JP2020008501A - Surface defect detection device and surface defect detection method - Google Patents

Abstract

To provide a surface defect detection device and surface defect detection method that can readily and accurately detect a surface defect of a measurement object as well.SOLUTION: A surface defect detection device has a computation processing device 11 that irradiates a measurement surface with linear light, photographs a light cutting line along a longitudinal direction as changing a position, and detects a surface defect on the basis of a plurality of light cutting images. The computation device has: a depth image generation unit 22 that identifies an x-direction coordinate position equal to or less than a prescribed pixel value where the light cutting image doest not exist as a missing value pixel for each light cutting image, generates a depth image representing an unevenness state of the measurement surface for each x-direction coordinate position for each light cutting image; a binarization processing unit 23 that generates a binarized depth image on the basis of a prescribed threshold; a defect image generation unit 24 that integrates the missing value image with the binarized depth image for each light cutting image, and generates a defect image representing each x-direction coordinate position by at least any of the binarized depth image and the missing value image is a first prescribed value as the surface detect.SELECTED DRAWING: Figure 4

Description

  The present invention relates to a surface defect detection device and a surface defect detection method for detecting a surface defect of an object to be measured using a light cutting method.

  The light-section method is a technique in which reflected light of linear light applied to a moving measurement target is imaged by an imaging device, and the unevenness of the measurement target is measured from the degree of bending of a line in the captured image. Here, a measurement method for measuring the unevenness of the measurement object using the light section method will be briefly described with reference to FIG. In this case, the measuring device for measuring the unevenness of the object to be measured by the light cutting method is provided with the linear light source 2 for irradiating linear light such as a line laser or a slit light. The measurement surface 1a is irradiated with linear light.

  The measuring device captures a light-section line 5 formed on the measurement surface 1a of the measurement target 1 by the imaging device 3, and obtains a captured image (also referred to as a light-section image) by an arithmetic processing device (see FIG. 1). (Not shown). At this time, if the measurement surface 1a is flat, a linear light cutting line 5 appears in the captured image. However, when the measurement surface 1a has irregularities, for example, a light cutting line 5 having a dent 5a is obtained as in a light cutting image 6 shown in the lower part of FIG.

  As described above, in the light-section method, the light-section image obtained in this manner is acquired at each irradiation section on the measurement surface 1a, and by repeating the process, the depth representing the unevenness state of the entire measurement surface 1a is obtained. An image can be calculated.

  Here, as shown in FIG. 2A, in the light sectioning method, for example, the linear light source 2 irradiates linear light perpendicular to the measurement surface 1a of the measurement target 1, and the imaging device 3 The measurement plane 1a is imaged from a direction at a predetermined angle θ with respect to the normal. Note that the positional relationship between the linear light source 2 and the imaging device 3 is not limited to FIG. 2A. For example, the imaging device 3 is arranged in a direction perpendicular to the measurement surface 1a, and the linear light source 2 is moved in a direction at a predetermined angle θ. It may be arranged. With such an optical arrangement, as shown in FIG. 2B, for example, in a concave portion 9 having a concave shape with a steep surface defect, the reflected light of the linear light is blocked by the concave portion 9 and does not reach the imaging device 3. In some cases, the pixel value of a portion corresponding to the concave portion 9 in the light-section image may be extremely low. In the light sectioning method, such a region having a low pixel value cannot be detected as the light cutting line 5, and therefore, in a depth image showing the unevenness of the measurement surface 1a, such a region having a low pixel value is represented by a pixel. The pixel value is displayed as a missing pixel having a missing value.

  For example, Patent Document 1 discloses detecting surface defects such as blowholes and cracks on the surface of a slab using such missing pixels. In the cited document 1, a missing value pixel is detected by a light section method, and when a plurality of missing value pixels exist continuously in the longitudinal direction, a continuous position of the missing value pixel is detected as a surface defect occurrence position.

  Also, in the cited document 2, the rate of change in the unevenness in the longitudinal direction of the slab surface calculated by the light cutting method using a laser projector and a laser light receiver, and the surface of the slab imaged using a halogen lamp and a camera provided separately from these devices. It discloses that a luminance distribution image is used to detect, as a surface defect, a portion where the concavo-convex change rate is equal to or more than a predetermined value and the pixel value of the luminance distribution image is low.

Japanese Patent No. 5396824 Japanese Patent No. 5200872

  However, according to the method disclosed in Patent Document 1, only the missing value pixel can be detected as a surface defect. Therefore, for example, even in a region inside the concave portion 9, the relationship between the incident angle, the depth, and the steepness of the concave portion 9 is obtained. Are not detected as surface defects, even though they are a part of the surface defects. As described above, in the method disclosed in Patent Literature 1, since only a part of the surface defect can be detected, there is a problem that the surface defect including the unevenness cannot be detected accurately.

  In the method disclosed in Patent Document 2, a camera is installed as a laser light receiver used in the light sectioning method, and a camera for generating a luminance distribution image is separately installed. Therefore, it is necessary to match the imaging regions of two images obtained by two different cameras having different imaging angles. In general, matching requires complicated image processing, and if erroneous matching is performed, a surface defect may be erroneously detected. Therefore, the method disclosed in Patent Document 2 has a problem that surface defects may not be easily and accurately detected.

  The present invention has been made in view of the above-described problems, and has an object to provide a surface defect detection device and a surface defect detection method that can more easily and accurately detect a surface defect of an object to be measured than before. I do.

  The surface defect detection device of the present invention is a surface defect detection device that detects a surface defect of a measurement surface of a measurement object having a longitudinal direction, wherein the linear light source that irradiates the measurement surface with linear light; An imaging device that captures a light-section line on the measurement surface by changing the measurement surface along the longitudinal direction, and generates a plurality of light-section images in which the light-section line extends in the x direction. An arithmetic processing unit that detects the surface defect based on the light-section image, wherein the arithmetic processing unit is configured such that, for each of the light-section images, an x-direction coordinate equal to or less than a predetermined pixel value where the light-section line does not exist. A missing-value image generating unit that generates a missing-value image in which a position is a missing-value pixel and a pixel value of the missing-value pixel is a first predetermined value; Depth image generation that generates depth images expressed for each directional coordinate position Performing a binarization process on the depth image, and calculating the depth indicated for each of the x-direction coordinate positions in the depth image, based on a predetermined threshold, the first predetermined value or the second predetermined value. A binarization processing unit that generates a binarized depth image, and integrates the missing value image and the binarized depth image for each of the light-section images to form the binarized depth image and the binarized depth image. In at least one of the missing value images, a pixel value at the x-direction coordinate position that is the first predetermined value is defined as the first predetermined value, and the second predetermined value of the binarized depth image and the first A defect image generation unit that generates a defect image representing each of the x-direction coordinate positions with a predetermined value, and a pixel value based on the plurality of defect images generated along the longitudinal direction of the measurement target. A defect detection unit that detects the area having the first predetermined value as the surface defect.

