JP2020051959A - Surface inspection device, component manufacturing method, and program - Google Patents

Abstract

To provide a surface inspection device and the like that make it easier to grasp a size of a defect in comparison to a case where planarly measuring the size of the defect from one direction with respect to an inspected body whose surface has a curved shape.SOLUTION: A surface inspection device 1 comprises: an illumination 10 that irradiates a workpiece W serving an inspected body with light; a rotation mechanism 20 that causes the workpiece W to rotate with a predetermined axis as a center; a camera 30 that photographs the workpiece W from a direction orthogonal to a direction of the axis as rotating the workpiece W, and thereby captures a plurality of images different in rotation angle; and a control unit 40 that detects a defect existing in a surface of the workpiece W on the basis of the image. The control unit 40 includes: a defect extraction unit that extracts the defect existing in the image; a two-dimensional map preparation unit that develops the extracted defect into a two-dimensional map expressing the surface of the workpiece W by a plane; a three-dimensional map preparation unit that converts the defect in the two-dimensional map to a three-dimensional map matching a curved face shape of the surface of the workpiece W; and a defect detection unit that detects a size of the defect.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a surface inspection device, a component manufacturing method, and a program.

2. Description of the Related Art Conventionally, there has been known a surface inspection apparatus that irradiates an object to be inspected with light and detects presence or absence of a defect such as a scratch on the surface of the object to be inspected based on an image of the object to be inspected.
At this time, when the surface of the object to be inspected has a curved surface shape such as a spherical shape, a method of acquiring an image by photographing the object to be inspected from directly above or by taking a plurality of images while rotating the object is known. May be used.

For example, in Patent Literature 1, a sphere appearance inspection device rotates a sphere to be inspected by a sphere rotating mechanism and captures an image of the entire surface of the sphere with a CCD camera. The document describes that image data obtained by the CCD camera is subjected to image processing by a computer to detect the presence or absence of scratches and color unevenness occurring on the outer surface of the sphere.
Further, in Patent Document 2, a surface inspection apparatus configures a measurement space having a curved wall and uniformly illuminates a measurement object in the measurement space by illuminating a light source light of an illumination device. All the illumination images of the object to be measured are photographed, and the pattern light is projected and reflected on a curved wall by a pattern light projecting device so as not to directly hit the object to be measured. It describes that imaging of a reflection pattern image of a projected measurement target is performed.

JP 2001-13083 A JP 2011-179982 A

However, if the surface of the object to be inspected has a curved shape such as a spherical shape, the size of an image photographed differs depending on the location even if the size of the defect is the same. Further, when trying to detect the inclination of a defect as a shadow, it is difficult to determine an appropriate illumination method for photographing the entire surface of the inspection object.
The present invention provides a surface inspection apparatus and the like in which the size of a defect can be more easily grasped as compared with a case where the size of a defect is measured two-dimensionally from one direction on an object to be inspected having a curved surface. The purpose is to:

Under such a purpose, the present invention provides a method of irradiating a test object having a curved surface with light, while rotating the test object about a predetermined axis, from a direction intersecting the axis direction. An image acquisition unit that acquires a plurality of images having different rotation angles from an imaging unit that captures an object to be inspected; a defect extraction unit that extracts a defect existing in the image; A two-dimensional map creating unit that develops a two-dimensional map representing the surface of the object into a two-dimensional map, and a three-dimensional map that converts defects in the two-dimensional map into a three-dimensional map that matches the curved surface shape of the surface of the inspection object The surface inspection apparatus includes a unit and a defect detection unit that detects a size of a defect based on a three-dimensional map.
Here, the two-dimensional map creating unit creates the two-dimensional map by developing the extracted defects on a plane by a predetermined projection, correcting the position corresponding to the rotation angle, and superimposing the positions. You can do so.
In addition, the two-dimensional map creation unit can create a two-dimensional map by performing correction using a correction value that takes into account the frequency of occurrence of defects on the surface of the inspection object.
Further, a first noise removing unit for removing noise from the created two-dimensional map can be further provided.
Further, the two-dimensional map creating unit may further include a second noise removing unit that develops the extracted defect on a plane by the equal-area drawing and removes a defect having a number of pixels equal to or less than a predetermined number of pixels from the three-dimensional map as noise. Can be
Then, the three-dimensional map can indicate the position of the defect using a rectangular coordinate system.
In addition, the surface of the inspection object to be inspected has a hemispherical shape, and the defect detection unit changes the size of a defect serving as a reference for inspection based on latitude and / or longitude set on the hemisphere. Can be
Furthermore, the defect detection unit can extract a defect by creating an average image of the plurality of images acquired by the photographing unit and calculating a difference between each of the plurality of images and the average image.

  Further, according to the present invention, there is provided an illumination unit that irradiates light to an inspected object having a curved surface, a rotating unit that rotates the inspected object around a predetermined axis, and a rotating unit that rotates the inspected object. By photographing the subject from a direction that intersects the direction of the axis while taking it, a photographing unit that acquires a plurality of images having different rotation angles, and a photographing unit based on the images acquired by the photographing unit. Detecting means for detecting a defect present on the surface, the detecting means comprising: a defect extracting unit for extracting a defect existing in the image; A two-dimensional map creating unit that develops a two-dimensional map, a three-dimensional map creating unit that converts a defect in the two-dimensional map into a three-dimensional map that matches the curved shape of the surface of the inspection object, and a three-dimensional map based on the three-dimensional map. Defect detection to detect the size of defects When a surface inspection apparatus comprising a.

Here, the lighting unit has a mask that forms a striped pattern formed by arranging a plurality of strips and blocks a part of the light emitted from the lighting unit, and the mask combines the strip shapes at a predetermined angle. A stripe pattern formed by arranging the V-shape can be formed.
The illuminating means may include a reflection surface that reflects light emitted from the light emitting source and irradiates the reflected light to the inspection object, and the mask may be arranged on the reflection surface.
Further, the lighting means may emit light along the curved surface.
Still further, the illuminating means may further include a backlight provided on the opposite side to the photographing means with the object to be inspected therebetween, and irradiating light from behind the object to be inspected.
Then, the photographing means can photograph by blurring the stripe pattern of the mask projected on the inspection object.
Further, the imaging unit can focus on the object to be inspected so as to blur the stripe pattern of the mask for imaging.

  Further, the present invention includes a manufacturing process for manufacturing a component having a hemispherical shape at least in part, and an inspection process for inspecting a hemispherical surface of the component, wherein the inspection process includes applying light to the hemispherical surface. An image acquisition step of irradiating and rotating a part about a predetermined axis, and acquiring a plurality of images having different rotation angles by an imaging means for imaging a hemispherical surface from a direction intersecting the axis direction. A defect extraction step of extracting defects present in the image; a two-dimensional map creation step of expanding the extracted defects into a two-dimensional map representing a hemispherical surface as a plane; At least one hemispherical shape having a three-dimensional map creating step of converting the defect into a three-dimensional map adapted to the surface of the hemispherical shape, and a defect detecting step of detecting the size of the defect based on the three-dimensional map. Part It is a method of manufacturing the parts for.

