelfth aspect of the present invention, in the illumination unit, the distance between the bright and dark portions of the light and dark pattern, the ratio between the illumination unit and the surface to be inspected is the curvature of the surface to be inspected and the expected defect. This is set based on the angle between the target and the surface to be inspected.

Further, the invention according to claim 24 is provided with means for performing labeling processing and calculation processing of the area of the defect and the center of gravity based on the data on the defect obtained by the defect detecting means. Further, in the invention according to claim 25, a plurality of the imaging means and a plurality of the illumination means are arranged in an arch shape around the object to be inspected, and a wide inspected surface of the object to be inspected is inspected simultaneously. It is what was constituted. This configuration is described in, for example, FIG.
This corresponds to the third embodiment.

[0016]

As described above, according to the first aspect of the present invention, a predetermined light and dark pattern is projected on the surface to be inspected by the illuminating means, which is imaged by the imaging means and converted into image data of an electric signal. Next, the boundary region identifying means processes the image data to identify a boundary region between a bright part and a dark part in a low frequency region among the frequency components in the image data.
In this process, for example, a signal corresponding to a boundary region can be obtained by differentiating an image obtained by performing a smoothing process on an original image of an image signal and obtaining an absolute value of the result. Further, the image enhancing means extracts only a component having a high frequency component and a level equal to or higher than a predetermined value among the frequency components in the image data of the light and dark pattern. The high-frequency components in the above image data are a portion having a luminance change such as a defect or a boundary region of a stripe and noise, and a noise component is removed by extracting only a component whose level is equal to or higher than a predetermined value as described above. However, only the defect and the boundary area can be set. Next, the defect detection means detects, as a defect, a portion which is not the boundary area identified by the boundary area identification means among the areas extracted by the image enhancement means. As described above, only the defect and the boundary area are extracted by the image enhancement means, and the result of the boundary area identification means is only the boundary area. For example, if the difference between the two is obtained, only the defective part can be detected. Can be done. Alternatively, AND of the values obtained by binarizing the result of the image enhancement means and the result of the boundary area identification means may be obtained.

As described above, the invention according to claim 1 is
This is to detect both a defect appearing black in a bright part and a defect appearing white in a dark part of a bright and dark stripe image. However,
Since it is impossible to detect defects at the boundary between light and dark, to inspect the entire surface to be inspected, the object to be inspected or the illuminating means and the imaging means are sequentially moved so that the bright part and the dark part (excluding the boundary area) ) Scans the entire surface to be inspected. However, since the area where the defect inspection can be performed in one screen is wider than that in the conventional example in which only the bright portion is inspected, the inspection can be performed quickly.

Next, in the second aspect of the invention, the boundary area processing means performs processing for enlarging the boundary area obtained by the boundary area identification means. This is because, as described later in detail, the reason why the “yuzu skin” is erroneously detected as a defect is that the boundary between the bright and dark stripes is disturbed by the “yuzu skin” and an isolated point is generated. By making the area where detection may occur collectively a boundary area,
No defect detection is performed for such a portion. Next, claims 3 to 5 show configuration examples of the boundary area processing means.
Enlarging the boundary area by a predetermined value determined in advance according to the roughness of the surface to be inspected, wherein the value according to the roughness of the surface to be inspected obtained from the image data obtained by the imaging means Only the boundary area is enlarged. Claim 5
Once the boundary area is enlarged, processing for reducing the boundary area is performed. As described above, once the boundary area is enlarged, the boundary line is disturbed by the influence of "yuzu skin",
After a region where an isolated point is generated is taken into a boundary region (for example, a dark portion), even if image processing for reducing the boundary region is performed, the disturbance and the isolated point are not reproduced. Therefore, by performing the above-described enlargement processing and reduction processing, the influence of “yuzu skin” can be eliminated, and the stripe portion can be returned to the initial state.

Next, claims 6 and 7 show examples of the configuration of the image enhancing means, and claims 8 and 9 show examples of the configuration of the boundary area identifying means. . Claims 10 and 11 show configuration examples of the defect detection means. Next, claim 1
In the invention described in item 2, a predetermined light / dark pattern is projected on the surface to be inspected by the illuminating means, and the light / dark pattern is converted into image data of an electric signal by capturing the image with the imaging means. Next, the smoothing means smoothes the image data obtained by the imaging means. This smoothing removes minute luminance change components due to noise or the like. Next, the emphasizing means extracts a portion having a luminance change from the image data of the light and dark pattern smoothed by the smoothing means. This emphasis processing is, for example, differentiation processing. Next, the binarization means binarizes the result of the emphasis processing. By this binarization process, a region having a change in luminance, such as a defective portion or a stripe boundary portion, is extracted. Next, the area determination processing means obtains an area for each of the portions having a change in luminance existing in one screen by calculating each of the areas obtained by the area processing means. The existing portion is detected as a defect. That is, as a portion having a change in luminance, a light-dark boundary portion of a stripe and a defective portion are detected. Of these, the area of the defective portion is significantly smaller than the area of the light-dark boundary portion of the stripe. By discriminating by area, only defective portions can be extracted. The twelfth aspect of the invention detects both a defect appearing black in a bright portion and a defect appearing white in a dark portion of a bright and dark stripe image. However, since a defect cannot be detected in the boundary area between light and dark, in order to inspect the entire surface to be inspected, the object to be inspected or the illuminating means and the image capturing means are sequentially moved in the same manner as in claim 1;
The light portion and the dark portion (portions other than the boundary region) are configured to scan the entire inspection surface.

In the invention according to the thirteenth aspect, the area of the value portion corresponding to the portion where the luminance changes in the binary signal obtained by the binarization means is expanded by the area processing means. Perform processing. This is because, as described later in detail, the reason why the “yuzu skin” is erroneously detected as a defect is that the boundary between the bright and dark stripes is disturbed by the “yuzu skin” and an isolated point is generated. The defect detection is not performed for the portion where the detection is likely to occur. Claims 14 to 16
Shows an example of the area processing means, and is the same as in the third to fifth aspects. The seventeenth to nineteenth aspects relate to a means for eliminating an error in area determination when the surface to be inspected is a curved surface in the twelfth aspect. 18. The method according to claim 18, further comprising:
The stripe boundary portions are all connected by image processing of the pixels. Claim 19 is configured so that each stripe is connected in advance in the stripe image of the illumination device. Claims 20 to 23 show configuration examples of the lighting means. Claim 24 shows the configuration of the post-processing means after the defect detection. Further, claim 25 shows a configuration for simultaneously inspecting a wide inspection surface of an object to be inspected, for example, a configuration for performing a coating surface inspection in a coating process of an automobile body.

[0021]

DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will be described below with reference to the drawings.
FIG. 2 is a diagram showing one embodiment of the present invention. In FIG. 2, reference numeral 1 denotes an illumination device, which is arranged so as to irradiate the surface 3 to be inspected with light having a predetermined light and dark pattern. Reference numeral 2 denotes a video camera (for example, a CCD camera or the like), which is arranged to capture an image of the surface 3 to be inspected on which a light-dark pattern is projected. Reference numeral 4 denotes a camera control unit, which generates an image signal of a received image picked up by the video camera 2 and outputs the image signal to the image processing device 5. Reference numeral 6 denotes a host computer, which has a function of controlling the image processing apparatus 5 and displaying or outputting processing results to the outside. Reference numeral 7 denotes a monitor which displays a screen captured by the video camera 2 and the like.