  The surface defect detection device of the present invention is a surface defect detection device that detects a surface defect of a measurement surface of a measurement object having a longitudinal direction, wherein the linear light source that irradiates the measurement surface with linear light; An imaging device that captures a light-section line on the measurement surface by changing the measurement surface along the longitudinal direction, and generates a plurality of light-section images in which the light-section line extends in the x direction. An arithmetic processing unit that detects the surface defect based on the light-section image, wherein the arithmetic processing unit displays, for each of the light-section images, the unevenness state of the measurement surface for each x-direction coordinate position. A depth image generating unit that generates a depth image obtained by performing a binarization process on the depth image, and sets the depth indicated for each x-direction coordinate position in the depth image to a predetermined threshold value. Generating a binarized depth image having a first predetermined value or a second predetermined value based on the first predetermined value or the second predetermined value. A binarization processing unit that identifies each x-direction coordinate position where a pixel value cannot be detected due to absence of the light section line as a missing pixel based on the binarized depth image; A defect that generates a defect image in which each x-direction coordinate position of the binarized depth image is represented by the first predetermined value and the second predetermined value, with the pixel value of the value pixel as the first predetermined value. An image generation unit, based on the plurality of defect images generated along the longitudinal direction of the measurement target, a defect detection unit that detects an area where a pixel value is the first predetermined value as the surface defect. , Is provided.

  Further, the surface defect detection method of the present invention is a surface defect detection method for detecting a surface defect of a measurement surface of a measurement object having a longitudinal direction, wherein the irradiation step of irradiating the measurement surface with linear light; An imaging step of imaging a light-section line on the measurement surface by light while changing the measurement surface along the longitudinal direction, and generating a plurality of light-section images in which the light-section line extends in the x direction; An arithmetic processing step of detecting the surface defect based on the light-section image, wherein the arithmetic processing step includes, for each light-section image, an x-direction equal to or less than a predetermined pixel value where the light-section line does not exist. A missing value image generating step of generating a missing value image in which a coordinate position is a missing value pixel and a pixel value of the missing value pixel is a first predetermined value, and for each of the light-section images, Depth for generating depth image expressed for each x-direction coordinate position An image generation step, performing a binarization process on the depth image, and calculating the depth indicated for each of the x-direction coordinate positions in the depth image, based on a predetermined threshold, the first predetermined value or the first (2) a binarization processing step of generating a binarized depth image having a predetermined value, and integrating the missing value image and the binarized depth image for each of the light section images; In at least one of an image and the missing value image, a pixel value at the x-direction coordinate position, which is the first predetermined value, is defined as the first predetermined value, and the second predetermined value of the binarized depth image is determined. A defect image generating step of generating a defect image representing each of the x-direction coordinate positions with the first predetermined value, based on the plurality of defect images generated along the longitudinal direction of the measurement target, A defect detection step of detecting a region where a pixel value is the first predetermined value as the surface defect; To.

  The surface defect detection method of the present invention is a surface defect detection method for detecting a surface defect of a measurement surface of a measurement object having a longitudinal direction, wherein the irradiation step of irradiating the measurement surface with linear light, An imaging step of imaging the light-section line on the measurement surface while changing the measurement surface along the longitudinal direction, and generating a plurality of light-section images in which the light-section line extends in the x direction, An arithmetic processing step of detecting the surface defect based on the light-section image, and the arithmetic processing step represents, for each of the light-section images, the unevenness state of the measurement surface at each x-direction coordinate position. A depth image generating step of generating a depth image, performing a binarization process on the depth image, and determining a depth indicated for each of the x-direction coordinate positions in the depth image based on a predetermined threshold. To generate a binarized depth image with the first predetermined value or the second predetermined value The binarization processing step, based on the binarized depth image, identifies each of the x-direction coordinate positions where a pixel value could not be detected without the light cutting line as a missing pixel, With the pixel value of the missing pixel as the first predetermined value, a defect image is generated in which each of the x-direction coordinate positions of the binarized depth image is represented by the first predetermined value and the second predetermined value. A defect image generating unit, and a defect detecting step of detecting, as the surface defect, an area having a pixel value of the first predetermined value based on the plurality of defect images generated along the longitudinal direction of the measurement object. And

  According to the present invention, since the missing pixel and the unevenness state of the measurement surface based on the depth image can be integrated to detect a surface defect on the measurement surface, only the missing pixel or only the depth image can be detected. Surface defects that could not be detected from the surface can also be detected. In addition, since a defect image can be generated based on a light-section image obtained from one imaging device, a matching process of matching a plurality of images having different imaging angles obtained by different imaging devices as in the related art becomes unnecessary, It is possible to prevent the occurrence of a problem due to the matching process. Therefore, the surface defect of the measurement object can be detected more easily and accurately than in the past.

It is the schematic for demonstrating a light-section method. FIG. 2A is a schematic cross-sectional view showing an example of a case where reflected light of linear light applied to a surface defect reaches an area camera in the light cutting method, and FIG. 2B is a diagram illustrating a line applied to a surface defect. It is a schematic sectional drawing which shows an example when the reflected light of shape light does not reach an area camera. It is a schematic diagram showing the whole surface defect detection device composition of an embodiment of the present invention. FIG. 1 is a block diagram illustrating an overall configuration of an arithmetic processing device according to an embodiment of the present invention. FIG. 5A is a schematic diagram schematically illustrating an example of a partially cut light section line, FIG. 5B is an enlarged view of the x-direction coordinate in FIG. 5A, and FIG. FIG. 5D is a depth image generated based on the light-section image, which is a missing value image generated based on the light-section image obtained by capturing the light-section line. FIG. 5E is a depth image generated based on the light-section image. 5F is a defect image generated from the missing value image of FIG. 5C and the binarization depth image of FIG. 5E. 5 is a flowchart illustrating a processing procedure of the arithmetic processing device according to the embodiment of the present invention. 9 is a flowchart illustrating a procedure of a process for generating a defect image. FIG. 11 is a block diagram illustrating an overall configuration of an arithmetic processing device according to another embodiment of the present invention. It is a flowchart which shows the processing procedure of the arithmetic processing unit of other embodiment of this invention.