  Still further, according to the present invention, a computer irradiates a computer with an object having a curved surface to illuminate the computer, rotates the object around a predetermined axis, and obtains the object from a direction intersecting the axis. An image acquisition function for acquiring a plurality of images having different rotation angles from a photographing means for photographing an inspection object, a defect extraction function for extracting a defect existing in the acquired image, and an extracted defect are inspected. A two-dimensional map creation function that develops a two-dimensional map representing the surface of the body into a two-dimensional map, and a three-dimensional map that converts defects in the two-dimensional map into a three-dimensional map that matches the curved surface shape of the surface of the inspection object This is a program for realizing a creation function and a defect detection function for detecting the size of a defect based on a three-dimensional map.

  According to the present invention, there is provided a surface inspection apparatus or the like in which the size of a defect can be more easily grasped as compared with a case where the size of a defect is measured in a plane from one direction for a test object having a curved surface. Can be provided.

It is a figure showing the schematic structure of the surface inspection device concerning a 1st embodiment. (A)-(b) is a figure showing the external appearance shape of the work. FIG. 2A is a schematic diagram when a defect generated on the surface of a hemispherical portion is viewed from the IIb direction in FIG. 2A. (B) shows an example in which the hemisphere is illuminated. (A)-(b) is a figure which showed in more detail about the dome illumination of this Embodiment. FIG. 4 is a diagram illustrating a difference between a distortion generated in a hemispherical portion of a work and a defect that causes a problem. FIG. 3 is a block diagram illustrating a functional configuration example of a control unit. (A)-(b) is a figure explaining the method in which a defect extraction part detects a contour and finds a center on a picture of a hemisphere part of a work from this. (A)-(c) is a figure which showed the method of calculating | requiring the difference of each of several images and an average image. (A)-(b) is a conceptual diagram showing a case where a two-dimensional map creation unit develops a defect on a plane. (C) is a diagram showing coordinates on a sphere used for creating a development view. FIG. 4 is a diagram illustrating an example of a two-dimensional map created by a two-dimensional map creating unit. (A) is a figure showing expansion processing. (B) is a figure showing contraction processing. (A)-(b) is the figure by which the 1st noise removal part compared before and after removing noise. FIGS. 7A and 7B are diagrams illustrating a method in which a three-dimensional map creating unit converts a defect on a two-dimensional map after noise removal by a first noise removing unit into a three-dimensional map. It is a figure showing the schematic structure of the surface inspection device concerning a 2nd embodiment. The case where the hemispherical portion of the work is illuminated by ring illumination is shown. It is a flowchart explaining the operation of the surface inspection device. (A)-(b) is a figure which compared the development view by the Sanson projection and the development view by the equirectangular projection.

  Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. However, the present invention is not limited to only these embodiments.

<Description of configuration of surface inspection device>
<< 1st Embodiment >>
Here, a first embodiment of the configuration of the surface inspection apparatus will be described first.
FIG. 1 is a diagram showing a schematic configuration of a surface inspection apparatus 1 according to the first embodiment.
The illustrated surface inspection device 1 performs overall control of the illumination 10 that illuminates the work W, a rotation mechanism 20 that rotates the work W, a camera 30 that photographs the surface of the work W, and the entire surface inspection device 1. And a control unit 40.

Here, the work W will be described first. The work W is an example of an object to be inspected having a curved surface.
FIGS. 2A and 2B are diagrams illustrating the external shape of the work W. FIG.
2 (a) is a front view of the work W, and FIG. 2 (b) is a top view of the work W as viewed from the IIb direction in FIG. 2 (a).
As illustrated in FIG. 2A, the workpiece W has an overall shape in which a hemispherical portion indicated by a hemispherical portion Wa and a cylindrical portion indicated by a cylindrical portion Wb are integrated. The surface of the work W has a property of reflecting light. That is, the work W is, for example, a part made of metal and having a polished surface. The work W is a component or the like whose surface is plated. In the present embodiment, the entire surface of the workpiece W is not inspected, but the hemispherical portion Wa is an inspection target, and the size of a defect generated on the surface of the hemispherical portion Wa is inspected. On the other hand, the surface of the cylindrical portion Wb is not an inspection target using the surface inspection device 1.

  Hereinafter, this hemisphere Wa is compared to the northern hemisphere of the earth, and the circumferential position along the horizontal direction in the figure is longitude, and the circumferential position along the vertical direction in the figure is latitude. May represent a location. In this case, the outer circumference circle at the bottom of the hemisphere Wa corresponds to the equator (latitude 0 °), and the top of the hemisphere Wa corresponds to the North Pole (latitude 90 °).

FIG. 3A is a schematic diagram when the defect K generated on the surface of the hemispherical portion Wa is viewed from the IIb direction in FIG. 2A.
The figure shows a case where a defect having a shape of “◇” is present on the surface of the hemispherical portion Wa as the defect K. Here, a description will be given by taking as an example a case where the defect K includes a defect K1, a defect K2, and a defect K3.
The defect K1 is located at the top of the hemisphere Wa. Further, the defects K2 and K3 are located on the sides other than the top of the hemisphere Wa, and the defect K3 is located farther from the top than the defect K2. In this case, the defect K1 is viewed from directly above, and its shape and size can be almost accurately determined. On the other hand, the defect K2 and the defect K3 are viewed from an oblique direction, and their shapes and sizes look different from the actual shapes and sizes. This is more pronounced for defect K3 than for defect K2. In other words, as the distance from the top of the hemispherical portion Wa increases, the shape and size of the defect K become curved and become different from the actual one. In other words, the lower the latitude where the defect K exists, the more the shape and size of the defect K are curved, and the defect K looks different from the actual one. As a result, in the case of the defect K2 and the defect K3, it is difficult to accurately detect the size.

FIG. 3B shows an example in which the hemisphere Wa is illuminated.
Here, an example is shown in which illumination is performed by a rectangular illumination S from above in the drawing. In this case, a bright portion that can illuminate the hemispherical portion Wa more brightly within the range of the area Ar, but other portions become darker and darker with the illumination S. In a dark part, it is difficult to detect the defect K. That is, in this example, the illumination does not brighten the whole, and the range in which the defect K can be detected is also limited. In addition, in the case of illumination for brightening a wide area, the inclination of a defect or the like as a defect K is unlikely to be a shadow, and as a result, the defect K is difficult to see and the defect K may be difficult to detect. On the other hand, when a narrow range as shown in FIG. 3B is illuminated, the inclination of a flaw or the like can be detected as a shadow, and the defect K can be easily seen. However, as described above, the range in which the defect K can be detected is limited.
As described above, when the surface of the work W has a curved surface shape such as a hemispherical portion Wa, and it is desired to detect the defect K on the curved surface, the size of the defect K can be detected from only one direction, or from one direction. It is difficult to detect the defect K only by performing illumination.

  On the other hand, there is also a method of detecting the defect K by photographing the work W from an oblique direction while rotating the work W. However, when photographing from an oblique direction, it is difficult to determine the position of the defect K. Further, when the detection results are accumulated, small noises also accumulate and become large, and the noises are likely to be erroneously detected as the defect K.

  Therefore, in the present embodiment, the surface inspection apparatus 1 of FIG. 1 is used to suppress these problems. Hereinafter, the surface inspection apparatus 1 of the present embodiment will be described.

The illumination 10 is an example of an illumination unit and irradiates the work W with light.
In the present embodiment, the illumination 10 emits light along a curved surface of the surface of the work W. Thus, the illumination 10 can be an illumination that illuminates the work W brightly as a whole. In this case, the illumination 10 includes a dome illumination 10a that irradiates light so as to cover the work W. That is, as the illumination 10, a hemispherical dome illumination 10 a having a shape along the hemispherical surface of the hemispherical portion Wa of the work W is employed. With the dome illumination 10a, light can be emitted from above the work W.