FIG. 3 is a block diagram showing the configuration of the image processing apparatus 5. In FIG. 3, an image signal from the camera control unit 4 is converted into a digital value by an A / D converter 9 via a buffer amplifier 8. Further, an MPU (microprocessor) 10 performs a predetermined operation, processing, and the like on the image data. Reference numeral 11 denotes a memory for storing image data and processing results.
It can be output to the monitor 7 via the / A converter 12 and displayed.

Next, FIG. 4 is an exploded perspective view showing the details of the lighting device 1. In the present embodiment, description will be made assuming that illumination of a light and dark stripe pattern is used. In FIG. 4, reference numeral 1a denotes a light source, the light of which is diffused by a diffusion plate 1b and radiated to a surface to be inspected through a stripe plate 1c. The diffusion plate 1b is, for example, a piece of frosted glass, and uniformly irradiates the inspection surface 3 with light. Stripe plate 1
c is a transparent or diffusing plate with black stripes at predetermined intervals.

FIG. 5 is a perspective view showing an example of an illumination device when the diffusion plate 1b is not used. In FIG. 5, if the light source 1a is a light source that generates light diffused in advance, such as a general fluorescent lamp, the same stripe light as described above can be obtained by making the background 1d black. Therefore, by configuring the illumination device in this manner, a bright and dark (black and white) stripe pattern can be projected on the surface 3 to be inspected.

Next, the principle of using a stripe illumination as described above to project a defect on the surface of an object to be inspected in a received light image will be described with reference to FIGS. In FIG. 6, it is assumed that there is a convex defect 13 on the surface 3 to be inspected.
Point P1 is an arbitrary point on a normal surface without defects, and point P2 is an arbitrary point on defect 13. When the defect 13 is convex as shown in the figure, each of the arbitrary points on the defect 13 has an angle between a tangent line of the point and a normal surface to be inspected. I do. In this case, the vicinity of the defect 13 and the point P1 is within the bright stripe of the stripe illumination (the part irradiated with light).
It is assumed that When an angle between the video camera 2 and the surface 3 to be inspected is an incident angle θi, a point P which is a normal plane
At 1, the light is specularly reflected (θi), and there is a bright portion of the stripe illumination in that direction, so that the point P 1 is projected by the video camera 2 as a bright (white) portion. However, at the point P2 on the defect 13, the incident light from the video camera 2 is not specularly reflected but irregularly reflected due to the inclination angle θ, and there is a black stripe in that direction, so that the point P2 is projected black by the video camera 2. Assuming that the irregular reflection angle at this time is θh, the defect point P
2, the angle (incident angle) in a certain direction of the video camera 2 is θi, so that θh = θi + 2θ (Equation 1) (see FIG. 7). That is, in the direction of the angle θh, a bright (white) portion of the stripe is projected white, and a black portion of the stripe is projected black (dark). Also,
As described above, since the light is specularly reflected on the normal plane, it can be expressed as θh = θi (Equation 2). Therefore, from the formulas (1) and (2), the irregular reflection due to the defect can be expressed as 2θ based on the regular reflection direction θi. In the above description, the video camera 2 is used as a starting point, but the same applies to the case where the lighting device 1 is used as a starting point. FIG. 7 shows an example in which the point P2 is on the right slope of the defect. However, the same applies to the case where P2 is on the left slope, and a description thereof will be omitted.

FIG. 8 shows a case where the surface to be inspected is a curved surface.
This will be described with reference to FIG. As shown in FIG. 8, the surface 3 to be inspected is a curved surface having a radius of curvature R, and the center thereof is C. An arbitrary point on this curved surface is defined as P1 ', and the central angle of the point is θc
And Here, the reflection angle θh at the point P1 ′ on the curved surface is represented by θh = θi + 2θc (Equation 3), and this is the regular reflection angle on the curved surface. Further, when there is a defect at the point P1 ', the reflection angle θh is given by θh = θi + 2θc + 2θ (Equation 4), as in the case of the plane. Therefore, the irregular reflection direction at the defect is 2θ regardless of the plane or the curved surface, based on the regular reflection direction at that point.
Becomes

As described above, the reflection angle on the surface to be inspected is obtained from the inclination angle θ at the defect. Therefore, the distribution of the inclination angle θ at the defect is measured in advance, and it is desired to detect the distribution.
In other words, if the area to be projected in black in the image (to match the minimum rank of the size to be determined as a defect according to the type of the inspection object) is determined, the dark (black) of the stripe in the reflection direction in that area is determined. 4), the distance between the stripes (T in FIG. 4) and the distance between the illumination device 1 and the inspection surface 3 (D in FIG. 6; also includes the angle between the illumination device and the inspection surface). At this time, a representative defect is sampled according to the type of the inspection object, and the defect is set so as to match the sample. In addition,
Contrary to the above description, the above principle can be used to project a defect white in a stripe that appears black on the surface to be inspected. However, in this case, since the light from the lighting device 1 is diffused light, the defect may be bright and large and difficult to be imaged (dark gray instead of white, and the area of the bright image is small). In this case, in order to make the defect appear brighter (whiter), the width of the light portion of the stripe is made wider than that of the dark portion (for example, the light / dark ratio is set to light: dark =
2: 1 or more), the light-dark interval T and the distance D of the stripes may be determined.

Next, the operation will be described. This embodiment corresponds to the block diagram of FIG. Assuming that light and dark stripes are projected on the surface 3 to be inspected, and that there are defects 13 and 13 'in the light and dark portions of the stripe, the received light image is as shown in FIG. According to the above principle, the defect 13 is black in the bright stripe and the defect 1
3 'appears white in the dark stripe. FIG. 9 (a)
In the received light image of the above, the coordinate axes x,
If y is taken, the luminance levels in the x direction at the defects 13 and 13 'are as shown in FIG. 9B. In FIG. 9B, large irregularities correspond to the bright and dark portions of the stripe, and fine irregularities are noise. The defect 13 is shown as a concave portion which is larger than the noise and falls from the convex portion (bright portion), and the defect 13 'is shown as a convex portion which protrudes from the concave portion (dark portion). The image processing device 5 described above
Captures the received light image as shown in FIG. 9 as the signal S0 of the original image. For example, when the image signal from the video camera 2 is A / D converted and the luminance level is converted to an 8-bit digital value, as shown in FIG. 0 becomes black and 1 screen becomes 51
When the image is captured at a resolution of 2 × 512 pixels, the horizontal axis indicates the number of pixels of 0 to 512. The signal S0 shown is y
This is a signal for a certain value in the axial direction (the value indicated by *), and such a signal exists for each value on the y-axis.

Next, FIG. 10 is a flowchart showing the processing contents in the image processing apparatus 5. Image processing device 5
In FIG. 10, as shown in FIG. 10, a defect is detected by performing an area recognition process P2, an image enhancement process P3, and a defect detection process P4 from the original image P1. Further, as post-processing P5 after detecting a defect as described above, there are labeling, area and barycentric coordinate calculation, and the results are displayed on a monitor or the like or output to a subsequent device.