  Hereinafter, an embodiment of the present invention will be described in detail with reference to the drawings. In the following description, the same components are denoted by the same reference numerals, and duplicate description will be omitted.

<About the shape measuring device of the present invention>
FIG. 3 is a schematic diagram showing the entire configuration of the surface defect detection device 10 according to the present invention. In the surface defect detection device 10, the measurement target 1 is transported in the longitudinal direction y of the measurement target 1, and detects a surface defect of the measurement surface 1a of the transported measurement target 1. The surface defect detection device 10 includes a linear light source 2 that irradiates linear light on a measurement surface 1a of the measurement target 1 in a predetermined x direction (in this case, a vertical direction perpendicular to the longitudinal direction y), An imaging device 3 that captures reflected light of the illuminated linear light of 1a and an arithmetic processing device 11 that detects a surface defect on the measurement surface 1a based on the light-section image generated by the imaging device 3 are provided. .

  The linear light source 2 includes a known light source such as a laser or an LED. On the measurement surface 1a of the measuring object 1 irradiated with the linear light, the linear light is reflected to form a linear bright portion. The reflected light formed on the measurement surface 1 a of the measurement target 1 by the linear light is imaged by the imaging device 3 as a light cutting line 5. The imaging device 3 captures an image of a predetermined area 7 of the measurement surface 1a on which the light cutting line 5 is formed, and acquires a captured image in which all pixels of the entire imaging field of view including the light cutting line 5 are exposed. After that, the imaging device 3 extracts a partial area including the light-section line 5 from the acquired captured image to generate a light-section image.

  Here, the captured image includes not only a form as a specific image displayed on the display but also imaging data before being generated as an image. Therefore, a partial area including the light-section line 5 is extracted from the acquired image-capture data without forming a specific image from the image-capture data in which all the pixels in the entire field of view are exposed. May be generated.

  As described above, the imaging device 3 extracts only the periphery of the light section line 5 as a light section image from the captured image in which all the pixels in the entire field of view are exposed, so that the data amount is reduced as compared with the captured image. Generated light section image. Since the measurement object 1 is being conveyed, the imaging device 3 images the light cutting line 5 whose position changes on the measurement surface 1a along the longitudinal direction y, and the plurality of light cutting lines 5 extending in the x direction. To generate a light section image of. The imaging device 3 sequentially transmits the light-section images acquired at predetermined intervals along the longitudinal direction y of the measurement target 1 to the arithmetic processing device 11.

  Next, the arithmetic processing unit 11 will be described. As shown in FIG. 4, the arithmetic processing device 11 includes an acquisition unit 12 that receives a light-section image from the imaging device 3, an image processing unit 20 that performs image processing on the light-section image to detect a surface defect, and a storage unit 14. And a display unit 15 for displaying the detection result of the surface defect. The acquisition unit 12 sends the light-section images sequentially input from the imaging device 3 to the image processing unit 20.

  The image processing unit 20 includes a missing value image generation unit 21, a depth image generation unit 22, a binarization processing unit 23, a defect image generation unit 24, a non-existing area detection processing unit 25, and a noise removal processing unit. 26 and a defect detection unit 27. The missing value image generation unit 21, the depth image generation unit 22, and the non-existing area detection processing unit 25 are connected to the acquisition unit 12, and receive the light section images from the acquisition unit 12, respectively.

  The missing-value image generation unit 21 performs a missing-value image generation process for each light-section image, and generates a missing-value image in which a pixel in which a light-section line is interrupted in each light-section image is specified as a missing-value pixel. Here, the missing value image generation processing for generating the missing value image will be described below. FIG. 5A is a schematic view showing the light cutting line 5 in the light cutting image, for example, schematically showing an example of the light cutting line 5 partially cut off by the steep concave portion 9.

  In FIG. 5A, the light-section line 5 extends in the x-direction in the light-section image, and the x-direction coordinates are defined along the x-direction, which is the direction in which the light-section line 5 extends. The numerical values 1 to 9 in FIG. 5A are numerical values added for convenience, and indicate the x-direction coordinate position. The numerical values of 1 to 9 are given in units of pixels arranged in the x direction in the light-section image. In the light-section image, the pixel value of the pixel having the light-section line is equal to or more than the predetermined threshold value, and the pixel is brightly displayed. The pixel value of the pixel without the light-section line is smaller than the predetermined threshold value, and is displayed dark. In FIG. 5A, the light-section line 5 in the light-section image is indicated by a solid line, and the light-section line 5 is interrupted in the light-section image to have a pixel value less than a predetermined threshold, and the pixel value is a missing value. The location of the line is indicated by a broken line. That is, the broken line indicates the concave portion 9 of the measurement surface 1a that does not appear due to the light cutting line 5.

  In this case, the x-direction coordinate positions 1 to 3 and the x-direction coordinate positions 8 and 9 indicate ranges in which there is no unevenness on the measurement surface 1a and there is no harmful area with no surface defects. At the x-direction coordinate positions 1 to 3, 8, and 9 which are such harmless areas, the light cutting line 5 appears linearly. On the other hand, at the x-direction coordinate positions 4 to 7, the measurement surface 1a has irregularities and indicates a range to be determined as a harmful region having a surface defect. Among the x-direction coordinate positions 4 to 7 which are harmful areas, the x-direction coordinate positions 4 and 7 have pixels having a pixel value equal to or larger than a predetermined threshold value, and the depth (the amount of unevenness) of the light cutting line 5 is measured. Is a possible part. On the other hand, among the x-direction coordinate positions 4 to 7 which are harmful areas, the x-direction coordinate positions 5 and 6 are portions where missing light pixels smaller than a predetermined pixel value where the light section line 5 does not exist.

  Upon receiving the light section image from the acquisition section 12, the missing value image generation section 21 generates a missing value image showing only the missing value pixels less than a predetermined threshold for each x-direction coordinate of the light section image. Here, FIG. 5B is an enlarged view of the x-direction coordinates shown in FIG. 5A. FIG. 5C shows a determination result based on the light section line 5 in FIG. 5A for determining whether or not there is a missing pixel at each x-direction coordinate position in FIG. 5B. 3 shows a value image.

