JP2008233106A - Visual examination method and visual examination device - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To accurately detect flaws inclusive of the peripheral edge part of an inspection target, even when the peripheral edge part has a curved surface, in a visual examination method and in a visual examination device. <P>SOLUTION: An inspection target image P0 including a curved surface part is obtained (S101), and a differential image P1 is obtained from the inspection target image (S102). A binarized image P2 is obtained by extracting end part pixels becoming a predetermined differential value or higher from the respective pixels on the differential image P1 (S103). The region connected by the respective end part pixels is set to an end part region (S104), and a first boundary line comprising the pixels on the end side of the inspection target and a second boundary line comprising the pixels inside the inspection target are determined in the end part region (S105). The first inspection region A1 on the end part side and the second inspection region A2 on the inner inside are determined, on the basis of the respective boundary lines (S106). Flaw inspection is performed under inspection conditions, respectively different in the respective inspection conditions A1 and A2. <P>COPYRIGHT: (C)2009,JPO&INPIT

Description

  The present invention relates to an appearance inspection method and an appearance inspection apparatus using image processing capable of detecting a defect even when a defect exists near the end of an inspection object.

An example of a conventional defect detection method is shown in FIG. This example provides a defect detection method capable of detecting defects present in the peripheral portion by including the peripheral portion of the inspection target in the inspection window. The defect detection method is a method of detecting a pixel having a large luminance change by differentiating a grayscale image obtained by imaging an inspection object, and the grayscale image is binarized so as to separate an inspection window representing the inspection region from other regions. A binarization step (S1) to be converted, an extraction step (S2) to extract only the inspection image from the inspection window, and pixels having the same luminance as the peripheral portion of the inspection image are arranged around the inspection image. The image enlarging step (S3) for creating an enlarged image and the differential binarizing step (S4) for differentiating the enlarged image and binarizing it to create an edge binary image are included. Of the edge binary image, the entire region included in the inspection window is set as a detection range (see, for example, Patent Document 1).
Japanese Patent Laid-Open No. 11-64236

  However, in the conventional example as described above, the peripheral portion can be inspected. However, when the peripheral portion of the inspection object (hereinafter also referred to as an end portion or a contour) has a curved surface, that is, the peripheral portion of the inspection object. If the image pickup surface is inclined with respect to the image pickup device and the inclination is not constant, light and shade appear on the image near the periphery due to the difference in the amount of light from the inclined surface, so that an actual defect is detected in the light and dark portion. There is a problem that becomes difficult.

  The present invention solves the above-mentioned problems, and provides an appearance inspection method and an appearance inspection apparatus capable of realizing accurate defect detection including a peripheral portion even when the peripheral portion of an inspection object is a curved surface. With the goal.

  In order to achieve the above object, the invention of claim 1 images an inspection object having a curved surface in an outline, and uses the grayscale distribution generated from the end to the inside of the captured image in the captured image. In the appearance inspection method for inspecting the appearance of the image, a differential image is generated by performing a differential process on each pixel of the gray image obtained by imaging the inspection object, and the pixels having a predetermined differential value or more on the differential image are end portions A process of extracting as a pixel, a process of extracting a region connected by each extracted end pixel as an end region, and determining a first boundary line on the end side of the inspection object in the end region; A width of the end region in a direction orthogonal to the first boundary line is obtained for each pixel on the first boundary line, and the width of the end region is used as a parameter to determine the width of the inner side of the object on the basis of the first boundary line. A boundary line determination process for determining the boundary line of 2; An inspection area determination process for determining a first inspection area and a second inspection area based on each boundary line, and inspecting the first inspection area and the second inspection area under different inspection conditions. This is an appearance inspection method.

  According to a second aspect of the present invention, in the visual inspection method according to the first aspect, the inspection determination level of the first inspection region is inclined so as to be looser or stricter as it is closer to the first boundary line.

  According to a third aspect of the present invention, in the appearance inspection method according to the first aspect, when the extracted end region is discontinuous, a differential direction value defined as a direction value orthogonal to the density gradient direction in the pixels of the discontinuous portion. Based on the above, discontinuous portions are connected to re-extract the end region.

  According to a fourth aspect of the present invention, in the visual inspection method according to the first aspect, after the first end pixel is extracted, the end pixel is determined based on the differential direction value defined as the direction value orthogonal to the density gradient direction in the end pixel. Search for the neighborhood of a partial pixel, track and extract the next consecutive edge pixel, and if the extracted pixel's differential value exceeds a predetermined threshold, the pixel is included in the edge pixel and the edge region is To decide.