Hereinafter, the contents of each of the above processes will be described in detail. First, the boundary region recognition processing P2 will be described. FIG. 11 is a waveform diagram of each component included in the signal S0 of the original image. As shown in FIG. 11, the signal S0 of the original image includes a shading component (low frequency component) due to uneven illuminance and the like.
It is divided into three components: a luminance change component (intermediate component) due to stripe illumination, and a luminance change component (high frequency component) due to defects 13, 13 'and noise. In order to recognize the boundary region of the stripe from such an original image, an area that is not defective among the luminance change components (intermediate components) is separated / extracted from the original image. The above frequency is a spatial frequency in image processing. In order to easily and reliably recognize the intermediate component, it is sufficient that the interval and width of the stripe pattern reflected on the surface to be inspected are sufficiently large for the defect. Taking FIG. 11 as an example, the cycle of the low-frequency component such as shading is 512 pixels or more in the x direction of one screen. An intermediate component such as a stripe pattern has a size of about 50 to 60 pixels per cycle because 4 to 5 cycles per screen (a total of 9 bright and dark stripes). Here, assuming that the defect has a size of about 0.5 to 1 mm and the field of view of the camera is 150 × 140 mm, the defect appears in a received image with a size of about 3 pixels. As described above, as the difference between the sizes (the number of pixels) of the three components is larger, the intermediate component can be more easily recognized. Therefore, the interval of the stripe illumination,
What is necessary is just to set the width and the ratio of light and dark.

Hereinafter, a specific example of the boundary area recognition processing P2 in FIG. 10 will be described. In the boundary region recognition processing, as shown in FIG. 10, each processing of smoothing P21, differentiation P22, and absolute value P23 is performed. First, the original image is smoothed (low-pass filter) to remove only high-frequency components due to defects and noise. This is because the intermediate component and the high-frequency component are separated to extract only the intermediate component in order to recognize a portion (a boundary region) having a luminance change other than a defect from all the regions having a luminance change. This is because it is necessary to take out only the defect component in order to emphasize only the luminance change due to the defect in the image enhancement process.

Hereinafter, a case where a smoothing filter is used for the above-described smoothing will be described. For example, as shown in FIG. 12, when performing a local operation of a 3 × 3 mask on each pixel of 512 × 512 pixels on one screen, the smoothing filter includes a pixel of interest Q0 (out of nine pixels arranged in a square shape). , And a simple average value of the luminance values of the surrounding pixels Q1 to Q8 is set as a new luminance value of the pixel of interest with respect to the pixels Q1 to Q8 surrounding the surrounding pixels. Since the simple average value of the eight peripheral pixels is thus obtained, minute noise and the like are removed. The size of the mask is arbitrary, such as a 5 × 5 mask or a 7 × 7 mask, and this is called a mask size. The number of times of processing of the smoothing filter and the mask size may be set according to the interval between stripes and the size of the defects 13 and 13 '. As a result of such a smoothing filter process, the signal S0 of the original image becomes the signal S1 shown in FIG.
Only the defect (high-frequency) component is removed, and an image is formed of a luminance change component (intermediate component) due to something other than the defect and a low-frequency component due to shading or the like. Next, the obtained signal S1 is differentiated. As a result, a low-frequency component whose luminance changes gradually due to shading or the like is removed, and only an intermediate component whose luminance changes rapidly is emphasized. However, in the general differentiation processing, since the rising edge and the falling edge of the luminance change appear positive or negative with respect to the luminance of 0, the absolute value of the result of the above differentiation is taken, and the processing is performed so that all the luminance changes are positive. Do. The result is as shown in signal S2.
In this manner, it is possible to separate / extract, that is, recognize a luminance change component due to a factor other than a defect, that is, a boundary region between light and dark stripes, from the original image.

Next, a specific example of the image enhancement process P3 in FIG. 10 will be described. In the image enhancement processing, as shown in FIG. 10, each processing of subtraction P31 and absolute value P32 is performed. In the present embodiment, a method using the signal S0 of the original image in FIG. 13 and the signal S1 from which only the defect component has been removed by smoothing will be described. This is a process of subtracting the signal S1 from the signal S0, thereby canceling the low-frequency component and the stripe component, and extracting only the high-frequency component due to a defect, a boundary between light and dark, and noise as in the signal S3. However, depending on the method of subtraction,
One of the defect 13 appearing black in the bright stripe and the defect 13 ′ appearing white in the dark stripe has a negative value.
Therefore, the absolute value of the subtraction result is obtained, and all the enhancement results are set to be positive values. In addition, in the image enhancement result S3 in FIG. 13, there may be a case where a portion (intermediate component) corresponding to the boundary region remains in addition to the defect, which causes erroneous detection. Therefore, the following defect detection processing is performed.

Next, the defect detection processing P4 in FIG. 10 will be described. In the defect detection processing, as shown in FIG. 10, each processing of subtraction P41 and binarization P42 is performed. First, FIG.
Is subtracted from the boundary area recognition result S2 from the image enhancement result S3, and the result is S4. This is because the above-described subtraction lowers the level of the luminance change region (intermediate component due to the boundary region) other than the defect and widens the setting range of the threshold value for binarization later. Note that the intermediate component area has a negative value as a result of the subtraction. If the intermediate component area has a negative value, the value is set to 0 or, as shown in S3 of FIG. A bias B may be added. Next, as shown in S4 of FIG. 13, binarization is performed with a predetermined threshold value (Th or bias B + Th), thereby obtaining a binarized signal as in S5. In this way, it is possible to mask the luminance change area (boundary area) other than the defect that is likely to cause erroneous detection, and to reliably detect only the defect without being affected by shading or the like.

Next, as post-processing after the completion of the defect detection as described above, labeling (labeling) and area of the defective portion are performed based on the defect detection signal S5 as shown at P5 in FIG. Calculation and barycentric coordinate calculation are performed. Then, the host computer 6 specifies the defect, positions and ranks the defect based on the result, and outputs the result to an output device such as a display device or a printer. Or, when there is a defect, control of the line such that the inspection object is switched from a normal line to a line dedicated to repair (for example, in the case of a painting process of an automobile body, only the defective portion is repair-painted), or a rank of the defect. (Size) and statistical processing for the location of occurrence may be used. Further, in the above description, the illumination device 1 that projects a stripe-like light-dark pattern has been described. However, as shown in FIG. 14, the light-dark pattern may be formed in a lattice shape. Specifically, it can be realized by forming the stripe plate 1c in FIG. 4 into a lattice pattern as shown in FIG. The operation and effect when such a lattice pattern is used are the same as those in the case of the stripe described above. Further, the intervals and ratios of the lattice pattern may be set in the same manner as described above.

Next, another embodiment of the image enhancing process P3 in FIG. 10 will be described. FIG. 15 is a flowchart of another embodiment of the image enhancement process P3. In this embodiment, each processing of the smoothing P33 with the normal function, the second derivative P34 and the absolute value P35 is performed. When only the second derivative is directly applied to the original image, electrical noise and noise such as a minute luminance change component are excessively emphasized. Therefore, in the present embodiment, the signal of the original image is first smoothed by a normal function (Gaussian function), and then the second derivative is applied. Note that the point at which the absolute value is finally obtained is the same as described above. In the x direction of the original image, a Gaussian function having an average of 0 and a variance of σ is represented by the following equation (5). The function form is shown in FIG.