  In this case, as shown in FIG. 5A, the missing value image generation unit 21 is configured such that, for each of the x-direction coordinate positions 1 to 9 in the light-section image, a pixel column arranged in the y-direction orthogonal to the x-direction in the light-section image It is determined whether or not there is a pixel whose pixel value is equal to or larger than a predetermined threshold.

  For example, as shown in FIG. 5A, since the light section line 5 does not exist at each of the x-direction coordinate positions 5 and 6 in the light-section image, a pixel having a pixel value equal to or larger than a predetermined threshold value is included in the pixel row in the y-direction. Does not exist. In this case, the missing value image generation unit 21 determines that there is no pixel having a pixel value equal to or larger than the predetermined threshold value for each of the x-direction coordinate positions 5 and 6 in the light-section image, and the missing value for which the unevenness state cannot be calculated by the light-section method. It is determined that the pixel is a pixel, and the pixel value of the missing value image is set to the first predetermined value “1” at each of the x-direction coordinate positions 5 and 6.

  On the other hand, as shown in FIG. 5A, since the light section line 5 exists at each of the x-direction coordinate positions 1 to 4 and 7 to 9 in the light section image, a predetermined threshold value is included in the pixel row in the y direction. There are pixels having the above pixel values. In this case, the missing value image generation unit 21 determines that the pixels having the pixel values equal to or larger than the predetermined threshold value exist at the respective x-direction coordinate positions 1 to 4 and 7 to 9 in the light-section image, Can be calculated, and the pixel value of the missing value image is set to the second predetermined value “0” at each of the x-direction coordinate positions 1 to 4 and 7 to 9. In this way, as shown in FIG. 5C, the missing value image generation unit 21 sets the pixel value at each x-direction coordinate position where the missing pixel of the light-section image is located as the first predetermined value, and A missing value image with the pixel value at the x-direction coordinate position as the second predetermined value is generated, and sent to the defect image generating unit 24.

  At this time, the depth image generation unit 22 performs a depth image generation process in parallel with the above-described missing value image generation process of the missing value image generation unit 21 and exists for each light-section image in the light-section image. On the basis of the y-direction coordinate position of the light cutting line 5, a depth image representing the unevenness of the measurement surface 1a of the measurement target 1 is generated. For example, when there is a concave surface defect (recess 9) on the measurement surface 1a, a curved section caused by the concave shape of the measurement surface 1a appears in the light-section line 5 in the light-section image. The unevenness state is calculated based on the y-direction coordinate position that changes in the light-section image based on the degree of bending of the curved portion.

  In addition, since the depth image generation processing of the measurement surface 1a by the depth image generation unit 22 is the same as that of Japanese Patent No. 5488953, a detailed description thereof is omitted here. As shown, the depth image generation unit 22 first obtains each y-direction coordinate position that takes the maximum pixel value for each x-direction coordinate position of the light-section image.

  Next, the depth image generation unit 22 calculates the depth indicating the amount of unevenness in the light-section image based on the displacement amount of the y-direction coordinate position indicating the position of the light-section line 5 obtained for each x-direction coordinate position. The depth is calculated for each direction position, and as shown in FIG. 5D, the depth as the calculation result is stored in each x-direction coordinate position to generate a depth image.

  For example, FIG. 5D shows a depth image generated based on the light cutting line 5 shown in FIG. 5A, and the light cutting line 5 is located at the x-direction coordinate positions 1 to 4 and 7 to 9 of the light cutting image. Existing. For this reason, the depth image generation unit 22 determines, for each x-direction coordinate position 1 to 4, 7 to 9 based on the position of the light cutting line 5 in the y direction at the x-direction coordinate position 1 to 4, 7 to 9 Depth is calculated, and a depth image is generated in which the calculation results (in this case, “−0.1”, “−0.8”, etc.) are associated with the respective x-direction coordinate positions 1 to 4, 7 to 9 I do.

  At this time, the depth image generation unit 22 cannot calculate the depth of the x-direction coordinate positions 5 and 6 where there is no pixel having a pixel value equal to or larger than the predetermined threshold value. This is a calculated value (in FIG. 5D, indicated by “?”).

  The depth image generation unit 22 sends the depth image to the binarization processing unit 23. Further, the depth image generation unit 22 stores the generated depth image in the storage unit 14. Upon receiving the depth image, the binarization processing unit 23 performs a binarization process on the depth image to generate a binarized depth image. In this case, the binarization processing unit 23 calculates a depth associated with each x-direction coordinate position in the depth image, and uses this depth as a reference for a predetermined threshold value. The depth image is binarized by setting the pixel value at each corresponding coordinate position to the first predetermined value “1” or the second predetermined value “0”. In the following, a description will be given of a case where a concave portion that can be a surface defect is generally set to a first predetermined value “1”. A threshold may be set for each of the concave side and the convex side so that any of the concave and convex surface defects can be detected. Instead of setting a threshold value in advance, an appropriate threshold value may be dynamically searched and determined by a known method.

  For example, when “−0.5” is set in the binarization processing unit 23 as the predetermined threshold, as shown in FIG. 5E, the x-direction coordinate position where the depth is “−0.5” or less 4, 7 are set to the first predetermined value, and the remaining x-direction coordinate positions 1 to 3, 8, 9 are set to the second predetermined value. Note that the binarization processing unit 23 does not binarize the x-direction coordinate positions 5 and 6 to which the uncalculated values are associated in the depth image, and keeps the uncalculated values. The binarization processing unit 23 sends the binarized depth image generated from the depth image by the binarization process to the defect image generation unit 24.

  In this case, the x-direction coordinate positions 3 and 8, which are shallower than the predetermined threshold, are recessed from the originally flat surface (corresponding to the depth of “0”) of the measurement surface 1a, Since the degree of the dent is small, it indicates a range that is not enough to determine harmful. On the other hand, the x-direction coordinate positions 4 and 7 whose depth is greater than the predetermined threshold value have a large dent and must be judged harmful. Indicates the range that must be met.

  Upon receiving the missing value image and the binarized depth image generated based on the same light section line, the defect image generating unit 24 performs a defect image generation process and integrates the missing value image and the binarized depth image. To generate a defect image. The defect image generation unit 24 compares the same x-direction coordinate position of the missing value image and the binarized depth image, and determines that the first predetermined value is at least one of the missing value image and the binarized depth image. The corresponding x-direction coordinate position is specified.