FIGS. 4A and 4B are diagrams showing the dome illumination 10a of the present embodiment in more detail.
4A is a view of the dome illumination 10a viewed from the same front direction as FIG. 1, and FIG. 4B is a view of the dome illumination 10a viewed from the IVb direction of FIG. 4A. FIG.
As shown in the figure, the dome lighting 10a has a white LED (Light Emitting Diode) 12 as a light emitting source arranged in an annular portion on the bottom surface of the dome 11. However, the emission color is not limited to white, and may be another color. Light emitted from the LED 12 is diffusely reflected on the inner surface 11a of the dome 11, and irradiates the hemispherical portion Wa of the work W. In the figure, light that is diffusely reflected is schematically represented by an arrow L. That is, the inner surface 11a of the dome 11 can be regarded as a reflection surface that reflects light emitted from the LED 12 and irradiates the reflected light to the work W. By using the dome illumination 10a, it is possible to brightly illuminate a wide area of the surface of the hemispherical portion Wa of the work W.

Further, as shown in FIG. 4B, in the present embodiment, the dome lighting 10 a includes a mask 13 on the inner surface 11 a of the dome 11. The mask 13 forms a striped pattern Sm formed by arranging a plurality of strips Ta, and blocks a part of light emitted from the LED 12 of the dome illumination 10a.
The striped pattern Sm is formed by arranging a V-shape Vj obtained by combining two strip shapes Ta1 and Ta2 at a predetermined angle. That is, in the illustrated example, each of the strip shapes Ta is inclined so as to form a predetermined angle from the reference line Ls. This angle is, for example, 30 °. The striped pattern Sm is formed on the entire inner surface 11a except for the hole 11b formed in the dome 11 to secure the field of view of the camera 30.

  When the mask 13 is formed with such a striped pattern Sm, the pattern of the striped pattern Sm is projected on the surface of the hemispherical portion Wa of the work W. In other words, bright and dark stripes alternate in the hemisphere Wa of the workpiece W. Thereby, the inclination of a flaw or the like is easily detected as a shadow, and the defect K is more easily seen. If there is a defect K at the boundary between the bright part and the dark part, it becomes more noticeable.

  Then, by forming the striped pattern Sm by arranging V-shaped shapes Vj which are combined at a predetermined angle, defects K such as scratches generated in various directions can be seen. That is, when the stripe pattern Sm is formed in only one direction, depending on the direction of the defect K such as a flaw, the inclination of the flaw or the like is unlikely to be a shadow, and the defect K may not be detected. In order to suppress this, it is conceivable to perform measurement by exchanging a plurality of domes 11 on which the masks 13 having different directions of the striped pattern Sm are formed, but it is troublesome to perform the exchange, and measurement is performed many times. There is a need. In the mask 13 of the present embodiment, since the stripe pattern Sm is formed in a V-shape Vj and is oriented in two directions, the defect K such as a flaw or the like generated in various directions in one measurement is obtained by one measurement. Can be seen.

  In the present embodiment, the illumination 10 further includes a backlight 10b provided on the opposite side to the camera 30 with the work W interposed therebetween, and irradiating light from behind the work W. Light can be emitted from below the work W by the backlight 10b. As described later in detail, when the camera 30 captures an image of the hemispherical portion Wa of the work W, the outline thereof can be easily identified. The backlight 10b includes, for example, a light emitting source in which a plurality of LEDs are arranged, but is not limited thereto. A fluorescent lamp, an incandescent lamp, a liquid crystal panel, an organic EL (OLED: Organic Light Emitting Diode) panel, an inorganic EL ( An IEL (Inorganic Electro-Luminescence) panel or the like may be used as the light emitting source. Further, the LED 12 of the dome lighting 10a described above may be used as these light emitting sources.

  Referring back to FIG. 1, the rotation mechanism 20 is an example of a rotation unit, and rotates the work W about a predetermined axis. The rotation mechanism 20 has a holding unit (not shown) that holds the work W. The rotation mechanism 20 includes, for example, a motor as a rotation power source, and rotates the work W by rotating the holding unit using the rotation power source. At this time, the direction of the rotation axis of the rotation mechanism 20 is made to coincide with the direction of the axis of the work W.

The camera 30 is an example of a photographing unit, and photographs the work W from a direction intersecting the axis direction while rotating the work W by the rotation mechanism 20. Thus, a plurality of images of the work W having different rotation angles are obtained. The angle of the intersecting directions is, for example, 40 ° as shown in FIG. This angle can also be viewed as 50 ° with respect to the horizontal plane. The camera 30 captures an image of the hemisphere Wa of the work W through the hole 11b of the dome 11 of the lighting 10. In this case, the hole 11b is formed at the top of the dome 11, and the camera 30 captures an image of the hemisphere Wa of the work W from the vertical direction.
Further, the camera 30 captures an image of the work W at each predetermined rotation angle. The rotation angle is, for example, 6 °. In this case, when the work W makes one rotation (360 ° rotation), a total of 60 shot images are obtained.

  At this time, the camera 30 blurs and shoots the striped pattern Sm of the mask 13 projected on the workpiece W. Accordingly, it is possible to prevent the distortion generated in the hemispherical portion Wa of the workpiece W from being photographed.

FIG. 5 is a diagram showing a difference between the distortion Ug generated in the hemispherical portion Wa of the workpiece W and the defect K which is a problem.
In FIG. 5, the distortion Ug is a surface in which a part of the hemisphere is slightly deviated from the hemisphere, for example, a surface in which a part of the hemisphere becomes flat, a curvature slightly different from the curvature of the hemisphere. , And a gently concave surface. In this case, the distortion Ug deviates from the ideal hemisphere, but there is no problem even if it is present. On the other hand, the defect K is an abrasion, a dent, an indentation, or the like, and has, for example, a groove shape that is angled with respect to a hemisphere. When such a defect K has a size equal to or larger than a predetermined size, the function of the work W is impaired, and the product is likely to be defective.

  Actually, the camera 30 focuses on the work W to blur and photograph the striped pattern Sm of the mask 13. That is, when the position of the mask 13 is focused, the stripe pattern Sm of the mask 13 is not blurred. Therefore, it is necessary to focus on a part other than this part. Then, by focusing on the surface of the hemispherical portion Wa of the workpiece W, the focus is on the location where the defect K exists, and the defect K becomes more visible.

Returning to FIG. The control unit 40 is an example of a detection unit, controls the entire surface inspection apparatus 1, and detects a defect K existing on the surface of the work W based on an image acquired by the camera 30.
FIG. 6 is a block diagram illustrating an example of a functional configuration of the control unit 40.
As illustrated, the control unit 40 performs a defect extraction unit 41 that extracts a defect K of the work W, a two-dimensional map creation unit 42 that creates a two-dimensional map of the defect K, and a noise removal on the two-dimensional map. A first noise removing unit 43 for performing a three-dimensional map of the defect K; a second noise removing unit 45 for removing noise on the three-dimensional map; And a defect detection unit 46 for detecting the defect.

The defect extraction unit 41 extracts a defect K existing in an image captured by the camera 30.
At this time, first, the defect extraction unit 41 performs positioning of each image using the outline of the work W. More specifically, the defect extracting unit 41 obtains the position of the center from the contour of the work W, and performs positioning by adjusting the position of the center. This contour can be detected by the light emitted from the backlight 10b.