[0037]

(Equation 5)

When the above equation (5) is secondarily differentiated, the following equation (6) is obtained. The function form is shown in FIG.

[0039]

(Equation 6)

In equation (6), the parameter σ
Since the emphasis ability can be adjusted by setting, σ may be set so as not to react to a noise component smaller than the defect or a stripe component larger than the defect and to emphasize only the defect. Actually, since the image is a digital signal having discrete values, the function of equation (6) shown in FIG. 17 also becomes as shown in FIG. FIG. 18 shows an example in which the filter is applied as a filter having a mask size of 9 pixels, and the coefficients a _{1 to} a ₅ may be adjusted according to the size of a defect to be emphasized. In the image emphasizing process of this embodiment, it is possible to emphasize only the defect ignoring minute points smaller than the defect, including a smoothing effect, and to perform these operations / processes at high speed. There are such effects.

In the conventional method of detecting or enhancing a defect simply by using a differentiation process as in the image enhancement process, a boundary between a bright portion and a dark portion of a bright and dark stripe pattern, a region other than the surface to be inspected, and a boundary between the regions. Or, since there is a luminance change even in places such as irregularities and processing parts provided as a design on the surface to be inspected, if such a place that is not a defect but has a luminance change exists in the received image, Although there is a problem in that a portion that is not a defect as described above is also emphasized, the above-described problem can be solved in the present embodiment as described above.

Next, a process for removing the influence of the paint color on the surface to be inspected will be described. As shown in FIG.
Since the luminance level of the entire signal and the contrast of the stripe are different between the high (bright) color and the low (dark) color of the color of the surface to be inspected, the result of the boundary region recognition processing P2 masks the intermediate component. May not be enough. That is, when the boundary area recognition processing result S2 is subtracted from the image enhancement processing S3 in FIG. 13, the level of the intermediate component other than the defect does not drop below the bias level B,
There are cases where the setting range of the binarization threshold is not expanded, and cases where the width of the intermediate component S2 changes due to the roughness of the surface to be inspected.

FIG. 21 is a flowchart of a process for addressing the above problem. FIG. 21 shows a part of the boundary area recognition processing P2 in FIG. In this embodiment, two processes of a luminance level judgment P24 and a smoothing (coefficient adjustment) P25 are added to the boundary region recognition process P2 in FIG. When a luminance histogram of the original image is taken, as shown in FIG. 20, the frequency of a pixel having a high luminance value is high when the surface to be inspected is a bright color, and the frequency of a pixel having a low luminance value is low when the surface to be inspected is a dark color. The frequency increases. Therefore, the luminance level can be detected / determined based on the color of the surface to be inspected. FIG. 22 is a signal waveform diagram in the present embodiment. In the case of a bright color, the contrast is large, so the intermediate component S2
Is large, but in the case of a dark color, the level L1 is small. When the absolute value S2 of the differentiation is smoothed again to increase the level L1, the result becomes S6, and the level L1 and the width W1 of the intermediate component change to L2 and W2. Assuming that this smoothing is a simple averaging filter, the level of S6 can be greatly changed like the level L3 of S7 by changing the coefficient or divisor of the filter. If the image enhancement result S3 is masked (subtracted) using S7, the setting range of the binarization threshold can be expanded without being affected by the color of the surface to be inspected. FIG.
In the flowchart of FIG. 1, at P24, the original image P1
The luminance level is determined by using the luminance histogram shown in FIG. 3, and the result is used to adjust the coefficient in the smoothing process of P25. The roughness of the surface to be inspected is measured in the same manner as described above, and a predetermined smoothing process is performed according to the degree of the roughness, so that the width of the intermediate level (FIG. 22: W2 in S6) can be set. Further, changing only the level of the intermediate component S2 can also be realized by changing the differential coefficient in the differential processing P22.

Next, the influence of the difference in the contrast of the stripe depending on the color of the surface to be inspected remains even when the above processing is used, or the high frequency component that does not become a defect because the surface to be inspected is considerably large. In the case shown in FIG.
By changing the threshold value Th for binarization in the defect detection process P4 of 0 based on the color or roughness of the surface to be inspected, only the defect can be reliably detected. In the same manner as described above, the configuration detects the color or roughness of the surface to be inspected, and if the contrast is high or the roughness of the stripe is large in a dark color, the threshold value for binarization in the defect detection process P4 is accordingly determined. The threshold value Th is set such that only the defect is reliably detected, such as increasing the value Th.

Next, a practical embodiment of the present invention will be described. FIG. 23 is a perspective view of an embodiment in which the present invention is applied to a coating inspection process of an automobile manufacturing line. As shown in FIG. 23, the painted automobile body 14 flows on the paint inspection line in the direction of the arrow. A plurality of lighting devices 1 and video cameras 2 are arranged in an arched manner so that a predetermined light and dark pattern is projected on a part of the painted surface of the body 14 and an image of the part where the light and dark pattern is projected is taken. Have been. Accordingly, while the body 14 moves on the paint inspection line and passes under the video camera arranged in an arch shape and under the stripe illumination, the defect detection of the inspection surface is performed. With such a configuration, the body 1
The painted surface of No. 4 can be thoroughly inspected.

As described above, since the illumination device 1 and the video camera 2 are fixed at predetermined positions, if the relative position with respect to the body 14 to be inspected and the line speed of the coating line are known, the host The computer 6 can specify a defect position on the inspection object. Therefore, for example, an external view of the inspected object is displayed to add a marker to the defect position, or the defect position is output as a numerical value such as coordinates, or a removable marker is directly added to the defect position on the inspected surface. The host computer 6 may be configured to notify the operator of the inspection result by any output method such as controlling an appropriate device. Alternatively, when there is a defect, line control such as switching the inspected object from a normal line to a line dedicated to correction, or a statistical process relating to the rank (size) of the defect and the place of occurrence may be performed. With such a configuration, the painted surface of the body can be inspected throughout, and there is no need to scan the video camera and the illumination along the surface to be inspected using an expensive robot device or the like. The whole body can be inspected.

Next, another practical embodiment of the present invention will be described. FIG. 24 shows a coating line of an automobile.
The car body 14 moves in the direction of the arrow on the painting line
This is an example in which the present invention is applied to an apparatus in which the painted surface 3 of the image No. 1 is imaged by the video camera 2 and inspected continuously. As described above, the present invention cannot inspect the boundary region (masked region) of the stripe. Therefore, if the images are sequentially shifted by the width of one screen, the above boundary area remains without being inspected. Therefore, as shown in FIG. 24, the defect detection is performed without fail by overlapping the respective light-receiving image ranges that are sequentially captured by the video camera 2. This will be described in more detail with reference to FIG. In FIG.
Assuming that an arbitrary n-th received light image is obtained from the video camera 2 as shown in the figure, if the (n + 1) -th received light image is an image shifted by △ (△ g) of the width of one stripe, n In the (n + 1) -th received light image, the boundary region of the masked stripe in the second received light image falls within the bright or dark stripe, so that no detection omission occurs. further,
If the process of taking in the (n + 2) th received light image by shifting by one screen (G) and taking in the (n + 3) th time again by shifting by Δg as in the previous time is repeatedly performed, defect detection is performed efficiently and continuously. I can do it.