  When at least one of the missing value image and the binarized depth image specifies the x-direction coordinate position associated with the first predetermined value, the defect image generation unit 24 generates a defect using the specified x-direction coordinate value. The pixel value of the image is set to the first predetermined value “1”. As a result, as shown in FIG. 5E, the x-direction coordinate positions 5, 6 associated with the uncalculated values in the binarized depth image have the first predetermined value in the missing value image as shown in FIG. 5C. Since "1" is associated, "1" of the first predetermined value is associated. Further, the defect image generation unit 24 converts the pixel value of the defect image to the second value at the x-direction coordinate position associated with the second predetermined value “0” in both the missing value image and the binarized depth image. By setting the predetermined value to “0”, as shown in FIG. 5F, a defect image in which the first predetermined value or the second predetermined value is associated with each x-direction coordinate position is generated.

  In this way, the defect image generation unit 24 calculates the x-direction coordinate positions 4 and 7 at which the depth of the surface defect can be measured based on the depth image and the x-direction where the pixel value becomes a missing value in the light-section image. By combining the coordinate positions 5 and 6, it is possible to generate a defect image from which a harmful surface defect can be determined.

  Here, of the missing value image and the binarized depth image obtained by the missing value image generation processing and the binarization processing described above, all the x-direction coordinate positions whose pixel values are the first predetermined value are directly used. If the defect is determined to be a surface defect and output, there is a risk that the measurement object 1 does not exist in the defect image, and that all the end regions in the x direction are determined to be harmful, or that minute noise remains in the defect image. As a result, there is a possibility that surface defects on the measurement surface 1a cannot be accurately detected.

  Therefore, the surface defect detection device 10 performs a non-existing area detection process and a noise removal process, which will be described later, on the defect image obtained by the defect image generation unit 24, and detects the end portion in the x direction where the measurement target 1 does not exist. Regions and noise are removed from the defective image. Hereinafter, the non-existing area detection processing and the noise removal processing will be described in order.

  In this case, the defect image generation unit 24 sends the generated defect image to the non-existing area detection processing unit 25. Here, since the light-section image used for generating the defect image is captured including the region where the measurement target 1 does not exist, the region where the light-section line 5 is not captured at the end region in the x direction in the light-section image. (Hereinafter, also referred to as a light-section-free area). Therefore, when a defect image is generated based on such a light-section image, the first predetermined value “1” corresponds to each x-direction coordinate position also in the x-direction end region where the light-section line 5 does not exist. Attached.

  The non-existing region detection processing unit 25 excludes the light cutting line non-existing region in such a defect image from the surface defect detection target, so that the light cutting line non-existing region is detected during the surface defect detecting process described later. This prevents erroneous detection as a surface defect. In this case, first, the non-existing region detection processing unit 25 detects the light cutting line existing region in the x direction where the light cutting line 5 is imaged based on the pixel value of the light cutting image, The end region in the x direction that deviates from the existing region is excluded from the defect image as a non-existent region of the light cutting line.

  In the present embodiment, for example, the non-existing region detection processing unit 25 moves along the x direction from the x direction coordinate position of both ends in the x direction of the light-section image toward the x direction coordinate position of the central portion. The x-coordinate position of the pixel indicating the existence of the light-section line 5 is sequentially detected, and the start position and the end position of the light-section line existing area are specified. As a method of specifying the start position and the end position, for example, the maximum pixel value at each x-direction coordinate position is calculated from both ends toward the center of the light-section image, and the x-direction coordinate position where a value equal to or greater than a predetermined value is first detected May be specified as both ends where the light cutting line 5 exists. In this way, the non-existing area detection processing unit 25 detects the x-direction coordinate position of the end of the measurement target 1 from the light-section line existing area, and detects the x-direction deviating from the light-section line existing area in the defect image. Is excluded from the defect image as a light cutting line non-existing region, and a defect image composed of a light cutting line existing region is generated. The non-existing area detection processing unit 25 sends the defect image subjected to the non-existing area detection processing to the noise removal processing unit 26.

  As another embodiment, for example, the non-existing region detection processing unit 25 obtains the maximum pixel value at the adjacent x-direction coordinate position in the light-section image, and then calculates the maximum pixel value of the adjacent pixels. The difference may be calculated, and a coordinate position where the calculated difference value is large may be used as a boundary between the light section line existing area and the light section non-existent area to generate a defect image including the light section line existing area.

  When the noise removal processing unit 26 receives a predetermined number of defect images composed of light-section lines existing areas along the longitudinal direction y of the measurement target 1, the noise removal processing unit 26 compares the defect images obtained in chronological order with the longitudinal direction of the measurement target 1. By arranging them along the direction y, a combined defect image is generated.

  The noise elimination processing unit 26 performs a noise elimination process on the synthesized defect image, and sets a pixel area having a first predetermined value in the synthesized defect image (a set of pixels associated with the first predetermined value. , Hereinafter also referred to as blobs), a region having a small area is removed as noise. Specifically, the noise removal processing unit 26 performs a labeling process for recognizing blobs on the combined defect image, and calculates the area of each blob.

  The noise removal processing unit 26 identifies blobs having a predetermined area or less as noise from the blobs based on the calculation result and removes the blobs from the defect image. As another embodiment, the noise removal processing unit 26 performs a morphological operation process (Open process, Close process, or the like) that is common in the field of image processing on a synthetic defect image to remove noise without performing labeling. May be. In this case, the labeling may be performed in a defect detection unit 27 described later. The noise removal processing unit 26 sends the combined defect image after noise removal to the defect detection unit 27.

  Upon receiving the combined defect image after the noise removal processing from the noise removal processing unit 26, the defect detection unit 27 detects a region (blob) having the first predetermined value from the combined defect image as a surface defect. At this time, the defect detection unit 27 reads the depth image from the storage unit 14 and refers to the depth of the surface defect (particularly, the depth around the pixel having the first predetermined value in the missing value image). It is also possible to determine whether the surface defect is a concave portion or a convex portion. Further, as another embodiment, for example, an index that quantifies the characteristics of a blob is separately calculated, and a known surface recognition method such as Support Vector Machine is used to further discriminate the surface defects detected in the combined defect image. Is also good. In this way, the types of detected surface defects can be classified in detail by pattern recognition.

  The defect detection unit 27 is connected to the display unit 15 and the storage unit 14 and sends out the detection result of the surface defect to the display unit 15 and the storage unit 14. Thereby, the storage unit 14 stores the detection result of the surface defect, and the display unit 15 displays the detection result of the surface defect, so that the operator can confirm the detection result.