FIGS. 7A and 7B are diagrams illustrating a method in which the defect extracting unit 41 detects a contour and obtains a center of the hemispherical portion Wa of the workpiece W on the image from the contour.
Among them, FIG. 7A shows a case where the contour Rk of the hemispherical portion Wa of the work W is detected for one image G among the plurality of captured images G. In this case, since the hemispherical portion Wa and the background can be clearly distinguished by the light emitted from the backlight 10b above the image G, the boundary is detected and set as the contour Rk. Then, as described below, a defect K is extracted after performing alignment of a plurality of images G using the detected contour Rk.

FIG. 7B shows a case where the center of the image of the hemispherical portion Wa of the workpiece W is obtained from the detected contour Rk. In this case, since the contour Rk is considered to be a part of a circle, the contour Rk is extended below the contour Rk to create a circle. The figure shows a case where a circle is created by the contour Rk and the extension En. Then, the coordinates of the center O of the circle are obtained. In the present embodiment, the coordinates of a pixel are pixel coordinates represented by the number of pixels.
Then, the defect extraction unit 41 performs the same processing for each of the plurality of captured images G to obtain the coordinates of the center O. Further, by moving each image G in the left, right, up, and down directions in the figure, the center O is aligned with the same coordinates, and alignment is performed.

  Next, the defect extraction unit 41 creates an average image Gh of the plurality of images G acquired by the camera 30. Here, the average image Gh is an image in which the pixel values of the pixels at the same coordinates are averaged for a plurality of images G after the alignment, and the average value is used as the pixel value. In this case, the plurality of images G are gray images. Then, the pixel value is represented by, for example, 8 bits, and is any integer value of 0 or more and 255 or less. The defect extraction unit 41 calculates the average value of the pixel values by summing up the pixel values of the pixels at the same coordinates in the image G and dividing the summed value by the number of images G. The number of images G is 60 in the case described above. Then, an image composed of pixels having the average pixel value of the 60 images G is defined as one average image Gh. At this time, the average image Gh can be regarded as an image when there is no defect K. That is, the defect K is photographed at a different position in each image G in order to photograph while rotating the work W. The defect K does not appear at a high frequency, and the number thereof is small. When there is a defect K, the pixel value of the image G changes as compared with the case where there is no defect K. However, in the case of the average image Gh, the change is evened out, and the pixel value is almost the same as when there is no defect. Become.

Then, the defect extracting unit 41 extracts the defect K by calculating a difference between each of the plurality of images G after the alignment and the average image Gh.
FIGS. 8A to 8C are diagrams illustrating a method of calculating a difference between each of the plurality of images G and the average image Gh.
FIG. 8A is a diagram showing the average image Gh. Then, a difference between the average image Gh and one image G among the plurality of captured images G is obtained. As described above, the difference is obtained for the pixels located at the same coordinates in each image G by performing the alignment of the center O.

  FIG. 8B is a diagram illustrating an image having a difference pixel value as a difference image Gs. In the example described above, 60 difference images Gs are created. In this case, since the difference may be a negative number, the pixel value of the difference may be not only a positive number and 0 but also a negative number. In the difference image Gs, the absolute value of the pixel value at the position where the defect K exists is large. That is, at a portion where no defect K exists, the pixel value is close to the pixel value of the average image Gh, and thus becomes 0 or close to 0. On the other hand, at the position where the defect K exists, the pixel value does not come close to the pixel value of the average image Gh, and thus becomes a larger positive number or a smaller negative number. Therefore, it is considered that the location where the defect K exists has an absolute value equal to or greater than the predetermined threshold. In this case, for example, when the threshold value is 30 and the absolute value of the pixel value of the difference is 30 or more as the location where the defect K exists, it is determined as the defect K.

  Further, the defect extracting unit 41 obtains an edge image of the defect K by obtaining a high-frequency component from the difference image Gs. In the case of the distortion Ug described above, the absolute value of the pixel value of the edge becomes small. On the other hand, in the case of the defect K such as a scratch, the absolute value of the pixel value of the edge becomes large. Therefore, it is considered that the absolute value of the obtained pixel value of the edge is greater than or equal to the predetermined threshold value at the location where the defect K exists. In this case, the threshold is set to 25, and when the absolute value of the pixel value of the obtained edge is 25 or more, it is determined to be a defect K. That is, the defect extraction unit 41 does not treat the edge K of the defect K extracted by calculating the difference as a defect K when the pixel value of the edge of the defect K is out of a predetermined range. Thereby, the distortion Ug can be further eliminated.

FIG. 8C shows an extracted image Gc which is an image of the defect K extracted by the defect extracting unit 41 as a result of these processes. Then, the defect extraction unit 41 extracts the defect K for each difference image Gs. This image is a binary image, and the location of the extracted defect K is represented by a pixel value of 1 and the location of the extracted defect K is represented by a pixel value of 0. When the number of difference images Gs is 60, the number of extracted images Gc is also 60.
In the present embodiment, the backlight 10b is provided in the illumination 10 as described above. Then, the defect extraction unit 41 detects the contour Rk of the hemispherical portion Wa of the work W from the light emitted by the backlight 10b, performs alignment of a plurality of images G based on the detected contour Rk, and then detects the defect K. Is extracted.

The two-dimensional map creation unit 42 develops the defect K extracted by the defect extraction unit 41 into a two-dimensional map Mp that represents the surface of the work W by a plane. In this case, the two-dimensional map creation unit 42 first develops the defect K extracted by the defect extraction unit 41 on a plane by a predetermined projection.
FIGS. 9A and 9B are conceptual diagrams showing a case where the two-dimensional map creating unit 42 develops the defect K on a plane.
9 (a) is the same as FIG. 8 (c), and shows the extracted image Gc. FIG. 9B shows a case where the defect K in the extracted image Gc shown in FIG. 9A is developed on a plane by a predetermined projection.
Here, the two-dimensional map creation unit 42 uses the equal-area projection as the projection. The equal-area projection is a projection in which a small area on a sphere and a corresponding small area on a plane have a certain proportional relationship. In the present embodiment, the Sanson projection is used as the equal-area projection. In the Sanson projection, all parallels that are parallel straight lines (lines representing latitude) and equidistant distances that are correctly expressed along the central meridian of a line perpendicular to the parallel lines are used. This is an equal-area projection that provides a sine curve.
In this case, the defect K shown in FIG. 9A is developed on the plane shown in FIG. 9B. In this case, the defect K on the hemisphere Wa is converted into coordinates on a plane and plotted. Hereinafter, as shown in FIG. 9B, the plane on which the defect K is developed may be referred to as a “development view Tn”. In this developed view Tn, the lower left vertex of the rectangle has a longitude and a latitude of 0 °, and the longitude becomes larger as going to the right, and is 360 ° -longitude resolution or less at the rightmost. In addition, in the figure, it is described as 360 degrees for easy understanding of the description (the same applies to the following drawings). The latitude increases as it goes upward, and is 90 ° at the top. Further, the developed view Tn is a binary image, and the pixel value of the developed defect K is 1 and the pixel value of the non-defect K is 0.