  According to a fifth aspect of the present invention, in the visual inspection method according to the first aspect, the reference inspection area is set in advance, and after the first and second inspection areas are determined, the first and second inspection areas are defined as the first and second inspection areas. Automatic adjustment that is displayed superimposed on the reference inspection area, and that an alarm is output when the positional deviation between the first and second inspection areas and the reference inspection area exceeds a predetermined amount, or instructing adjustment to the reference inspection area The illumination condition and / or the imaging condition are automatically adjusted by outputting the instruction signal.

  According to a sixth aspect of the present invention, in the appearance inspection method according to the first aspect, when regular reflection of illumination occurs in the end region of the inspection object, two or more types of images in which the illumination angle is switched are captured, and a plurality of images are captured. A specular reflection removal image is generated by removing the luminance value of the pixel having the maximum luminance from pixels in the same part of the object between the images, and the first and second inspection areas are determined using the specular reflection removal image. It is.

  A seventh aspect of the present invention is the visual inspection method according to the sixth aspect, wherein the inspection is performed using the regular reflection removed image.

  According to an eighth aspect of the present invention, in the appearance inspection method according to the sixth aspect, the specular reflection portion is inspected using an image that is not specularly reflected.

  According to a ninth aspect of the present invention, in the visual inspection method according to the first aspect, the inspection object is imaged in a plurality of regions under illumination imaging conditions suitable for each end portion, and each end portion is provided for each image. The first and second inspection areas corresponding to the above are determined, the respective first and second inspection areas are inspected for each image, and then the inspection results are integrated and determined.

  According to a tenth aspect of the present invention, in the visual inspection method according to the ninth aspect, the inspection areas are overlapped with respect to the plurality of areas to be imaged.

  The invention according to claim 11 is the appearance inspection method according to claim 1, wherein the differential direction is defined as a direction value orthogonal to the density gradient direction in each pixel in each of the smaller areas in each of the first or second inspection areas. Based on the value distribution data, the defect determination threshold value is switched for each differential direction value of the defect candidate portion in each small region.

  According to a twelfth aspect of the present invention, in the appearance inspection method according to the eleventh aspect, detection sensitivity of pixels having a differential direction value occupying a large number is set lower than other pixels based on the distribution data of the differential direction value. .

  According to a thirteenth aspect of the present invention, an appearance inspection apparatus that images an inspection object having a curved surface in an outline and inspects the appearance of the inspection object using a gray-scale distribution generated from an end portion of the object to the inside of the captured image. End pixel extraction, wherein a differential image is generated by differentiating each pixel of the grayscale image obtained by imaging the inspection object, and a pixel having a predetermined differential value or more on the differential image is extracted as an end pixel. Means, an end region extraction unit that extracts a region connected by each extracted end pixel as an end region, and, in the end region, determines a first boundary line on the end side of the inspection object; A width of the end region in a direction orthogonal to the first boundary line is obtained for each pixel on the first boundary line, and the width of the end region is used as a parameter to determine the width of the inner side of the object on the basis of the first boundary line. A boundary line determining means for determining the boundary line of 2; An appearance inspection apparatus having inspection area determining means for determining a first inspection area and a second inspection area based on each boundary line, and inspection / determination means for inspecting the inspection areas under different inspection conditions. is there.

  According to the first aspect of the present invention, the vicinity of the peripheral portion and the region of the inner portion of the inspection object separated from the peripheral portion are divided, and different image processing conditions are set for image processing and determination processing. Detection is easy even when the contour portion has a curved surface, a change in density occurs from the end to the inside of the inspection object, and a defect exists in the portion.

  According to the first aspect of the present invention, the second boundary line is determined based on the inspection region width from the first boundary line on the end side of the inspection object and the differential direction value of the pixel on the boundary line. Therefore, it is possible to set a boundary line that follows the width of the delicate curved surface portion in the end region.

  According to the second aspect of the present invention, the end region originally has a shading change, and if the inspection level is set strictly, there is a possibility of overdetecting the defect. Can be avoided. Also, when it is desired to make a stricter determination as it is closer to the end portion, the inspection accuracy can be improved by setting the inspection level to be stricter in an inclined manner as it is closer to the end side.

  According to the invention of claim 3, the differential value is locally lowered in the end region due to the subtle unevenness or surface property change of the inspection object, and is lost and discontinuous when the threshold processing is performed. However, it is possible to extract a stable end region by performing the connection process based on the differential direction value of the neighboring pixels.