As another method for preventing the above-mentioned detection leakage, an illuminating device having unequally spaced stripes as shown in FIG. 26 may be used. In other words,
An interval between mask regions corresponding to the boundary region of the stripe;
It is highly likely that a defect occurs when the timing of appearance of the defect coincides with the timing of appearance on the light receiving screen. Therefore, if the detection timing is substantially constant, as shown in FIG. 26, if the intervals or widths of the stripes are not constant, it is possible to eliminate the occurrence of missed detection.

Next, there is a possibility that erroneous detection may occur in the case where the above-mentioned "yuzu skin" is present. Hereinafter, the boundary region processing for solving the problem will be described. FIG.
It is a flowchart which shows the process at the time of performing a boundary area process as a measure against "yuzu skin". In FIG. 27, FIG.
The point that the boundary area processing P6 is added to the flowchart of FIG.
33, and the other parts are the same. If the surface to be inspected has thin irregularities such as "yuzu skin",
Depending on the degree of the roughness, the mask processing may remain without being completely masked, and a portion other than a defect may be erroneously detected. For example, as shown in FIG. 28, the boundary between the light and dark portions of the stripe image is disturbed according to the surface roughness, and an isolated point appears as shown in FIG. I do. This part of the range α causes the erroneous detection. Since this increases in proportion to the surface roughness, the larger the range α is, the more the mask remains without being masked, and erroneous detection is likely to occur. In order to solve the above problem, the present embodiment is configured as follows. First, in the boundary region recognition processing, the result of performing the same processing of smoothing P21 and differentiation P22 as described above is binarized at P24. In the image enhancement process P3, the result of the subtraction P31 is binarized in P33.
At this time, for example, in P24, the boundary area is “0” (black),
In P33, binarization is performed so that a portion that is not a boundary region is “1” (white), and a portion with luminance change is “1” (white) and a portion that is not changed is “0” (black) in P33. I do. next,
In the boundary area processing P6, the boundary area (for example, “0”) is enlarged (as will be described later) in the result of P24. next,
In the defect detection processing P4, an AND of the boundary area processing result and the image enhancement processing result is obtained in P41. In the above example, a portion that is not a boundary region is “1” in the boundary region processing result, and a portion with a change in luminance (a defect or a boundary region portion) is “1” in the image enhancement processing result. Is obtained, only the defective portion can be detected. Further, as described above, the mask area is enlarged by performing the enlargement processing of the boundary area, and the part of the range α in FIG. 28 is also masked. As a result, there is no danger of erroneously detecting “yuzu skin” as a defect. Basically, a configuration excluding the above-described boundary region processing P6, that is, a configuration in which an AND of the boundary region recognition processing result of P2 and the image enhancement processing result of P3 may be obtained.

Next, a specific method of the above boundary area processing will be described. FIG. 29 is a diagram for explaining the principle of the boundary area processing. The process of enlarging the area of the dark part is described in FIG.
9A, Q1 to Q8 surrounding the target pixel Q0.
When there is at least one graphic pixel (dark part of the graphic) among the pixels of the target pixel, the target pixel Q0 is set to the same value as the graphic pixel. As a result, as shown in FIG. 29B, the connected components of the graphic image in the image are enlarged outward by one pixel, and the black portion is enlarged to the hatched portion. In addition,
The area reduction processing is the reverse of the above-described enlargement processing. If there is at least one background pixel (light part of the figure) among the pixels Q1 to Q8 surrounding the target pixel Q0, the target pixel Q
This is a process of setting 0 to the same value as the background pixel. As a result, the dark part of the figure is reduced and the light part is enlarged. In the above-described boundary region processing in the pixel figure, the processing amount is proportional to the number of times of processing since one peripheral pixel is enlarged / reduced by one processing.

As a method of setting the number of times of processing in the actual boundary region processing (that is, the amount of enlarging the dark part),
For example, there are the following methods (1) and (2). (1) If the surface to be inspected has a value within a substantially determined range, such as a painted surface of an automobile, an erroneously detected portion due to roughness (range α) is adjusted to the maximum value of the roughness range. The number of times required for completely masking (filling) up to this point may be obtained in advance by experiments or the like, and set to that value. This makes it possible to easily set the amount by which the dark portion is enlarged during the actual inspection. (2) measuring the surface roughness of the surface to be inspected for each input image;
The optimum number of enlargement processes may be automatically obtained from the result. By doing so, it is possible to enlarge the dark part by an optimal amount according to each test object,
Precise inspection can be performed. Methods for measuring the surface roughness include (a) a method using an ordinary surface roughness measuring instrument, and (b) a method for obtaining the amount of disturbance of a stripe of an input image from the spatial frequency of the image.

Hereinafter, the method (b) will be briefly described. When a stripe pattern is projected on the surface to be inspected and captured with a video camera, the stripe pattern is disturbed according to the surface irregularities as shown in FIG. When this signal is subjected to FFT (Fast Fourier Transform) processing to obtain a power spectrum, the result is as shown in FIG. In the characteristic curve of FIG. 30B, the large convex region at the left end is a fundamental wave corresponding to the lightness and darkness of the stripe, and the long wavelength region to the right is a portion corresponding to unevenness having a relatively large area such as “yuzu skin”. The middle wavelength region and the short wavelength region to the right are portions corresponding to unevenness having a smaller area. Therefore, if the surface roughness due to “yuzu skin” is large, the area of the long wavelength region (the area of the shaded area) becomes large.
From this value, the degree of “yuzu skin” can be measured.

The area of the erroneously detected portion is sufficiently smaller than the area of the original defective portion and is constant (the roughness is measured in advance,
If it can be determined that it is almost constant), after performing the enlargement processing of the dark part, the contraction processing of the dark part to return only the original defect to the original size (area) before processing (Bright portion enlargement processing) may be performed. That is, as described above, the area of the dark part is once enlarged,
After the area where the boundary line is disturbed by the influence of “Yuzu skin” or an area where an isolated point is generated is taken into the dark area, even if image processing for reducing the dark area is performed, Isolated points are not reproduced. Therefore, by performing the above-described enlargement processing and reduction processing, the influence of “yuzu skin” can be eliminated, and the stripe portion can be returned to the initial state.

Next, another embodiment of the present invention will be described. This embodiment corresponds to the block diagram of FIG. In this embodiment, parts such as the lighting device 1, the video camera 2, the camera control unit 4, the image processing device 5, and the host computer 6 are the same as those shown in FIG.
This embodiment is the same as the first embodiment, and only the processing contents are different.

The operation will be described below. Assuming that light and dark stripes are projected on the surface 3 to be inspected and there are defects in the light and dark portions, the received light image is shown in FIG.
As shown in (a), the defect appears black in the bright stripe and white in the dark stripe. In the received light image of FIG. 31A, when the coordinate axes x and y are set with the origin at the upper left of the screen, the luminance level in the x direction at the y coordinate (the value of the mark *) of the defective portion is as shown in FIG. 31B. become. The image processing apparatus 5 captures the received light image of FIG. 31A as an original image signal S20 as shown in FIG. 31B. For example, when an image signal is A / D converted and the luminance level is converted to an 8-bit digital value, 255 is the maximum value on the vertical axis, and white is 0, and 0 is black, and one screen is captured with a resolution of 512 × 512 pixels. In this case, the horizontal axis indicates the number of pixels from 0 to 511.