<Surface defect detection processing of the present invention>
Next, the above-described surface defect detection processing executed by the arithmetic processing unit 11 of the surface defect detection apparatus 10 will be briefly described with reference to the flowchart shown in FIG. As shown in FIG. 6, the processing unit 11 proceeds from the start step to step S1.

  In step S1, when the surface defect detection process is started, the acquisition unit 12 acquires a light section image from the imaging device 3, and sends the light section image to the missing value image generation unit 21 and the depth image generation unit 22. Then, the process proceeds to the next steps S2 and S3. In step S2, the depth image generation unit 22 generates a depth image representing the unevenness of the measurement surface 1a from the received light-section image, and proceeds to the next step S4.

  In step S4, the binarization processing section 23 binarizes the depth image to generate a binarized depth image, and proceeds to the next step S5. On the other hand, in step S3, the missing-value image generation unit 21 specifies, as a missing-value pixel, an x-direction coordinate position in which the pixel value of the received light-section image is equal to or less than a predetermined threshold, and specifies the x-direction coordinate position as the missing-value pixel. A missing value image is generated in which the predetermined value is set to 1 and the x-direction coordinate position other than the missing value pixel is set to the second predetermined value, and the process proceeds to the next step S5.

  In step S5, the defect image generation unit 24 performs a defect image generation process, and generates a defect image by integrating the missing value image and the binarized depth image. Here, the defect image generation processing in step S5 will be described below with reference to the flowchart shown in FIG. As shown in FIG. 7, the defect image generation unit 24 proceeds from the start step to step S10. In step S10, the defect image generation unit 24 initializes the value of the counter i indicating each x-direction coordinate of the missing value image and the binarized depth image, sets the value of the counter i to 1, for example, Move to step S11.

  In step S11, the defect image generation unit 24 determines whether or not the x-direction coordinate position i of the missing value image is a missing value pixel. Specifically, when the first predetermined value is associated with the x-direction coordinate position i of the missing value image, it is determined that the x-direction coordinate position i is a missing pixel, and the process proceeds to the next step S14. On the other hand, when the second predetermined value is associated with the x-direction coordinate position i of the missing value image, it is determined that the x-direction coordinate position i is not a missing value pixel, and the process proceeds to the next step S12.

  In step S12, the defect image generation unit 24 determines whether the x-direction coordinate position i of the binarized depth image is a harmful surface defect. Specifically, when the first predetermined value is associated with the x-direction coordinate position i of the binarized depth image, it is determined that the x-direction coordinate position i is a harmful surface defect, and the next step is performed. Move to S14. On the other hand, when the second predetermined value is associated with the x-direction coordinate position i of the binarized depth image, it is determined that the x-direction coordinate position i is not a harmful surface defect, and the next step is performed. Move to S13.

  In step S13, since negative results are obtained in step S11 and step S12, respectively, the x-direction coordinate position i is not a missing pixel and is not a harmful surface defect. The pixel value at the x-direction coordinate position i is set as the second predetermined value, and the flow advances to the next step S15. On the other hand, in step S14, since a positive result is obtained in either step S11 or step S12, the x-direction coordinate position i is either a missing pixel or a harmful surface defect. The generation unit 24 sets the pixel value at the x-direction coordinate position i as the first predetermined value, and proceeds to the next step S15.

  In step S15, the defect image generation unit 24 adds 1 to the value of the counter i indicating the x-direction coordinate position, and proceeds to the next step S16. In step S16, the defect image generation unit 24 determines whether the value of the x-direction coordinate position i is n. Here, n is a positive integer, and indicates the x-direction coordinate position of the end of the light-section image in the x-direction. Therefore, when a positive result is obtained in step S16, this means that the value of the x-direction coordinate position i becomes n, and the above-described processing is performed on all the pixels in the x direction of the missing value image and the binarized depth image. Implies that the defect image has been generated by the completion of the processing of the above, the defect image generating unit 24 ends the above-described processing, and proceeds to step S6 in FIG.

  On the other hand, if a negative result is obtained in step S16, this means that the value of the x-direction coordinate position i is not n. At this time, the defect image generating unit 24 obtains a positive result in step S16. Steps S11 to S15 described above are repeated until the operation is completed. Next, returning to the flowchart of FIG. 6, steps S6 and subsequent steps will be described.

  In step S6, each time a defect image is obtained, the non-existing region detection processing unit 25 detects a light cutting line existing region where a light cutting line exists from the defect image, and The optical cut line non-existing area is specified, the optical cut line non-existent area is excluded from the defect image, and the process proceeds to the next step S7.

  In step S7, the noise removal processing unit 26 generates a combined defect image by arranging the defect images sequentially obtained along the longitudinal direction y of the measurement target 1 along the longitudinal direction y, and removes noise from the combined defect image. Then, the process proceeds to the next step S8.

  In step S8, the defect detection unit 27 detects the blob as a surface defect from the combined defect image, and the above-described surface defect detection processing ends.

<Action and effect>
In the above configuration, the surface defect detection device 10 generates a missing value image in which the pixel value of the missing value pixel at the x-direction coordinate position where no light cutting line exists is 1 (first predetermined value). In addition, the surface defect detection device 10 generates a depth image representing the unevenness of the measurement surface 1a of the measurement target 1 based on the light-section image, performs a binarization process on the depth image, A binarized depth image in which the depth indicated at each x-direction coordinate position in the image is set to 1 (first predetermined value) or 0 (second predetermined value) based on a predetermined threshold value is generated. . Then, the surface defect detection apparatus 10 sets the pixel value at the x-direction coordinate position as the first predetermined value to 1 (first predetermined value) in at least one of the missing value image and the binarized depth image, The pixel value at the x-direction coordinate position other than the first predetermined value is set to 0 (second predetermined value) for both the value image and the binarized depth image, and the missing value image and the binarized depth image are set for each light-section image Is integrated to generate a defect image. Thus, the surface defect detection device 10 can detect a surface defect based on a plurality of defect images generated along the longitudinal direction y of the measurement target 1.

  According to the present invention, the surface defect detection device 10 can detect the surface defect of the measurement surface 1a by integrating the missing pixel and the unevenness state of the measurement surface 1a based on the depth image. Only, or the entire surface defect that could only be partially detected from the depth image.