Next, a specific method of creating the development view Tn will be described.
FIG. 9C is a diagram showing coordinates on a sphere used for creating the development view Tn.
First, the two-dimensional map creation unit 42 selects one pixel from the pixels constituting the defect K of one extracted image Gc illustrated in FIG. 9A. Then, the amount of deviation of the spherical center coordinates (center coordinates of the hemispherical portion Wa) of this image from the spherical center coordinates of the reference image is considered. This reference image is, for example, the image shown in FIG. 9A created based on the image G shot on the first image. Assuming that the coordinates of this pixel are (Row, Col) and the amount of displacement is (Row _offset , Col _offset ), the coordinates (X, Y, Z) of this pixel on the sphere can be expressed by the following equation (1). it can. In the equation 1, R is the radius of the sphere expressed by the number of pixels.

  Next, the two-dimensional map creation unit 42 corrects the angle of the work W when the work W is photographed. The corrected coordinates (A, B, C) of the pixel on the sphere can be expressed by the following equation (2). In Expression 2, θ corresponds to the angle of the work W with respect to the horizontal plane when the work W is photographed, and is 50 ° in the above-described example.

  Then, the two-dimensional map creation unit 42 converts the corrected coordinates (A, B, C) on the sphere into latitude and longitude coordinates (φ, λ). The coordinates of latitude and longitude (φ, λ) can be expressed by the following equation (3).

Further, the two-dimensional map creation unit 42 matches the coordinates of the latitude and longitude (φ, λ) with the number of pixels in the horizontal direction and the vertical direction of the developed view Tn. As a result, the latitude and longitude coordinates (Row _Dev , Col _Dev ) in the developed view Tn can be expressed by the following equation (4). In Expression 4, Height _Dev is the number of pixels in the vertical direction of the development view Tn. The Scale _Dev is the longitude resolution (rad / pixel) of the developed view Tn. Thereby, one pixel can be developed on the development diagram Tn. Note that the pixel value of the defect K after development in the development diagram Tn is 1, and the pixel value of a portion without the defect K is 0. That is, the developed view Tn is a binary image.

  Then, the two-dimensional map creator 42 performs the same processing for the other pixels constituting the defect K, and a single development Tn as shown in FIG. 9B is completed. Thereafter, the same processing is performed for the other extracted images Gc. When the number of the extracted images Gc is 60, the developed view Tn is 60.

  As shown in FIG. 9B, in this case, the horizontal direction in the developed view Tn represents the longitude of the hemispherical portion Wa, which is a hemispherical surface, and the vertical direction represents the latitude. The longitude is not less than 0 ° and not more than 360 ° -longitude resolution. And the latitude is 0 ° or more and 90 ° or less. The resolution of the developed view Tn is determined by determining the angular resolution for each latitude and longitude. For example, the latitude may be 90 ° / latitude resolution, and the longitude may be 360 ° / longitude resolution. Then, the defect K is plotted on the developed view Tn.

Then, the two-dimensional map creation unit 42 creates a two-dimensional map Mp by correcting the position of the defect K developed on the development view Tn by an amount corresponding to the rotation angle and superimposing the defect K. The defect K on the extracted image Gc is based on the image G captured while rotating the work W as described above. As a result, since each image G has a deviation in the longitude direction within a range of 0 ° to 360 ° -longitude resolution or less, it is necessary to correct the position in the longitude direction according to the rotation angle. In the correction, the first image G is set to the position of 0 °, and the rotation angle is obtained and corrected based on this position. After the correction, the pixel values of the pixels at the same coordinates are all added up. At this time, if the development map Tn is 60, the 60 maps are to be added, and as a result, one 2-dimensional map Mp is created. As described above, in the development view Tn, the pixel value of the pixel at the position of the defect K is 1, and the pixel value of the pixel at a position other than the defect K is 0. Therefore, when the development diagram Tn is 60, the pixel value of each pixel constituting the two-dimensional map Mp is 0 or more and 60 or less.
FIG. 10 shows an example of the two-dimensional map Mp created by the two-dimensional map creating unit 42 by the above processing.

  Furthermore, in the present embodiment, it is preferable that the two-dimensional map creator 42 creates the two-dimensional map Mp by correcting the correction value in consideration of the frequency of occurrence of the defect K in the measurement range on the surface of the workpiece W. . That is, when the workpiece W is rotated, the measurement range is the narrowest at the equator of the hemisphere Wa, and the measurement range becomes wider as the latitude increases. In addition, the vicinity of 90 ° latitude is included in the measurement range for substantially the entire circumference. Therefore, the appearance frequency of the same defect K in an image photographed while the work W makes one round is low near the equator of the hemisphere Wa, and increases as the latitude increases. As a result, the number of images of the defect K near the equator decreases, and the number of images increases as the latitude increases. That is, the appearance frequency of the defect K differs depending on the latitude. In this case, it is necessary to correct the appearance frequency because it affects the pixel value of the developed view Tn. Specifically, a coefficient corresponding to the number of meridians through which the defect K passes within the measurement range is set for each latitude, and the resultant value is multiplied by a pixel value obtained by summing the correction values.

The first noise removing unit 43 removes noise from the two-dimensional map Mp created by the two-dimensional map creating unit 42. The first noise removing unit 43 removes noise in the two-dimensional map Mp by, for example, two types of methods.
The first method is to remove noise by binarizing the two-dimensional map Mp and creating a binary image. In this case, a predetermined threshold value is prepared, and the pixel value of each pixel on the two-dimensional map Mp is set to 0 when the pixel value is equal to or less than the threshold value, and to 1 when the pixel value exceeds the threshold value. Create As a result, among the images captured by the camera 30, only a small number of the images are captured, so that the pixel values of the two-dimensional map Mp are reduced, and thus are removed as noise.

The second method is a method of removing noise by performing expansion processing and contraction processing on the two-dimensional map Mp.
FIG. 11A is a diagram illustrating the expansion processing.
In the expansion processing, one pixel in the two-dimensional map Mp is set as the target pixel Pc, and when the pixel value of the target pixel Pc is 0, there is at least one pixel having a pixel value of 1 in the vicinity of 8 around the target pixel Pc. , The pixel value is replaced from 0 to 1.
FIG. 11B illustrates the contraction process.
In the contraction processing, one pixel in the two-dimensional map Mp is set as the target pixel Pc, and when the pixel value of the target pixel Pc is 1, if there is at least one pixel having a pixel value 0 near 8 around the target pixel Pc. , The pixel value is replaced with 1 to 0.
By performing the expansion processing and the contraction processing in combination, noise can be removed. At this time, the expansion processing and the contraction processing are not limited to once, but may be two or more.

FIGS. 12A and 12B are diagrams comparing the noise before and after the noise is removed by the first noise removing unit 43.
Among them, FIG. 12A shows an image before removing noise, which is the above-described two-dimensional map Mp. FIG. 12B is a noise-removed two-dimensional map Mp ′ after removing noise from the two-dimensional map Mp.
In FIG. 12B, fine points disappear as compared with FIG. 12A, and noise is removed.
By removing the noise from the two-dimensional map Mp by the first noise removing unit 43, it is possible to suppress the problem that small noises are accumulated and increased, and the noise is likely to be erroneously detected as the defect K.

The three-dimensional map creation unit 44 converts the defect K in the two-dimensional map Mp into a three-dimensional map that matches the curved shape of the surface of the work W.
FIGS. 13A and 13B show a method in which the three-dimensional map creating unit 44 converts the defect K on the two-dimensional map Mp ′ after the first noise removing unit 43 removes noise into a three-dimensional map Ms. FIG.
Here, since the curved surface is a hemisphere, the coordinates of the defect K on the two-dimensional map Mp ′ after noise removal are converted so as to be located on the hemisphere. In this case, the defect K on the two-dimensional map Mp shown in FIG. 13A is plotted on the spherical surface shown in FIG.