Next, FIG. 32 is a flowchart showing the processing contents in the image processing apparatus 5. Image processing device 5
In FIG. 32, as shown in FIG. 32, basically, a defect is detected by performing a smoothing process P11, an emphasizing process P12, a binarizing process P13, and an area determining process P14 from the original image P1. As will be described later, a region process P15 may be added as a measure against “yuzu skin”. As post-processing after detecting a defect as described above, there is labeling, area and barycentric coordinate calculation P16, and the results are displayed on a monitor or the like at P17 or output to a subsequent device.

Hereinafter, the contents of each of the above processes will be described in detail. FIG. 33 is a diagram showing each image and its signal (at a certain y-coordinate, in this example, at the value of the mark * in FIG. 31). Hereinafter, the content of the image processing will be described with reference to FIG. First, a smoothing process of a predetermined mask size is performed on the original image S20 to remove only a small luminance change component due to noise or the like, thereby obtaining S21. This smoothing processing is, for example, a simple averaging filter that obtains an average value of luminance values of a target pixel and pixels in the vicinity thereof and uses the average value as a new luminance value of the target pixel. At this time, the mask size of the smoothing filter may be determined according to the magnitude of noise generated in the original image. Next, S21
Is emphasized, thereby emphasizing an area having a luminance change.
This emphasis process is, for example, a differentiation process, which is performed by taking the sum of the absolute values of the differentiation results in the x and y directions or the square root of the sum of the squares, and outputs all the brightness change areas to the positive (+) side. Then, it becomes as shown in S22. In addition, the image of S22 is shown by inverting white and black. When the result of S22 is binarized with a predetermined threshold value so that the brightness change area becomes black (“0”) and the other areas become white (“255”), S23
become that way. As shown in the image of S23, a region having a luminance change such as a defective portion and a stripe boundary portion is extracted. Note that, depending on the state of the surface to be inspected, the boundary of the stripe is extracted with distortion, but is extracted as one continuous region by smoothing the original image as described above. Almost no. However, near the boundary of the stripe, there is a possibility that noise due to the influence of “yuzu skin” on the surface to be inspected may remain. Therefore, region processing is performed to remove the noise. That is, S23
By performing a process of enlarging a dark portion (corresponding to a portion having a change in luminance) in the image of (1), the stripe boundary region and the noise due to “yuzu skin” are connected to form one region.

Next, the area of each part is determined based on the above result. As is clear from the image of S23, the area (size) of the defective portion is much smaller than the area of the stripe boundary portion. Only one can be extracted. The result of the area determination is as shown in S24, and it is possible to detect both a defect appearing black in the bright part and a defect appearing white in the dark part of the bright and dark stripe image. However, since a defect cannot be detected at a boundary portion between light and dark (a portion having a change in luminance), the above-described FIG.
As in the embodiment of (a), in order to inspect the entire surface to be inspected, the object to be inspected or the illuminating means and the imaging means are sequentially moved so that the bright portion scans the entire surface to be inspected (for example, 23 and 24).

Next, a special process when the surface to be inspected is a curved surface will be described. In the above embodiment, the following problem occurs because the defect is detected by the area determination. That is, as shown in FIG. 34, when the surface to be inspected is a curved surface, the stripe is deformed along the curved surface. For this reason, the stripe boundary line may be interrupted at the corner of the screen to reduce the area (a part surrounded by a circle) in some cases. In such a case, this part may be erroneously detected as a defect in the area determination. In order to solve such a problem, the stripe boundary line must be a window (screen 4).
If the detection area is in contact with the window, the process of excluding the area from the processing target may be performed. Specifically, when one screen has 512 × 480 pixels, the maximum and minimum points of the x and y coordinates of the detection area are 0, 479 or 511.
If so, it is determined that the area is in contact with the window, and that part may be removed.

As another method for solving the above problem, there is the following method. That is, as shown in FIG. 35, by setting the periphery of the screen to the same luminance value (black in FIG. 35) as that of the detection area for one pixel (the hatched portion in FIG. 35).
Since all the stripe boundary lines are connected to form one region, the area is increased, and the area determination is performed more reliably.

Further, as another method for solving the above problem, there is the following method. In the stripe plate 1c of the illuminating device 1, a line similar to the above, which crosses this line, is inserted diagonally to the stripe so as to be black in the white stripe and white in the black stripe. Put another one in advance. FIG. 36A shows a stripe image when such a lighting device is used. When such an original image is processed, the result is as shown in FIG. 36 (b).
Since there are three regions, the area is increased, and the area determination is performed more reliably. However, as shown in FIG. 23 and FIG. 24, when the inspection object is sequentially moved and a large area is inspected, if the moving direction of the inspection object coincides with the direction of the oblique line, the line If there is a defect at the boundary portion of, the defect will not be detected. Therefore, it is necessary to set so that the above-mentioned oblique line does not coincide with the moving direction. In the embodiment corresponding to FIG. 1B, the configuration of the illuminating means described in the embodiment corresponding to FIG. The practical application device and the like can be applied in the same manner as described above.

Next, a practical method of setting stripe illumination in the illumination device 1, that is, a method of setting the interval T between stripes, the ratio of black and white, and the distance D from the inspection surface will be described. First, the inspection range L of the video camera 2 will be described with reference to FIG.

In FIG. 37, the distance from the video camera 2 composed of a lens and a light receiving element (for example, a CCD) to the inspection surface 3 is a, the inspection range (camera field of view) on the inspection surface 3 is L, and the viewing angle is Assuming that θg, the focal length is b, and the element size is c, the relationship L: a = c: b holds. Therefore, if the lens of the video camera to be used and the detection range are determined, the distance a is also determined. For example, when used in a paint inspection process of an automobile, considering the inspection efficiency and space, L = 100 to 200 mm, a
= Approximately 50 to 100 cm. Further, as shown in FIG. 38, the video camera 2 is
When arranged in the direction of i, the inspection range L is slightly wider than in the case of FIG. Assuming that the ends of the inspection range L on the plane of the inspection surface 3 are J ₁ and J ₂ respectively, as shown in FIG. 38, if the illumination device 1 is arranged in the regular reflection direction at J ₁ and J ₂ , The entire inspection range L can be irradiated with stripe light. The distance to the lighting device 1 at this time is D.