  According to the fourth aspect of the present invention, since the end region is determined in consideration of the direction of change in shading of each pixel, the end pixel can be tracked accurately and efficiently, and the end region can be extracted at high speed.

  According to the fifth aspect of the present invention, in the actually captured image with respect to the contour of the inspection region that should be the reference, the end region may be shifted depending on the illumination or the imaging condition. This makes it possible to clarify the deviation between the edge region extracted by the above and the region to be used as a reference, and to easily adjust the illumination / imaging conditions. In addition, when the end region extracted from the captured image is deviated from the inspection region to be used as a reference, an output indicating this is made easier to recognize the necessity of illumination / imaging condition correction. In addition, when the edge region extracted from the captured image is shifted from the inspection region to be used as a reference, the adjustment of the illumination / imaging conditions can be made efficient by automatically adjusting the edge region.

  According to the invention of claim 6, when it is unavoidable that regular reflection occurs in the end region depending on the illumination condition, it is possible to perform an accurate inspection with the influence removed.

  According to the invention of claim 7, after inspection of the specular reflection component removed image after setting the inspection region by removing the specular reflection component, the inspection of the portion where the specular reflection was made in one inspection Is possible.

  According to the eighth aspect of the present invention, the specular reflection component is removed and the inspection area is set, and then the portions of the plurality of images used for the setting that do not include the specular reflection component are inspected with respect to the original image. The inspection can be done without any deterioration.

  According to the invention of claim 9, when inspecting an inspection object, if the setting of the inspection area is not successful under a single illumination imaging condition, imaging is performed under a plurality of conditions, and inspection is performed based on each image. Since the area is set, the optimum inspection area can be set.

  According to the invention of claim 10, when an inspection object is inspected, the inspection area cannot be set properly under a single illumination imaging condition, and imaging is performed under a plurality of conditions, and inspection is performed based on each image. When setting the areas, it is possible to inspect while maintaining the continuity between the areas by providing an overlapping portion in the inspection area on each image.

  According to the eleventh aspect of the invention, the differential process is performed when setting the inspection area, and the differential direction value calculated at that time is used to characterize the differential direction value in the defect in the inspection area in the subsequent inspection. By setting a threshold value that is easy to detect in some cases (for example, continuity, directionality, etc.), inspection accuracy can be improved.

  According to the twelfth aspect of the present invention, when there is an array of pixels having a specific differential direction value as a pattern in the inspection region, the threshold value is increased so that the direction component is not recognized as a defect and the detection sensitivity is decreased. Therefore, it is possible to improve the defect detection accuracy other than the pattern without overdetecting the pattern.

  According to the invention of claim 13, even if the contour portion of the inspection object has a curved surface, a change in density occurs from the end portion to the inside of the inspection object, and there is a defect in the portion, Since the vicinity of the part and the inside are separated and inspected under different inspection conditions, defect detection becomes easy.

  Hereinafter, an appearance inspection method and an appearance inspection apparatus according to an embodiment of the present invention will be described with reference to the drawings. Common members in the drawings are denoted by the same reference numerals, and redundant description is omitted. First, an outline of the appearance inspection method will be described. FIG. 1 shows a process flow of an appearance inspection method. The object of the appearance inspection is assumed to have a curved surface at the contour portion (end portion) and include the curved surface portion as an inspection range, and image capturing including the curved surface portion is performed, and the density intensity An inspection target image P0, which is a light and shade distribution image using (the intensity and brightness of white density) e as data, is obtained (S101). A differential operation is performed on the distribution of the density intensity e of the obtained inspection target image P0 to obtain a differential image P1 that is an image of the differential intensity f (S102). Next, a pixel having a predetermined differential value or more is extracted from each pixel on the differential image P1, and an end pixel extraction process for selecting a pixel smaller than the predetermined differential value is performed to obtain a binarized image P2. (S103). Further, an area connected by the extracted end pixels is set as an end area (S104). Next, in the end region, a first boundary line composed of pixels on the end side of the inspection object and a second boundary line composed of pixels inside the object are determined (S105). Next, based on each boundary line, a first inspection area A1 that is an end-side area and a second inspection area A2 that is an inner-side area are determined (S106). Then, image processing is performed in which inspection is performed under different inspection conditions in the first inspection region A1 and the second inspection region A2 (S107). Thus, by performing inspection under different inspection conditions for each inspection region, it is possible to perform an appearance inspection with high defect detection accuracy without erroneous detection or missing detection. Next, the appearance inspection method and apparatus will be described in detail.