Hereinafter, a specific method of creating the three-dimensional map Ms will be described.
First, the three-dimensional map creation unit 44 selects one of the pixels constituting the defect K on the two-dimensional map Mp. Then, the coordinates (φ, λ) of the latitude and longitude of the pixel on the two-dimensional map Mp are obtained. The coordinates of latitude and longitude (φ, λ) can be expressed by the following equation (5).

  Then, the three-dimensional map creating unit 44 converts the coordinates of the latitude and longitude (φ, λ) into the coordinates on the sphere (A, B, C). This conversion can be expressed by the following equation (6).

Thereby, one pixel can be developed on the three-dimensional map Ms. Note that the pixel value of the defect K after expansion in the three-dimensional map Ms is 1, and the pixel value of a portion without the defect K is 0. That is, the three-dimensional map Ms is a three-dimensional binary image.
Then, the three-dimensional map creator 44 performs the same processing for the other pixels constituting the defect K on the two-dimensional map Mp. As a result, the defect K is plotted on the three-dimensional map Ms, and one three-dimensional map Ms is completed. The three-dimensional map Ms indicates the position of the defect K using a rectangular coordinate system. Then, the three-dimensional map Ms represents the defect K by a pixel group whose position is determined in the orthogonal coordinate system.

  The second noise removing unit 45 removes noise from the three-dimensional map Ms. In the present embodiment, the second noise removing unit 45 removes, as noise, a defect K having a size equal to or smaller than a predetermined size from the defect K plotted on the three-dimensional map Ms.

The defect detector 46 detects the size of the defect K based on the three-dimensional map Ms from which noise has been removed by the second noise remover 45.
Specifically, the defect detection unit 46 performs labeling on the defect K after plotting and removing noise. Then, for the labeled defect K, the size of the defect K is obtained from the coordinates of the points forming the defect K. The size of the defect K is defined as the length when the width of the defect K is the largest. The direction for obtaining the width of the defect K may be not only the longitude direction and the latitude direction but also a direction intersecting these directions, that is, a so-called oblique direction.

  Then, the defect detection unit 46 calculates the number of defects K having a size equal to or larger than a predetermined size. If the number is equal to or larger than the predetermined number, it is determined that the inspection result indicates that the workpiece W is defective. On the other hand, when this number is less than the predetermined number, it is determined that the work W is a non-defective product as an inspection result.

  Further, the defect detection unit 46 may change the size of a defect serving as a reference for inspection based on the longitude and / or latitude set on the hemisphere Wa. For example, in a certain latitude range or longitude range, the number of defects K having a size of 50 μm or more is obtained. On the other hand, in other latitude ranges and longitude ranges, the number of defects K having a size of 100 μm or more is obtained.

<< 2nd Embodiment >>
Next, a second embodiment of the configuration of the surface inspection apparatus 1 will be described. In the second embodiment, the illumination 10 is changed from the first embodiment.
FIG. 14 is a diagram illustrating a schematic configuration of the surface inspection apparatus 1 according to the second embodiment.
The surface inspection apparatus 1 shown in the drawing has a lighting 10 for illuminating the work W, a rotation mechanism 20 for rotating the work W, and a camera for photographing the surface of the work W, similarly to the surface inspection apparatus 1 shown in FIG. 30 and a control unit 40 that controls the entire surface inspection apparatus 1.
However, the illustrated surface inspection apparatus 1 is different from the surface inspection apparatus 1 illustrated in FIG. 1 in that the illumination 10 uses a ring illumination 10c instead of the dome illumination 10a. Other parts have the same configuration and function.
The ring illumination 10c is a normal ring illumination used for inspection or the like, and has a ring shape (ring shape). An LED or the like, which is a light emitting source, is provided in the annular portion, and light can be emitted from above the work W. The striped pattern Sm as described in the first embodiment is not formed on the ring illumination 10c.
The ring illumination 10c also has an annular portion shaped along the hemisphere of the hemispherical portion Wa of the work W, and irradiates light along the curved surface of the work W. Therefore, the ring illumination 10c can be an illumination that illuminates the work W brightly in a wider range.

FIG. 15 shows a case where the hemispherical portion Wa of the workpiece W is illuminated by the ring illumination 10c.
In the ring illumination 10c, since the light-emitting sources are arranged in a ring shape, a bright portion that can illuminate the hemispherical portion Wa of the work W more brightly also has a ring shape. In the figure, a bright portion is indicated by an area Ar. The outline Rk of the hemispherical portion Wa of the work W can be clearly detected by the backlight 10b as in the first embodiment.
In this case, the defect K is extracted from the ring-shaped irradiation range. Note that, although the portion other than the area Ar is a dark portion, the defect K is easily detected at the boundary between the bright portion and the dark portion, and the defect K can be detected more accurately if the latitude to be detected is set as this portion.

  According to the surface inspection apparatus 1 described above, the defect K is plotted on the three-dimensional map Ms by the three-dimensional map creation unit 44. Thereby, the size and shape of the defect K are reproduced more accurately. Therefore, the size of the defect K can be grasped more accurately, and the measurement of the size of the defect K can be performed more accurately. In other words, the position of the defect K can be easily determined even by a method of photographing the work W from an oblique direction while rotating the work W.

  Further, this three-dimensional map Ms indicates the position of the defect K in the rectangular coordinate system. Then, the three-dimensional map Ms represents the defect K by a pixel group whose position is determined in the orthogonal coordinate system. Therefore, it is easier to calculate the size of the defect K. In this case, the size of the defect K not only in the latitude and longitude directions but also in a diagonal direction intersecting the latitude and longitude directions can be measured more accurately. Therefore, even if the surface of the workpiece W to be inspected has a curved surface shape, the size of the defect K can be measured more accurately.

  Further, according to the surface inspection apparatus 1 described above, the first noise removing unit 43 removes noise from the two-dimensional map Mp. Thus, it is possible to suppress the problem that small noises accumulate and increase, and the noises are likely to be erroneously detected as the defect K.

Further, according to the first embodiment, the dome illumination 10a is used as illumination. Thereby, it is possible to illuminate the surface of the hemispherical portion Wa of the work W in a wider range. In the present embodiment, while rotating the work W by the rotation mechanism 20, the work W is photographed from a direction intersecting the axial direction. Then, a plurality of images of the work W having different rotation angles are acquired. For this reason, the defect K is also photographed from a plurality of angles, and the defect K is easily detected even with illumination that brightens a wide area.
Further, by forming a mask 13 having a stripe pattern Sm on the inner surface 11a of the dome 11 of the dome illumination 10a, the inclination of a flaw or the like is easily detected as a shadow, and the defect K is more easily seen. By forming the striped pattern Sm in a V-shape Vj formed by combining them at a predetermined angle, defects K such as scratches generated in various directions can be seen.

  Then, according to the present embodiment, when the development view Tn and the two-dimensional map Mp are created by using the equal-area projection such as the Sanson projection, the area of the original image G is determined regardless of the latitude at which the defect K exists. The ratio is maintained. This means that if the area of the defect K is the same in the original image G, the number of pixels constituting the defect K in the developed view Tn and the two-dimensional map Mp does not change depending on the latitude. Therefore, even in the three-dimensional map Ms, the number of pixels constituting the defect K does not change regardless of the latitude. Therefore, the second noise removing unit 45 can perform a process of removing a defect K having a number of pixels equal to or less than a predetermined number of pixels from the defect K plotted on the three-dimensional map Ms as noise. As a result, the calculation time tends to be shorter than in other projections. Further, in the developed view Tn created by the Sanson projection illustrated in FIG. 9B, the total number of pixels tends to be smaller than other projections. As a result, the number of calculations required for coordinate transformation is smaller than in other projections, and the calculation time is shorter.