Next, consider the case where there is a defect in a point J ₁ of FIG. 39. In FIG. 39, the regular reflection direction is set to 0 (deg) as a reference, and the irregular reflection angle due to the unevenness of the defect is ± α.
(Deg) (however, the sign of ± indicates the direction). FIG. 40 is a diagram showing a measurement result of a convex defect called “garbage”. If the distribution of the angles θ at arbitrary plural points on the defect is obtained from such a measurement result,
The diffuse reflection angle α at each point is determined. Next, FIG.
As shown, when thought of as a point J ₁ is on the left slope of the defect, if there is a black stripe stripe illumination greater direction than irregular reflection angle + alpha, the angle at that point θ
A region having a larger inclination angle will appear darker than the surroundings. If this range is W, the relationship between W and α is shown in FIG.
As shown in FIG. Therefore, from FIG. 42, it is required to determine how many ranges where the inclination angle θ of the defect is more than a black point in the white stripe. For example, if a field of view of L = 150 mm is imaged at a resolution of 512 pixels, a defect of 1 mm in width will appear with a width of about 3 pixels, and it can be determined that defect detection is possible. Therefore, α can be found from FIG. 42 such that W = 1 mm. For example, in FIG. 43, when the ratio of white to black of the stripe is 2: 1, the white stripe is -5 to 5 (deg), -10 to 20 (deg),
It is assumed that (W ₁ −W ₂ ) + W ₃ ≧ 1 mm at a ratio of the angle α of the black stripe of 5 to 10 (deg) and 20 to 25 (deg). As shown in FIG. 44 and based on this, J _1, J ₂
, A line with an angle α of the above ratio is drawn. Then, it is sufficient that the black stripe exists at a portion where the black stripe direction (the range of 5 to 10 deg and the range of 20 to 25 deg) intersects.
Thus, from FIG. 44, the approximate spacing T of the stripes
And the distance D can be determined. As described above, the inclination angle distribution as shown in FIG. 40 is obtained for the smallest defect to be detected, and based on this, the stripe illumination is set as described above, so that the optimal illumination for defect inspection can be obtained.

Further, in order to obtain the exact interval T and distance D, the angle calculation in the detection range L may be performed by using the above-mentioned equations (Equation 1) and (Equation 2) of diffuse reflection. . In the above description, the end J of the detection range L
_1, but consider J _2, considering including a plurality of other points, it is possible to perform more precise lighting settings. Further, from FIG. 38, it can be seen that the smaller the θg, the smaller the lighting device 1 can be. When the surface 3 to be inspected is a curved surface, it may be calculated and designed in the same manner as in the case of the above-mentioned plane, based on the above-mentioned (Equation 4) in consideration of the radius of curvature R and the central angle θc. Note that the method of setting the stripe illumination is not limited to the above example.

[0066]

As described above, according to the present invention, both a defect appearing black in a light portion of a stripe image and a defect appearing white in a dark portion of a stripe image are detected. Since the area which can be detected by the method is widened, the defect can be detected quickly. In addition, since a region that is likely to be affected by extremely thin unevenness that does not become a defect such as so-called “yuzu skin” is excluded from the defect detection region, there is no possibility that “yuzu skin” is erroneously detected as a defect. The effect is obtained that the defect can be detected with higher accuracy.

In the case where the amount of area processing for excluding the area where the above-mentioned influence is likely to occur from the defect detection area is set to a value obtained in advance by an experiment or the like, the amount of processing performed at the time of actual inspection is reduced. The amount can be easily set, and the surface roughness of the surface to be inspected is measured for each input image, and the optimum amount of processing is automatically determined from the results. It can be set to an optimal amount according to it, and a precise inspection can be performed.
Further, in the case of adjusting the differential coefficient, the binarization threshold, and the like based on the luminance histogram of the light-dark pattern image, a highly accurate inspection can be realized without being affected by the change in light-darkness due to the color. Further, in the case where a defect is detected by area determination and the stripe boundary line portion is configured as a continuous area, the influence of a curved surface is eliminated, and a highly accurate inspection can always be realized.
Further, in a factory line where a plurality of illumination means and imaging means are arranged so as to surround the inspected object in an arch shape, the entire inspected object can be easily inspected without using an expensive device such as a robot. Can be obtained.

[Brief description of the drawings]

FIG. 1 is a functional block diagram of the present invention.

FIG. 2 is a diagram showing a first embodiment of the present invention.

FIG. 3 is a block diagram of an example of an image processing apparatus 5 in the embodiment of FIG.

FIG. 4 is an exploded perspective view of an example of the illumination device 1 in the embodiment of FIG.

FIG. 5 is a perspective view of another example of the illumination device 1 in the embodiment of FIG.

FIG. 6 is a view showing the principle of displaying a defect on the surface of an inspection object in a received light image.

FIG. 7 is a view showing the principle of displaying a defect on the surface of the inspection object in a received light image.

FIG. 8 is a diagram for explaining a case where the surface to be inspected is a curved surface.

FIG. 9 is a diagram showing a received light image and its image signal.

FIG. 10 is a flowchart showing processing contents in the first embodiment.

FIG. 11 is a diagram showing components included in a signal of an original image.

FIG. 12 is a pixel diagram for explaining a smoothing filter (smoothing).

FIG. 13 is a view showing signals in each processing of the first embodiment.

FIG. 14 is a diagram showing another light and dark pattern of the lighting device.

FIG. 15 is a flowchart illustrating another processing procedure of the image enhancement processing.

FIG. 16 is a diagram showing a function form of a normal function.

FIG. 17 is a diagram showing a function form obtained by secondarily differentiating a normal function.

FIG. 18 is a diagram showing characteristics obtained by digitizing the characteristics shown in FIG. 17;

FIG. 19 is a diagram showing an image signal when there is a luminance change due to color.

FIG. 20 is a diagram showing the frequency of luminance when there is a luminance change due to color.

FIG. 21 is a flowchart illustrating another processing procedure of the boundary area recognition processing.

FIG. 22 is a signal waveform diagram in the boundary area recognition processing of FIG. 21.

FIG. 23 is a perspective view showing an embodiment when the present invention is applied to an automobile body painting inspection line.

FIG. 24 is a perspective view showing another embodiment when applied to an automobile body painting inspection line.

FIG. 25 is a view for explaining the movement state of the image in FIG. 24;

FIG. 26 is a diagram showing another illumination image in FIG. 24;

FIG. 27 is a flowchart showing processing contents when a boundary area processing is provided as a measure against “yuzu skin”.

FIG. 28 is a diagram showing disturbance of a stripe image due to roughness of a surface to be inspected.

FIG. 29 is a pixel diagram for explaining area enlargement / reduction processing.

FIG. 30 is an image diagram and a characteristic diagram for explaining surface roughness measurement.

FIG. 31 is a diagram showing a received light image and its image signal according to the second embodiment of the present invention.

FIG. 32 is a flowchart showing processing contents in the second embodiment of the present invention.

FIG. 33 is a diagram showing an image and its image signal in each processing of the second embodiment of the present invention.

FIG. 34 is a diagram showing a change in an image when the surface to be inspected is a curved surface.

FIG. 35 is a diagram showing a result of converting one pixel around a screen;

FIG. 36 is a diagram showing a received light image and its image signal in a case where a diagonal stripe is added to the illumination device.

FIG. 37 is a diagram showing a relationship between each numerical value of the video camera and an inspection range L.

FIG. 38 is a view for explaining an inspection range L.

FIG. 39 is a view for explaining a relationship between a defect and a detection angle.

FIG. 40 is a view showing measurement results of a convex defect.

Diagram for explaining an example in which Figure 41 points J ₁ is on the left slope of the defect.

FIG. 42 is a characteristic diagram showing a relationship between W and α.

FIG. 43 is a characteristic diagram showing an example of the relationship between W and α.

FIG. 44 is a view for explaining a method of determining a distance T and a distance D between stripes.