  The configuration of the appearance inspection apparatus will be described along the flow of image data. FIG. 2 shows the configuration of the appearance inspection apparatus. The inspection object 4 placed on the mechanism unit 5 is illuminated from the illumination device L, the surface of the inspection object 4 is imaged by the CCD camera 2, and the data is transferred to the appearance inspection apparatus body 3 via the cable 23. Sent. The captured image data is taken into the appearance inspection apparatus main body 3, and the result of quality determination regarding image processing and appearance inspection is sent to the mechanism unit 5. The mechanism unit 5 performs defective product discharge control based on the result of the quality determination.

  The appearance inspection apparatus main body 3 mainly performs image processing. FIG. 3 shows a block configuration of the appearance inspection apparatus main body 3. The appearance inspection apparatus 3 will be described along the flow of image data captured by the CCD camera 2. The image data is converted into multi-gradation digital image data from the CCD camera 2 via the A / D converter 31 and stored in an image memory (VRAM) 32. An image processing program including a program for setting an inspection area is loaded in advance from a storage medium (ROM) 35 into an image processing program memory area 34d of a main storage section (RAM) 34 used by a calculation section 36 that performs image processing. Has been. The calculation unit 36 performs various image processes on the image data input from the image memory (VRAM) 32 to the image memory area 34a of the main storage unit 34, and necessary intermediate data (differential image, differential direction image, etc.). Are sequentially stored in the differential image memory area 34b and the differential direction image memory area 34c. In addition, the calculation unit 36 outputs the determination result of the appearance inspection, and performs operation control of the external mechanism unit 5 via the external control 39 (for example, the inspection object discharge method is changed according to pass / fail). The signal control 37 controls data between the memories 32 and 34 and the calculation unit 36. The image data in the image memory (VRAM) 32 is converted into an analog signal via the D / A converter 38 and displayed on the monitor M.

  The inspection object 4 will be described. FIG. 4 shows a partial cross-sectional view of the inspection object. The inspection object 4 has a curved surface 40 at its end. When the inspection object 4 having a curved surface at such an end is irradiated with illumination light L1 from above and picked up by the CCD camera 2 positioned above, the side surface 42 of the inspection object 4 becomes a dark part and the curved surface 40 is below From the top to the top, it gradually becomes a bright part, and the inner upper surface 41 becomes a bright part. The upper surface 51 of the mechanism portion on which the inspection object 4 is placed is a flat portion, and becomes a bright portion in the same manner as the inner upper surface 41.

  An image captured including the curved surface 40 will be described. FIG. 5A shows the captured grayscale image, and FIG. 5B shows the image density. The inspection target image P0 includes a background image 51a, a side image 42a, a curved surface image 40a that gradually changes from dark to bright, and an inner upper surface image 41a that is a bright portion. The distribution of the image density e on the scanning line x that crosses the inspection target image P0 from side to side is as shown in FIG. In the grayscale image, the dark portion has a large image signal intensity and is bright and white, and the light portion has a small image signal intensity and is dark and black. In other words, the density represents white density, and the higher the density, the brighter the density.

  The differential processing of the inspection target image P0 will be described. 6A shows an image obtained by differentiating the grayscale image shown in FIG. 5A, and FIG. 6B shows the differential intensity. When the grayscale image is subjected to differential processing, a portion with a large shade change becomes bright and a portion with little change becomes dark, and a differential image P1 in FIG. 6A is obtained. Here, the differential processing is obtained by scanning the original image P0 in the x direction (horizontal direction of the image) and the y direction (vertical direction of the image) with, for example, a 3 × 3 pixel x direction differential operator and a y direction differential operator. The differential intensity in each pixel is calculated by combining the differential values in the x and y directions. The differential image P1 includes a background differential image 51b, a side differential image 42b, a curved differential image 40b that gradually changes from light to dark, and an inner upper differential image 41b that is a dark portion. The distribution of the differential intensity f on the scanning line x crossing the differential image P1 to the left and right is as shown in FIG.

  Next, the binarization process and the end pixel extraction process of the differential image P1 will be described. FIG. 7A shows a binarized image, and FIG. 7B shows differential intensity indicating a differential threshold. In the differential image P1 obtained by differentiating the inspection object image P0, a portion having a differential value equal to or higher than a differential threshold f0 set in advance is defined as an end region. Further, in the end region thus obtained, a first boundary line S1 composed of pixels located on the end side (external side) of the inspection object is set, and the inspection object is further detected in the end region. A second boundary line S2 composed of pixels inside the object is set. At this time, the differential threshold value for determining the first and second boundary lines S1 and S2 can be determined based on values set separately in advance. The first boundary line S1 is usually a side surface portion of the inspection object. In the inspection object image P0, the boundary between the background image and the image of the inspection object appears as a side image (contour image). In general, it can be assumed that this contour image corresponds to a portion where the density change is large. It can also be adjusted by adjusting the light from the background.