<Description of operation of surface inspection device>
Next, the operation of the surface inspection apparatus 1 will be described.
FIG. 16 is a flowchart illustrating the operation of the surface inspection apparatus 1.
First, the work W is set on the rotation mechanism 20, and light is emitted from the illumination 10 (step 101).
Next, the work W is rotated by operating the rotation mechanism 20, and the image is captured by the camera 30 at a predetermined rotation angle to obtain an image G (step 102). At this time, in the first embodiment, the camera 30 focuses on the workpiece W to blur and photograph the stripe pattern Sm of the mask 13 of the dome illumination 10a. The number of images G to be photographed is, for example, 60.
The image data of the captured image G is sent to the control unit 40, and the control unit 40 performs the following processing.

First, the defect extracting unit 41 detects the contour Rk of the hemispherical portion Wa of the workpiece W for one image G of the plurality of captured images G by the method described with reference to FIG. 103).
Next, the defect extraction unit 41 creates a circle by using the method described with reference to FIG. 7B together with the extension En that extends the contour Rk, and obtains the coordinates of the center O of the circle (step 104).
Then, after performing the processing of steps 103 to 104 for all the images G, the defect extraction unit 41 aligns the center O of the circle to the same coordinates and performs alignment (step 105).

Then, the defect extracting unit 41 creates an average image Gh as shown in FIG. 8A using the plurality of images G after the alignment (step 106).
Further, the defect extraction unit 41 calculates a difference between each of the plurality of images G and the average image Gh, and creates a difference image Gs as shown in FIG. 8B (step 107).
Then, when the absolute value of the pixel value and the absolute value of the pixel value of the edge of the difference image Gs are equal to or larger than a predetermined threshold value, the defect extracting unit 41 determines that the defect K is a defect K, and FIG. Then, an extracted image Gc as shown in FIG. 9A is created (step 108).

Next, the two-dimensional map creation unit 42 develops the defect K extracted by the defect extraction unit 41 on a plane using a predetermined projection. That is, the two-dimensional map creation unit 42 creates a developed view Tn as shown in FIG. 9B from the extracted image Gc created in step 108 (step 109). This projection is the Sanson projection.
Then, the two-dimensional map creator 42 creates a two-dimensional map Mp of the defect K by correcting the position of the defect K developed on the development view Tn by correcting the position corresponding to the rotation angle (step S1). 110). At this time, it is preferable to perform the above-described appearance frequency correction. The created two-dimensional map Mp is, for example, as shown in FIG.

Next, the first noise removing unit 43 removes noise by binarizing the two-dimensional map Mp to create a binary image (step 111).
Further, the first noise removing unit 43 removes noise by performing expansion processing and contraction processing on the two-dimensional map Mp (step 112).

Next, the three-dimensional map creation unit 44 creates a three-dimensional map Ms that matches the defect K in the two-dimensional map Mp with the spherical shape of the surface of the work W (step 113). The created three-dimensional map Ms is, for example, as shown in FIG.
Then, the second noise removing unit 45 removes noise from the three-dimensional map Ms (Step 114). At this time, the second noise removing unit 45 removes, as noise, a defect K having a number of pixels equal to or less than a predetermined number of pixels from the defect K plotted on the three-dimensional map Ms.

The defect detection unit 46 detects the size of the defect K based on the three-dimensional map Ms from which noise has been removed by the second noise removal unit 45 (step 115).
Then, the defect detection unit 46 calculates the number of defects K having a size equal to or larger than a predetermined size (step 116).
Further, the defect detection unit 46 determines whether or not this number is equal to or greater than a predetermined number (step 117).
As a result, if the number is equal to or larger than the predetermined number (Yes in step 117), it is determined that the inspection result indicates that the workpiece W is defective (step 118). On the other hand, when the number is less than the predetermined number (No in step 117), it is determined that the work W is a non-defective product as an inspection result (step 119).

  Note that the above-described method can be considered as a method of manufacturing the work W as a part having a hemispherical shape at least in part. The method of manufacturing the work W includes a manufacturing step of manufacturing the work W having at least a part of a hemispherical shape, and an inspection step of inspecting the hemispherical surface of the work W. Then, the inspection step irradiates light onto the hemispherical surface, and rotates the work W about a predetermined axis, while photographing the hemispherical surface from a direction intersecting the direction of the axis. An image acquiring step of acquiring a plurality of images having different rotation angles, a defect extracting step of extracting a defect present in the image, and a two-dimensional map representing the extracted defect with a hemispherical surface as a plane. A two-dimensional map creating step for developing the defect, a three-dimensional map creating step for converting a defect in the two-dimensional map into a three-dimensional map adapted to a hemispherical surface, and a defect size based on the three-dimensional map. And a defect detection step of detecting

In the first embodiment, the striped pattern Sm of the mask 13 is blurred in order to prevent the distortion Ug from being detected. However, the inspection of the workpiece W may require the detection of the distortion Ug. Can occur. In this case, the distortion Ug can be detected by focusing and photographing the striped pattern Sm without blurring. Further, in this case, it is considered that the boundary of the striped pattern Sm becomes gradation by blurring. Therefore, in order to prevent the distortion Ug from being detected, the outer edge of the striped pattern Sm of the mask 13 may be set to a gradation pattern in addition to the method of blurring and photographing the striped pattern Sm of the mask 13 described above. . In this case, the mask 13 has a gradation in which the center is, for example, black in the short side direction of the strip shape Ta, and the outer edge is brighter in the direction from the center to the outer edge. In this case, instead of blurring and photographing the striped pattern Sm, the photographing is performed by focusing on the striped pattern Sm of the mask 13.
Further, the pattern of the mask 13 is the striped pattern Sm, but is not limited to this, and may be, for example, a polka dot pattern, a checkerboard (checkered pattern), or the like. However, in this case, the distortion Ug tends to be more easily detected, and the striped pattern Sm is more preferable from the viewpoint that it is difficult to detect the distortion Ug.
Furthermore, in the above-described embodiment, the surface of the hemispherical portion Wa to be inspected of the workpiece W to be inspected has a hemispherical surface. However, the present invention is not limited to this. If there is, the surface inspection apparatus 1 of the present embodiment can be applied. Therefore, for example, the surface of the columnar portion Wb of the work W can be inspected using the surface inspection device 1.

Further, in the above-described embodiment, the defect K is developed on a plane by the equal-area projection such as the Sanson projection, and the development view Tn is created. However, the projection is not limited to this, and for example, the equirectangular projection is used. The expansion method used may be used.
FIGS. 17A and 17B are diagrams comparing a developed view Tn by the Sanson projection and a developed view Tn by the equirectangular projection.
Among them, FIG. 17A is a developed view Tn by the Sanson projection, and FIG. 17B is a developed view Tn by the equirectangular projection. As described above, in the Sanson projection, the meridians are sinusoidal. The latitude line is a straight line. On the other hand, in the equirectangular projection, the parallels and meridians are both straight lines and intersect at right angles and at equal intervals.