  Furthermore, since the surface defect detection device 10 can generate a defect image based on a light-section image obtained from one imaging device, it matches a plurality of images obtained by different imaging devices with different imaging angles as in the related art. This eliminates the need for a matching process, which simplifies the process and prevents the occurrence of a problem due to the matching process. Therefore, the surface defect of the measurement object can be detected more easily and accurately than in the past.

  Further, for example, in a case where the defect shown in FIG. 5 is to be detected, if a process of ignoring a pixel that has become a missing value because the value is an uncalculated value is performed, the x direction in FIG. There is a risk of erroneously detecting that two defects have occurred only from the depths of the coordinate positions 4 and 7. However, in the case of the surface defect detection device 10, the defect is located in the range of the missing pixel and in the vicinity of the missing value. Since the range that is determined to be harmful can be comprehensively detected as a defect, erroneous detection can be reduced.

  In addition, if the pixel having the missing value is mechanically determined to be defective, the unevenness information of the pixel having the missing value cannot be obtained. Consider the depth of the range you want to judge and the depth around the range you want to judge as harmful (if such a range is concave, you can judge that the defect including the missing value is concave; if the range is convex, you can judge the defect including the missing value Can be determined to be convex), it is possible to determine whether the entire defect is a concave defect or a convex defect.

<Other embodiments>
In the above embodiment, a case has been described in which a missing value image is generated separately from a binarized depth image, and the missing image and the binarized depth image are integrated to generate a defect image. Is not limited to this. For example, without generating a missing value image, a missing pixel at an x-direction coordinate position is specified from the binary depth image, and a defect image is generated based on the binary depth image specifying the missing pixel. You may do so.

  In this case, for example, as illustrated in FIG. 5E, in the binarized depth image, at the x-direction coordinate positions 5 and 6, the binarization process is performed because the uncalculated value is associated with the depth image. And the uncalculated value (represented by “?” In FIG. 5E) remains. Therefore, as another embodiment, the x-direction coordinate position of the missing value pixel can be specified from the uncalculated value associated with the binarized depth image. Thus, it is possible to generate a defect image only from the binarized depth image without generating a missing value image separately from the depth image.

  FIG. 8, in which the same components as in FIG. 4 are assigned the same reference numerals, is a block diagram illustrating the configuration of the arithmetic processing device 18 according to the other embodiment described above. The arithmetic processing device 18 illustrated in FIG. 8 is different from the arithmetic processing device 11 of the above-described embodiment in the configuration of the defect image generation unit 31 provided in the image processing unit 28, and is mainly described here. The following description focuses on the defect image generation unit 31.

  The image processing unit 28 includes a depth image generation unit 22, a binarization processing unit 23, a defect image generation unit 31, a non-existing area detection processing unit 25, a noise removal processing unit 26, and a defect detection unit 27. It has. In this case, the defect image generation unit 31 receives a binarized depth image obtained by binarizing the depth image from the binarization processing unit 23. Upon receiving the binarized depth image as shown in FIG. 5E, for example, the defect image generating unit 31 determines the x-direction coordinate positions 5 and 6 to which the uncalculated values are associated in the binarized depth image. Identify.

  The defect image generation unit 31 associates the first predetermined value with the x-direction coordinate positions 5, 6 to which the uncalculated value is associated in the binarized depth image, and positions the x-directional coordinate position of the binarized depth image. 1 to 9 generate a defect image defined only by the first predetermined value or the second predetermined value. As a result, the image processing unit 28 does not need a missing value image generation process for generating a missing value image separately from the binarized depth image, and integrates the missing value image and the binary depth image. Is unnecessary, so that the calculation processing load can be reduced.

  When a defect image is generated by the defect image generation unit 31, the image processing unit 28 can generate a defect image including a light section line existing region by the non-existing region detection processing unit 25, as in the above-described embodiment. Further, similarly to the above-described embodiment, the image processing unit 28 arranges, by the noise removal processing unit 26, the defect images formed of the light cutting line existing regions in the order in which the defect images are obtained along the longitudinal direction y of the measurement target 1. As a result, a combined defect image can be generated and noise can be removed. As described above, in the other embodiments, the same effects as those of the above-described embodiments can be obtained.

  Next, surface defect detection processing in the arithmetic processing unit 18 according to another embodiment will be briefly described with reference to the flowchart shown in FIG. In this case, in step S1, the acquisition unit 12 acquires the light-section image from the imaging device 3, sends the light-section image to the depth image generation unit 22, and proceeds to the next step S2.

  In step S2, the depth image generation unit 22 generates a depth image based on the light-section image, and proceeds to the next step S4. In step S4, the binarization processing unit 23 binarizes the depth image to generate a binarized depth image, and proceeds to the next step S30. In step S30, the missing value image generation unit 21 specifies an x-direction coordinate position that is an uncalculated value in the binarized depth image, and sets the specified x-direction coordinate position to the first predetermined value “1”. And a missing value image in which the first predetermined value or the second predetermined value is associated with the x-direction coordinate position of the binarized depth image, respectively, and then proceeds to the next step 6. In the subsequent processing, as in the flowchart of FIG. 6 in the above-described embodiment, steps S6 to S8 are executed, and the above-described processing ends.

  Further, in another embodiment, after the missing image is generated by specifying the missing pixel from the binarized depth image by the defect image generating unit, the missing image and the binarized depth image are combined. The defect image may be generated by integration.

  In the above embodiment, the calculation was performed at all the x-direction coordinate positions in the missing value image generation unit 21 and the depth image generation unit 22, but the processing of the non-existing area detection processing unit 25 is performed inside the acquisition unit 12. Is also good. As a result, the calculation target of the missing value image generation unit 21 and the depth image generation unit 22 can be limited to the light cutting line existing region, and the speed can be further increased.

  In the above-described embodiment, the case has been described in which the noise removal processing unit 26 generates a combined defect image in which defect images are arranged along the longitudinal direction y of the measurement target 1, but the present invention is not limited to this. A combined defect image in which defect images are arranged along the longitudinal direction y of the measurement target 1 may be generated by the defect image generation unit or the non-existing region detection unit.