  Next, setting of the inspection area will be described. FIG. 8A shows the relationship between the inspection object image and the inspection area, and FIG. 8B shows the differential intensity indicating the differential threshold. A first inspection region A1 is set in a portion between the first boundary line S1 and the second boundary line S2 that needs to be inspected, and the portion inside the inspection object from the second boundary line S2 Then, the second inspection area A2 is set in the portion that needs to be inspected. It is assumed that there is a defect X1 in the first inspection area A1 and a defect X2 in the second inspection area A2.

Next, defect detection will be described. FIG. 9A shows a defect detection image, and FIG. 9B shows an image density including a defect detection threshold. In the defect detection by image processing, the detection principle is that the defect portion in the grayscale image has a different shade level compared to other portions. Here, the description will be made on the assumption that the density of the defective portion is lower than that of the other portion, that is, a dark portion. In such a case, by performing binarization processing on the inspection target image P0 with a predetermined image density threshold value in order to detect a defect, as shown in FIG. 9A, the defective portions X1 and X2 are dark portions. Can be embossed on the defect detection image P3. In the case of the present invention, separate binarization threshold values (image density threshold value, defect detection threshold value) in the first inspection area A1 and the second inspection area A2, for example, the threshold value in the inspection area A1. A value TH1 is set, and a threshold value TH2 is set in the inspection area A2. Here, with respect to the average image density e1 in the area A1, the average image density e2 in the area A2, and the image density ex of the defective portion, the magnitude relationship of the threshold is:
ex ≦ TH1 ≦ e1 (region A1),
ex ≦ TH2 ≦ e2 (region A2),
It is. By the way, if the threshold value is uniformly set to THx without dividing the inspection area, all portions corresponding to the first inspection area A1 are defective. In this way, by dividing the inspection area and using different binarization threshold values, even if there are defects in the edge area or the area inside the object, it is possible to inspect according to the inspection conditions suitable for each. Become.

  Next, the flow of image processing for defect extraction will be described. FIG. 10 shows the flow of the processing. First, the defect detection is performed by binarizing the inspection area A1 of the inspection target image P0 with the threshold value TH1 (S201). When a defect is detected (Yes in S202), the defect is recorded (S203). Next, the inspection area A2 is binarized with the threshold value TH2 to detect a defect (S204). If a defect is detected (Yes in S205), the defect is recorded (S206). Next, if there is no defect record in both the inspection areas A1 and A2 (Yes in S207), it is determined that there is no defect (S208), and if there is a defect record in any of the inspection areas A1 and A2 (No in S207), the defect It is determined that there is (S209).

  Next, another method for determining the second boundary line will be described. FIG. 11A shows a binarized image for determining the boundary line, and FIG. 11B shows the differential intensity. As described above, with respect to the differential image P1 obtained by differentiating the inspection target image P0, a portion where the differential value is equal to or larger than a preset threshold value is defined as an end region, and the end side (outside) of the inspection target in the end region. ) Determines the first boundary line S1. At this time, as shown in FIG. 11A, the width W1 of the end region in the direction orthogonal to the first boundary line S1 is obtained, and the width W1 is used as a parameter, with the first boundary line S1 as a reference. A second boundary line S2 is determined. For example, the distance W2 between the boundary lines S1 and S2 is determined based on a condition such as W2 = 1.2 × W1. Since the direction orthogonal to the first boundary line S1 is the direction of the density gradient in the pixels on the boundary line, it is possible to set the boundary line following the width of the delicate curved surface portion in the end region.

  As yet another method, the first inspection region is determined with reference to the first boundary line S1, and the direction perpendicular to the first boundary line S1 is determined at each end pixel on the first boundary line S1. The second boundary line S2 and the second inspection region are obtained with reference to the position where the differential value (differential intensity value) is not more than a predetermined threshold value on the line. A2 may be determined. In this way, it is possible to set the inspection area that closely follows the change in shading in the end area.