<Description of the program>
Here, the processing performed by the control unit 40 in the present embodiment described above is prepared, for example, as a program such as application software. This processing is realized by cooperation between software and hardware resources. That is, a CPU (not shown) in the computer provided in the control unit 40 executes a program for realizing each of the above-described functions, and realizes each of these functions.

  Therefore, in the present embodiment, the processing performed by the control unit 40 is as follows. The computer irradiates the computer with light on the work W, which is a test object having a curved surface, and rotates the work W about a predetermined axis. The camera 30 that captures the workpiece W from a direction that intersects the direction of the axis while capturing a plurality of images G having different rotation angles and a defect K existing in the captured image G are extracted. A defect extraction function, a two-dimensional map creation function for expanding the extracted defect K into a two-dimensional map Mp representing the surface of the work W in a plane, and a defect K in the two-dimensional map Mp A three-dimensional map creation function for converting to a three-dimensional map Ms conforming to the curved surface shape of the object and a defect detection function for detecting the size of the defect K based on the three-dimensional map Ms. It can be.

  It should be noted that the program for realizing the present embodiment can be provided not only by communication means but also stored in a recording medium such as a CD-ROM.

  Although the embodiment has been described above, the technical scope of the invention is not limited to the scope described in the embodiment. It is apparent from the description of the claims that various modifications or improvements made to the above embodiment are included in the technical scope of the present invention.

DESCRIPTION OF SYMBOLS 1 ... Surface inspection apparatus, 10 ... Lighting, 10a ... Dome lighting, 10b ... Backlight, 20 ... Rotation mechanism, 30 ... Camera, 40 ... Control part, 41 ... Defect extraction part, 42 ... Two-dimensional map creation part, 43 ... 1st noise removing section, 44: three-dimensional map creating section, 45: second noise removing section, 46: defect detecting section, G: image, Gh: average image, K: defect, Rk: contour, Mp: 2 Dimensional map, Ms: 3D map, W: Work, Wa: Hemisphere

Claims (17)

An imaging unit configured to irradiate a light on a test object having a curved surface and to rotate the test object about a predetermined axis while photographing the test object from a direction intersecting the direction of the axis; An image acquisition unit that acquires a plurality of images having different rotation angles from
A defect extraction unit for extracting a defect present in the image,
A two-dimensional map creating unit that develops the extracted defect into a two-dimensional map representing the surface of the inspection object by a plane,
A three-dimensional map creating unit that converts a defect in the two-dimensional map into a three-dimensional map adapted to a curved shape of the surface of the inspection object;
A defect detection unit that detects the size of the defect based on the three-dimensional map;
A surface inspection device comprising:   The two-dimensional map creating unit creates the two-dimensional map by developing the extracted defect on a plane by a predetermined projection, correcting the position corresponding to the rotation angle, and superimposing the position. The surface inspection apparatus according to claim 1, wherein:   3. The surface inspection according to claim 2, wherein the two-dimensional map creating unit creates the two-dimensional map by performing correction using a correction value that takes into account the frequency of occurrence of defects on the surface of the inspection object. apparatus.   The surface inspection apparatus according to any one of claims 1 to 3, further comprising a first noise removing unit that removes noise from the created two-dimensional map. The two-dimensional map creating unit develops the extracted defect on a plane by the equal-area drawing method,
The surface inspection apparatus according to any one of claims 1 to 4, further comprising a second noise removing unit that removes a defect having a number of pixels equal to or less than a predetermined number from the three-dimensional map as noise.   The surface inspection apparatus according to any one of claims 1 to 5, wherein the three-dimensional map indicates a position of a defect using a rectangular coordinate system. The object to be inspected has a hemispherical surface to be inspected,
The surface inspection according to any one of claims 1 to 6, wherein the defect detection unit changes the size of a defect serving as a reference for inspection based on latitude and / or longitude set on a hemisphere. apparatus.   The defect detection unit may extract a defect by creating an average image of the plurality of images acquired by the imaging unit, and calculating a difference between each of the plurality of images and the average image. The surface inspection apparatus according to any one of claims 1 to 7, wherein Illumination means for irradiating light to the inspection object having a curved surface,
Rotating means for rotating the test object about a predetermined axis,
While rotating the object to be inspected by the rotating unit, by photographing the object to be inspected from a direction intersecting the direction of the axis, an imaging unit that acquires a plurality of images having different rotation angles,
Based on the image obtained by the imaging unit, a detection unit that detects a defect present on the surface of the inspection object,
With
The detection means,
A defect extraction unit for extracting a defect present in the image,
A two-dimensional map creating unit that develops the extracted defect into a two-dimensional map representing the surface of the inspection object by a plane,
A three-dimensional map creating unit that converts a defect in the two-dimensional map into a three-dimensional map adapted to a curved shape of the surface of the inspection object;
A defect detection unit that detects the size of the defect based on the three-dimensional map;
A surface inspection device comprising: The illuminating unit has a mask that forms a striped pattern formed by arranging a plurality of strips and blocks a part of light emitted from the illuminating unit,
The surface inspection apparatus according to claim 9, wherein the mask forms a stripe pattern formed by arranging a V-shape obtained by combining the strip shapes at a predetermined angle.   11. The illumination device according to claim 10, wherein the illumination unit includes a reflection surface that reflects light emitted from a light emitting source and irradiates the reflected light to the object to be inspected, and the mask is disposed on the reflection surface. Surface inspection apparatus according to 1.   12. The surface inspection apparatus according to claim 9, wherein the illumination unit irradiates light along a curved surface.   13. The lighting device according to claim 9, wherein the illumination unit further includes a backlight provided on a side opposite to the imaging unit with the object to be inspected therebetween, and irradiating light from behind the object to be inspected. The surface inspection apparatus according to claim 1 or 2.   The surface inspection apparatus according to claim 10, wherein the imaging unit is configured to blur and image a stripe pattern of the mask projected on the inspection object.   15. The surface inspection apparatus according to claim 14, wherein the imaging unit focuses on the object to be inspected to blur the stripe pattern of the mask. A manufacturing process for manufacturing a part having a hemispherical shape at least in part,
An inspection step of inspecting the hemispherical surface of the component;
Including
The inspection step includes:
By irradiating the hemispherical surface with light and rotating the component around a predetermined axis, a rotation angle is obtained by a photographing unit that photographs the hemispherical surface from a direction intersecting the direction of the axis. An image obtaining step of obtaining a plurality of images different from each other,
A defect extraction step of extracting a defect present in the image,
A two-dimensional map creating step of developing the extracted defects into a two-dimensional map representing the hemispherical surface as a plane;
Converting a defect in the two-dimensional map into a three-dimensional map adapted to the hemispherical surface;
A defect detection step of detecting the size of the defect based on the three-dimensional map;
A method for producing a component having at least a part of a hemispherical shape having On the computer,
An imaging unit configured to irradiate a light on a test object having a curved surface and to rotate the test object about a predetermined axis while photographing the test object from a direction intersecting the direction of the axis; An image acquisition function for acquiring a plurality of images having different rotation angles,
A defect extraction function for extracting a defect present in the acquired image,
A two-dimensional map creation function of developing the extracted defect into a two-dimensional map representing the surface of the inspection object by a plane,
A three-dimensional map creation function for converting a defect in the two-dimensional map into a three-dimensional map adapted to a curved surface shape of the surface of the inspection object;
A defect detection function for detecting the size of a defect based on the three-dimensional map;
The program that realizes.