[Explanation of symbols]

DESCRIPTION OF SYMBOLS 1 ... Illumination device 6 ... Host computer 1a ... Light source 7 ... Monitor 1b ... Diffusion plate 8 ... Buffer amplifier 1c ... Stripe plate 9 ... A / D converter 1d ... Background 10 ... MPU 2 ... Video camera 11 ... Memory 3 ... Inspection Surface 12 D / A converter 4 Camera control unit 13 Defect 5 Image processing device 14 Body

──────────────────────────────────────────────────続 き Continuation of the front page (56) References JP-A-63-111448 (JP, A) JP-A-6-148098 (JP, A) JP-A-4-16752 (JP, A) JP-A-2- 298841 (JP, A) JP-A-6-235624 (JP, A) JP-A-5-164703 (JP, A) JP-A-63-171345 (JP, A) JP-A-2-93313 (JP, A) (58) Field surveyed (Int.Cl. ⁷ , DB name) G01B 11/00-11/30 102 G01N 21/84-21/958

Claims (25)

(57) [Claims] 1. A surface defect inspection apparatus which irradiates a surface to be inspected with light, creates a light reception image based on light reflected from the surface to be inspected, and detects a defect on the surface to be inspected based on the light reception image. An illumination unit for projecting a predetermined light and dark pattern on the surface to be inspected; an imaging unit for converting a received light image obtained by imaging the surface to be inspected into image data of an electric signal; and a low frequency component among the image data. Boundary region identification means for identifying a boundary region between a bright part and a dark part in a frequency domain, and image enhancement means for extracting only a high-frequency region among frequency components in the image data, and a level having a predetermined value or more, Defect detection means for detecting, as a defect, a portion other than the boundary area identified by the boundary area identification means in the area extracted by the image enhancement means, Inspection equipment. 2. The surface defect inspection apparatus according to claim 1, further comprising a boundary area processing means for performing processing for enlarging the boundary area obtained by said boundary area identification means. 3. The surface defect according to claim 2, wherein said boundary area processing means enlarges the boundary area by a predetermined value according to the roughness of the surface to be inspected. Inspection equipment. 4. The apparatus according to claim 1, wherein said boundary area processing means expands the boundary area by a value corresponding to the roughness of the surface to be inspected obtained from the image data obtained by said imaging means. 3. The surface defect inspection device according to 2. 5. The method according to claim 2, wherein said boundary area processing means performs processing for reducing the boundary area after expanding the boundary area once. Surface defect inspection equipment. 6. The image enhancement means for calculating an absolute value of a difference between an original image of image data obtained by the imaging means and an image obtained by performing a predetermined smoothing process on the original image. The surface defect inspection apparatus according to any one of claims 1 to 5, wherein: 7. The image emphasizing means is for obtaining an absolute value of a result obtained by subjecting an original image of the image data obtained by the imaging means to a normal function and then performing a second differentiation. The surface defect inspection device according to claim 1. 8. The method according to claim 1, wherein the boundary area identifying means differentiates an image obtained by performing a smoothing process on an original image of the image data obtained by the imaging means, and obtains an absolute value of the result. The surface defect inspection apparatus according to any one of claims 1 to 7, wherein 9. An image obtained by the boundary area identifying means,
Performing a smoothing process set based on the luminance histogram of the original image or the roughness of the surface to be inspected, or setting the differential coefficient in the boundary area recognition means based on the luminance histogram of the original image. The surface defect inspection apparatus according to any one of claims 1 to 8, wherein: 10. The method according to claim 1, wherein said defect detecting means calculates a difference between a result of said boundary area recognizing means and a result of said image enhancing means, and binarizes the result with a predetermined threshold value. The surface defect inspection apparatus according to any one of claims 1 to 9, wherein: 11. The apparatus according to claim 10, wherein said defect detecting means adjusts said binarization threshold based on a luminance histogram of an original image or roughness of a surface to be inspected. Surface defect inspection equipment. 12. A surface defect inspection apparatus that irradiates light to a surface to be inspected, creates a light receiving image based on the reflected light from the surface to be inspected, and detects a defect on the surface to be inspected based on the light receiving image. An illumination unit for projecting a predetermined light and dark pattern on a surface to be inspected; an imaging unit for converting a received light image obtained by imaging the surface to be inspected into image data of an electric signal; and an image obtained by the imaging unit Smoothing means for smoothing data; emphasizing means for extracting a portion having a change in brightness from image data of a light and dark pattern smoothed by the smoothing means; and binarizing means for binarizing the result of the emphasizing means. And calculating the area of each of the portions of the screen having the change in brightness obtained by the binarization means, thereby obtaining the area of each of the portions of the screen having the change in brightness. In The area determination processing means for detecting the portion as a defect, a surface defect inspection apparatus comprising the. 13. An apparatus according to claim 12, further comprising an area processing means for performing processing for enlarging an area of a value part corresponding to the part having a change in luminance in the binary signal obtained by said binarization means. Item 13. A surface defect inspection device according to item 12. 14. The method according to claim 1, wherein the area processing means expands an area of a value portion corresponding to a portion having the luminance change by a predetermined value according to the roughness of the surface to be inspected. The surface defect inspection apparatus according to claim 13, wherein: 15. The area processing means for enlarging an area of a value part corresponding to a part having a change in luminance by a value corresponding to roughness of a surface to be inspected obtained from image data obtained by the imaging means. The surface defect inspection apparatus according to claim 13, wherein: 16. An apparatus according to claim 16, wherein said area processing means performs a process of temporarily enlarging an area of a value portion corresponding to a part where the luminance changes, and then reducing the area. A surface defect inspection apparatus according to any one of claims 13 to 15. 17. The apparatus according to claim 12, wherein said area determining means performs an area determining process after removing a predetermined area in contact with a peripheral boundary of a screen in said binarized image. Item 17. A surface defect inspection device according to any one of Items 16. 18. The method according to claim 18, wherein the area determining means determines the area after making one pixel in contact with the peripheral boundary of the screen in the binarized image the same as the dark portion of the stripe. A surface defect inspection device according to any one of claims 12 to 16. 19. The lighting device according to claim 12, wherein the illuminating means projects a pattern consisting of parallel stripes and lines crossing the stripes obliquely as a light and dark pattern. The surface defect inspection device according to the above. 20. The surface defect inspection apparatus according to claim 1, wherein said illuminating means projects a stripe-like light and dark pattern on a surface to be inspected. 21. A surface defect inspection apparatus according to claim 1, wherein said illumination means projects a grid-like light and dark pattern on a surface to be inspected. 22. The illumination means, wherein the width, that is, the interval between the bright and dark portions of the light and dark pattern projected on the surface to be inspected is set to a value sufficiently larger than the size of a defect expected for the purpose of inspection. 22. The surface defect inspection device according to claim 20, wherein the surface defect inspection device is set. 23. The illumination means, wherein the distance between the light and dark portions of the light and dark pattern and the distance from the illumination means to the surface to be inspected are determined by the curvature of the surface to be inspected and the expected defect to the surface to be inspected. It is set based on the angle made,
The surface defect inspection apparatus according to any one of claims 19 to 22, wherein: 24. A system according to claim 21, further comprising means for performing a labeling process and a process of calculating the area of the defect and the center of gravity based on the data on the defect obtained by said defect detecting means or said area determining means. The surface defect inspection apparatus according to any one of claims 1 to 23. 25. A system according to claim 25, wherein a plurality of said image pickup means and a plurality of said illuminating means are arranged in an arch around the object to be inspected, so that a wide inspected surface of the object to be inspected is inspected simultaneously. The surface defect inspection device according to any one of claims 1 to 24.