  Next, another example of the defect detection threshold will be described. 12A and 12B show the image density including the defect detection threshold. As shown in the figure, the image density e0 is not constant in the first inspection area A1, so that the closer to the first boundary, the closer to the first boundary line, instead of a constant inspection determination level (differential value threshold). By setting the defect detection threshold value TH3 to a lower level, it is possible to carry out a defect inspection that is more realistic. In this way, the end region originally has a change in shading, and if the inspection level is set strictly, there is a possibility of overdetecting a defect. Therefore, the closer to the end side, the overdetection can be avoided by relaxing the inspection level. Further, if the inspection determination level of the first inspection area A1 is made stricter as it is closer to the first boundary line, it is possible to avoid missing a defect for a special defect that occurs peculiar to the end of the inspection object.

  Next, connection of pixels in the end region will be described. FIG. 13 shows a local enlarged view in the end region. In the end region extracted from the image P2 obtained by differentiating the inspection target image P0 and binarized, the end region is obtained even though a certain pixel px1 includes all the surrounding pixels px in the end region. For example, when the end region pixel is extracted, the pixel becomes lower than the threshold for differential binarization and is connected (included) to the end image. There are cases where it is not possible. For the pixel px1 that has become discontinuous in this way, the differential direction value is examined, and if the direction is continuous to the peripheral end pixel (the differential direction value is the same within a predetermined range), the end region To be added. Here, the differential direction value is a direction (tangential direction vector T) orthogonal to the density gradient vector N in the grayscale image. Due to subtle unevenness and surface properties of the inspection object, the differential value is locally low in the edge region, and this is the case for pixels that have become discontinuous due to threshold processing. By performing the pixel connection processing, it is possible to extract a stable end region.

  Next, another method for generating the end region will be described. For example, after determining the first boundary line S1, the pixels constituting the first boundary line S1 are extracted as the first end pixel, and defined as the direction value orthogonal to the density gradient direction at the end pixel. Based on the differential direction value to be searched, the vicinity of the end pixel is searched and the next consecutive end pixel is tracked and extracted, and when the differential value of the extracted pixel exceeds a predetermined threshold value, The edge region is determined including the partial pixel. As described above, when extracting the pixels included in the end pixels, it is determined whether or not the end pixels are determined by examining the differential values of the pixels that are continuous from the differential direction values of the first extracted pixels. By repeating this process, an end pixel group is extracted and extracted as an end region. According to this, since the edge region is determined in consideration of the direction of change in shading of each pixel, the edge pixel can be tracked accurately and efficiently, and the edge region can be extracted at high speed.

  Next, adjustment of illumination / imaging conditions will be described. FIG. 14 shows the configuration and screen display of the appearance inspection apparatus. After the reference inspection area B (B1, B2) is set in advance and the first and second inspection areas A (A1, A2) are determined, the first and second inspection areas A are defined as the reference inspection area B. When the displacement between the first and second inspection areas A and the reference inspection area B exceeds a predetermined amount, the appearance inspection apparatus main body 3 outputs an alarm. Or, together with the alarm output, the appearance inspection apparatus main body 3 outputs an automatic adjustment instruction signal or a defect signal, and automatically adjusts the illumination conditions of the illumination apparatus L and / or the imaging conditions of the CCD camera 2. In the actually captured image, the edge region may be shifted due to the illumination or the imaging conditions with respect to the outline of the inspection region that should be the reference. By this method, it is possible to clarify the deviation between the end region extracted by image processing and the region to be used as a reference, and to easily adjust the illumination / imaging conditions. Further, when the end region extracted from the captured image is deviated from the inspection region to be used as a reference, an output indicating the fact makes it easy to recognize the necessity of illumination / imaging condition correction. In addition, when the edge region extracted from the captured image is shifted from the inspection region to be used as a reference, the adjustment of the illumination / imaging conditions can be made efficient by automatically adjusting the edge region.

  Next, processing of the regular reflection component of illumination will be described with reference to FIGS. 15 and 16. FIG. 15 shows a grayscale image including regular reflection light, and FIG. 16 shows an image density including regular reflection light. As shown in FIG. 15, when the inspection target image P01 of the inspection target includes the regular reflection DR1 and the inspection target image P02 having different illumination conditions includes the regular reflection DR2, the normal inspection is performed from the two inspection target images P01 and P02. An inspection target image P0 excluding the reflection components DR1 and DR2 is obtained. As shown in FIG. 16, the regular reflection removed image P0 compares the image densities ee1 and ee2 of the pixels at the same location in the images P01 and P02, and the smaller one is the image density e0 of the regular reflection removed image P0. Is obtained. An inspection can be performed using such a regular reflection removal image P0. Alternatively, an inspection area may be set in advance using the regular reflection removal image P0, and the actual defect detection inspection may be performed on a portion where there is regular reflection and no regular reflection in the images P01 and P02.