REFERENCE SIGNS LIST 1 measurement object 2 linear light source 3 imaging device 10 surface defect detection device 11 arithmetic processing device 21 missing value image generation unit 22 depth image generation unit 23 binarization processing unit 24 defect image generation unit 27 defect detection unit

Claims (7)

In a surface defect detection device that detects a surface defect of a measurement surface of a measurement object having a longitudinal direction,
A linear light source that irradiates the measurement surface with linear light,
An imaging apparatus for imaging a light-section line on the measurement surface by the linear light while changing the measurement surface along the longitudinal direction, and generating a plurality of light-section images in which the light-section line extends in the x direction. When,
An arithmetic processing device that detects the surface defect based on the plurality of light-section images,
Has,
The arithmetic processing unit,
For each of the light-section images, a missing-value image in which an x-direction coordinate position equal to or less than a predetermined pixel value where the light-section line does not exist is defined as a missing pixel, and a pixel value of the missing pixel is set to a first predetermined value. A value image generator,
For each light-section image, a depth image generation unit that generates a depth image representing the unevenness state of the measurement surface for each x-direction coordinate position,
A binarization process is performed on the depth image, and the depth indicated for each x-direction coordinate position in the depth image is set to the first predetermined value or the second predetermined value based on a predetermined threshold. A binarization processing unit that generates a binarized depth image;
The missing value image and the binarized depth image are integrated for each of the light section images, and at least one of the binarized depth image and the missing value image is the first predetermined value. A pixel value at an x-direction coordinate position is defined as the first predetermined value, and a defect image representing each of the x-direction coordinate positions is generated by the second predetermined value and the first predetermined value of the binarized depth image. A defect image generation unit;
A defect detection unit configured to detect, as the surface defect, an area in which a pixel value is the first predetermined value, based on the plurality of defect images generated along the longitudinal direction of the measurement target; Defect detection device. In a surface defect detection device that detects a surface defect of a measurement surface of a measurement object having a longitudinal direction,
A linear light source that irradiates the measurement surface with linear light,
An imaging apparatus for imaging a light-section line on the measurement surface by the linear light while changing the measurement surface along the longitudinal direction, and generating a plurality of light-section images in which the light-section line extends in the x direction. When,
An arithmetic processing device that detects the surface defect based on the plurality of light-section images,
Has,
The arithmetic processing unit,
For each of the light-section images, a depth image generation unit that generates a depth image representing the unevenness state of the measurement surface for each x-direction coordinate position,
A binarization process is performed on the depth image, and the depth indicated for each of the x-direction coordinate positions in the depth image is set to a first predetermined value or a second predetermined value based on a predetermined threshold. A binarization processing unit that generates a binarized depth image;
Based on the binarized depth image, identify each of the x-direction coordinate positions where a pixel value could not be detected without the light cutting line as a missing value pixel, and determine a pixel value of the missing value pixel as the missing value pixel. A defect image generating unit that generates a defect image in which each of the x-direction coordinate positions of the binarized depth image is represented by the first predetermined value and the second predetermined value as a first predetermined value;
A defect detection unit configured to detect, as the surface defect, an area in which a pixel value is the first predetermined value, based on the plurality of defect images generated along the longitudinal direction of the measurement target; Defect detection device.   The arithmetic processing unit detects a light cutting line existing region in the x direction in which the light cutting line is imaged based on a pixel value, and detects an end region in the x direction deviating from the light cutting line existing region. 3. The surface defect detection device according to claim 1, further comprising a non-existing region detection processing unit that specifies a cutting line non-existing region and excludes the light cutting line non-existing region from the surface defect detection targets.   2. The arithmetic processing device according to claim 1, further comprising: a noise removal processing unit that removes, from the defect image, an area of the defect image in which a pixel value of a pixel whose pixel value is the first predetermined value is equal to or less than a predetermined value. The surface defect detection device according to any one of claims 1 to 3.   The surface defect detection device according to claim 1, wherein the defect detection unit detects an uneven state of the surface defect using the defect image and the depth image. In a surface defect detection method for detecting a surface defect of a measurement surface of a measurement object having a longitudinal direction,
An irradiation step of irradiating the measurement surface with linear light,
An imaging step of imaging a light-section line on the measurement surface by the linear light while changing the measurement surface along the longitudinal direction, and generating a plurality of light-section images in which the light-section line extends in the x direction. When,
An arithmetic processing step of detecting the surface defect based on the plurality of light-section images,
Has,
The arithmetic processing step includes:
For each of the light-section images, a missing-value image in which an x-direction coordinate position equal to or less than a predetermined pixel value where the light-section line does not exist is defined as a missing pixel, and a pixel value of the missing pixel is set to a first predetermined value. A value image generation step,
For each light-section image, a depth image generating step of generating a depth image representing the unevenness state of the measurement surface for each x-direction coordinate position,
A binarization process is performed on the depth image, and the depth indicated for each x-direction coordinate position in the depth image is set to the first predetermined value or the second predetermined value based on a predetermined threshold. A binarization processing step of generating a binarized depth image;
The missing value image and the binarized depth image are integrated for each of the light section images, and at least one of the binarized depth image and the missing value image is the first predetermined value. A pixel value at an x-direction coordinate position is defined as the first predetermined value, and a defect image representing each of the x-direction coordinate positions is generated by the second predetermined value and the first predetermined value of the binarized depth image. A defect image generation step;
A defect detection step of detecting, as the surface defect, an area having a pixel value of the first predetermined value, based on the plurality of defect images generated along the longitudinal direction of the measurement object. Defect detection method. In a surface defect detection method for detecting a surface defect of a measurement surface of a measurement object having a longitudinal direction,
An irradiation step of irradiating the measurement surface with linear light,
An imaging step of imaging a light-section line on the measurement surface by the linear light while changing the measurement surface along the longitudinal direction, and generating a plurality of light-section images in which the light-section line extends in the x direction. When,
An arithmetic processing step of detecting the surface defect based on the plurality of light-section images,
Has,
The arithmetic processing step includes:
For each of the light-section images, a depth image generation step of generating a depth image representing the unevenness state of the measurement surface for each x-direction coordinate position,
A binarization process is performed on the depth image, and the depth indicated for each of the x-direction coordinate positions in the depth image is set to a first predetermined value or a second predetermined value based on a predetermined threshold. A binarization processing step of generating a binarized depth image;
Based on the binarized depth image, identify each of the x-direction coordinate positions where a pixel value could not be detected without the light cutting line as a missing value pixel, and determine a pixel value of the missing value pixel as the missing value pixel. A defect image generating unit that generates a defect image in which each of the x-direction coordinate positions of the binarized depth image is represented by the first predetermined value and the second predetermined value as a first predetermined value;
A defect detection step of detecting, as the surface defect, an area having a pixel value of the first predetermined value, based on the plurality of defect images generated along the longitudinal direction of the measurement object. Defect detection method.

Images