  Next, the division inspection of the inspection object will be described. FIG. 17 shows illumination imaging conditions for the inspection object. In the case where it is better to image the four sides near 4a to 4d of the imaging surface of the inspection object M under different illumination conditions, after setting the inspection area separately for the images captured under the respective illumination conditions, Perform defect detection inspection. The determination result by the defect inspection of the inspection object is performed by integrating the inspection results of the plurality of images. Also, when multiple images are taken under different illumination conditions, each inspection object belongs to a series of objects, so an overlapping part is set between each image in order to provide continuity in the inspection area And imaging.

  Next, the removal of the influence by a specific pattern that is not a defect will be described. FIG. 18 shows an inspection target image and a differential image when there is a pattern. As shown in the inspection object image P0 in FIG. 18A, when the surface of the inspection object originally has a striped pattern st and there is a defective portion (black dot) X3 between the patterns, the image is differentiated. Then, as shown in the differential image P11 of FIG. 18B, the differential intensity of the portion where the density suddenly changes in each of the original image densities of the stripe pattern st1 together with the defect portion X31 increases, and the differential corresponding to those portions. The density of the portion of the image P11 increases. In such a case, when the defect determination threshold value of the pixels having the differential direction values in the streak patterns st and st1 directions is set softly (under the condition that the striped pattern is not detected), as shown in the differential image P12, the striped pattern is obtained. It is possible to remove the image of this component, and it is possible to detect only the defective portion.

  In addition, in the case of an inspection object having a surface with a stripe pattern, the frequency of pixels having the direction of the stripe pattern component is high in the differential direction image (image in which the differential direction value is associated with each pixel). By reducing the pixel detection sensitivity, it is possible to easily detect a defective portion having a differential direction component other than the stripe pattern direction. In this way, when a differential process is applied to a pattern having a specific directionality, the directionality information is weighted in each direction when the differential intensity is calculated by combining the x-direction differential value and the y-direction differential value. As a result, it is possible to improve the accuracy of defect detection.

  The present invention is not limited to the above-described configuration, and various modifications can be made. In addition, in the grayscale image differentiation process, other methods can be used in addition to the standard 3 × 3 pixel method.

The flowchart of the external appearance inspection method by one Embodiment of this invention. The block diagram of the external appearance inspection apparatus by one Embodiment of this invention. The block block diagram of an external appearance inspection apparatus same as the above. The partial cross-section perspective view of an external appearance test object. (A) is a grayscale image obtained by the same visual inspection apparatus, and (b) is an image density diagram in the scanning direction x of (a). (A) is a differential image of the same gray image diagram, (b) is a differential intensity diagram in the scanning direction x of (a). (A) is the binarized image of the differential image same as the above, (b) is the differential intensity | strength figure in the scanning direction x of (a). (A) is the figure which set the test | inspection area | region in the grayscale image obtained by the visual inspection apparatus same as the above, (b) is the differential intensity | strength figure in the scanning direction x of (a). (A) is the figure of the grayscale image and defect obtained by the external appearance inspection apparatus same as the above, (b) is the figure of the average image density and the binarization threshold value in the scanning direction x of (a). The defect extraction process flow figure in an external appearance inspection method same as the above. (A) is a differential intensity binarized image diagram according to the same visual inspection method as above, and (b) is a differential intensity diagram in the scanning direction x of (a). (A) is a figure which shows the differential intensity | strength binarization threshold value setting which concerns on an external appearance inspection method same as the above, (b) is a figure which shows the other setting of the threshold value. The figure explaining the connection method of the boundary line pixel which concerns on an external appearance inspection method same as the above. Explanatory drawing of the structure and screen display of an external appearance inspection apparatus which concern on an external appearance inspection method same as the above. The gray image figure explaining the process of the image containing the regular reflection light which concerns on an external appearance inspection method same as the above. (A) and (b) are image density diagrams of an image including regular reflection light, and (c) is an image density diagram of a regular reflection light removed image. The perspective view of the test target object explaining the imaging method which concerns on an external appearance inspection method same as the above. (A) is a gradation image figure of the test object containing a striped pattern, (b) is a differential intensity image figure of (a), (c) is a differential intensity figure of (a) by an external appearance inspection method same as the above. The defect detection flowchart in a conventional method.

Explanation of symbols

DESCRIPTION OF SYMBOLS 3 Appearance inspection apparatus main body 4 Inspection object A1 1st inspection area A2 2nd inspection area P0, P01, P02 Gray image P1 Differential image P2 Binary image px End pixel S1 1st boundary line S2 2nd Boundary line TH1 to TH4 Inspection judgment level (threshold)

Claims (13)

In an appearance inspection method for inspecting an appearance of an inspection object by imaging a test object having a curved surface in an outline and using a gray-scale distribution generated from an end portion of the object to the inside of the captured image,
A process of differentiating each pixel of the grayscale image obtained by imaging the inspection object to generate a differential image, and extracting a pixel having a predetermined differential value or more on the differential image as an end pixel;
Extracting a region connected by each extracted end pixel as an end region;
In the end region, a first boundary line on the end side of the inspection object is determined, and a width of the end region in a direction orthogonal to the first boundary line is obtained for each pixel on the first boundary line. A boundary line determination process for determining a second boundary line inside the object based on the first boundary line with the width as a parameter;
An inspection area determination process for determining a first inspection area and a second inspection area based on each boundary line;
An appearance inspection method characterized by inspecting the first inspection region and the second inspection region under different inspection conditions.   2. The appearance inspection method according to claim 1, wherein the inspection determination level of the first inspection region is inclined so as to be looser or stricter as it is closer to the first boundary line.   When the extracted end region becomes discontinuous, the end region is re-extracted by connecting the discontinuous portions based on the differential direction value defined as the direction value orthogonal to the density gradient direction in the pixels of the discontinuous portions. The appearance inspection method according to claim 1.   After extracting the first edge pixel, based on the differential direction value defined as the direction value orthogonal to the density gradient direction at the edge pixel, the neighborhood of the edge pixel is searched and the next consecutive edge pixel is tracked 2. The appearance inspection method according to claim 1, wherein when the differential value of the extracted pixel exceeds a predetermined threshold value, the end region is determined by including the pixel in the end pixel.   A reference inspection area is set in advance, and after determining the first and second inspection areas, the first and second inspection areas are displayed so as to overlap the reference inspection area, and the first and second inspection areas are displayed. When the deviation of the position of the reference inspection area exceeds a predetermined amount, an alarm is output, or the automatic adjustment instruction signal output for instructing the adjustment to the reference inspection area is automatically adjusted to adjust the illumination condition and / or the imaging condition. The appearance inspection method according to claim 1.   When regular reflection of illumination occurs in the end region of the inspection object, two or more types of images with different illumination angles are captured, and the luminance of the pixel having the maximum luminance among the pixels of the same part of the object between the plurality of images The appearance inspection method according to claim 1, wherein a regular reflection removal image is generated by removing values, and the first and second inspection regions are determined using the regular reflection removal image.   The appearance inspection method according to claim 6, wherein the inspection is performed using the regular reflection removed image.   7. The appearance inspection method according to claim 6, wherein the specular reflection portion is inspected using an image that is not specularly reflected.   The inspection object is imaged by dividing it into a plurality of regions under illumination imaging conditions suitable for each end, and first and second inspection regions corresponding to the respective end portions are determined for each image, and for each image 2. The appearance inspection method according to claim 1, wherein inspection is performed in each of the first and second inspection regions, and then the inspection results are integrated and determined.   The visual inspection method according to claim 9, wherein each inspection region is overlapped with respect to a plurality of regions to be imaged.   Based on the distribution data of the differential direction value defined as the direction value orthogonal to the density gradient direction in each pixel in each small region in the first or second inspection region, the differential direction of the defect candidate portion in each small region The appearance inspection method according to claim 1, wherein the defect determination threshold value is switched for each value.   12. The appearance inspection method according to claim 11, wherein the detection sensitivity of pixels having differential direction values occupying a large number is set lower than other pixels based on the distribution data of the differential direction values. In an appearance inspection apparatus that images an inspection object having a curved surface in an outline, and inspects the appearance of the inspection object using a gray-scale distribution generated from an end portion of the object to the inside of the captured image,
End pixel extraction means for generating a differential image by performing differential processing on each pixel of the grayscale image obtained by imaging the inspection object, and extracting a pixel having a predetermined differential value or more on the differential image as an end pixel; ,
End region extraction means for extracting the region connected by each extracted end pixel as an end region;
In the end region, a first boundary line on the end side of the inspection object is determined, and a width of the end region in a direction orthogonal to the first boundary line is obtained for each pixel on the first boundary line. , A boundary line determining means for determining a second boundary line inside the object based on the first boundary line, using the width as a parameter;
Inspection area determining means for determining a first inspection area and a second inspection area based on each boundary line;
An appearance inspection apparatus comprising: inspection / determination means for inspecting each inspection region under different inspection conditions.

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