JP6119663B2 - Surface defect detection method and surface defect detection apparatus - Google Patents

Description

  The present invention relates to a surface defect detection method and a surface defect detection device for optically detecting a surface defect of a steel material.

  In recent years, in the manufacturing process of steel products, it has been required to detect surface defects of steel materials hot or cold from the viewpoint of improving yield by preventing mass nonconformity. The steel materials described here are steel products such as seamless steel pipes, welded steel pipes, hot-rolled steel sheets, cold-rolled steel sheets, thick steel plates, and other steel products, and are produced in the process of manufacturing these steel products. It means semi-finished products such as slabs. For this reason, as a method for detecting surface defects in steel materials, a method has been proposed in which a billet in the manufacturing process of a seamless steel pipe is irradiated with light to receive reflected light, and the presence or absence of surface defects is determined by the amount of reflected light. (See Patent Document 1). In addition, visible light in multiple wavelength ranges that do not affect each other and do not affect each other with the self-emission emitted from the hot steel material are obliquely symmetric with respect to the normal line of the hot steel surface. A method is also proposed in which an image by synthetic reflected light and an image by individual reflected light are obtained in the normal direction of the surface of the hot steel material, and surface defects of the hot steel material are detected from a combination of these images. (See Patent Document 2).

JP 11-37949 A JP 59-52735 A

  According to the method described in Patent Document 1, since the reflectance of the scale and harmless pattern is different from the reflectance of the ground iron portion, it is possible to erroneously detect a healthy scale or harmless pattern that is not a surface defect as a surface defect. There is sex. For this reason, in the method of patent document 1, a billet and a scale are discriminated using the fact that the shape of a billet is linear. However, the surface defects of the steel material have various shapes such as a circular shape as well as a straight shape. For this reason, it is difficult to apply the method of patent document 1 to the detection process of the surface defect of steel materials. On the other hand, in the method described in Patent Document 2, since there are a large number of types of surface defects, scales, harmless patterns, etc., it is difficult to discriminate scales and harmless patterns from surface defects simply by combining images. . Moreover, it is practically difficult to construct a detection logic corresponding to a large number of image combinations.

  The present invention has been made in view of the above problems, and an object of the present invention is to provide a surface defect detection method and a surface defect detection apparatus capable of accurately distinguishing scales and harmless patterns from surface defects.

  The surface defect detection method according to the present invention is a surface defect detection method for optically detecting a surface defect of a steel material, and is substantially the same from different directions to the same inspection target site using two or more distinguishable light sources. An irradiation step of irradiating illumination light at the same incident angle, and acquiring images by reflected light of each illumination light, extracting a bright part and a dark part of an image obtained by performing a difference process between the acquired images, A determination step of determining the presence or absence of uneven surface defects from the extracted positional relationship between the bright and dark portions and the irradiation direction of the illumination light.

  In the surface defect detection method according to the present invention as set forth in the invention described above, the determination step performs an expansion process on the bright part and dark part images, and extracts an overlapping part of the bright part and dark part images subjected to the expansion process. And calculating a positional relationship between the bright part and the dark part.

  In the surface defect detection method according to the present invention, in the above invention, the determination step performs binarization processing and labeling processing on the image of the bright portion and the dark portion, and compares the gravity center positions of the images subjected to the labeling processing. And calculating a positional relationship between the bright part and the dark part.

  In the surface defect detection method according to the present invention, in the above invention, the determination step emphasizes the bright part and the dark part by performing a filtering process on the image of the bright part and the dark part. The step of calculating the positional relationship of is included.

  In the surface defect detection method according to the present invention, in the above invention, the determination step includes brightness of the bright part and the dark part from a combination of the bright part and the dark part obtained by calculating a positional relationship between the bright part and the dark part. And calculating at least one of the ratio, the area ratio, and the circularity as a feature amount, and determining the presence or absence of uneven surface defects based on the calculated feature amount.

  The surface defect detection apparatus according to the present invention is a surface defect detection apparatus that optically detects a surface defect of a steel material, and is substantially the same from different directions to the same inspection target site using two or more distinguishable light sources. An irradiation unit that irradiates illumination light at the same incident angle, and obtains an image by reflected light of each illumination light, extracts a bright part and a dark part of an image obtained by performing a difference process between the acquired images, And determining means for determining the presence or absence of uneven surface defects from the extracted positional relationship between the bright part and the dark part and the irradiation direction of the illumination light.

  According to the surface defect detection method and the surface defect detection apparatus according to the present invention, it is possible to accurately discriminate between scales, harmless patterns, and surface defects.

FIG. 1 is a schematic diagram showing a configuration of a surface defect detection apparatus according to an embodiment of the present invention. FIG. 2 is a diagram illustrating an example of two two-dimensional images obtained by photographing uneven surface defects, scales, and harmless patterns, and a difference image thereof. FIG. 3 is a diagram showing a shadow when light is irradiated from one side when the surface shape of the examination target part is a concave shape or a convex shape. FIG. 4 is a diagram illustrating an example of a difference image of concave surface defects. FIG. 5 is a flowchart showing a flow of a method for calculating the positional relationship between the bright part and the dark part using the expansion process. FIG. 6 is a diagram illustrating an example of the one-dimensional profile of the difference image and the light / dark pattern. FIG. 7 is a diagram illustrating an example of a two-dimensional image and a one-dimensional profile of a filter. FIG. 8 is a diagram illustrating an example of a difference image and a one-dimensional profile that have been subjected to filter processing using the filter illustrated in FIG. 7. FIG. 9 is a schematic diagram showing a modification of the arrangement position of the light source. FIG. 10 is a schematic diagram showing a light / dark pattern obtained by the arrangement position of the light source shown in FIG. FIG. 11 is a diagram illustrating a result of the surface defect detection process of the example.

  Hereinafter, the configuration and operation of a surface defect detection apparatus according to an embodiment of the present invention will be described with reference to the drawings.

[Configuration of surface defect detection device]
FIG. 1 is a schematic diagram showing a configuration of a surface defect detection apparatus according to an embodiment of the present invention. As shown in FIG. 1, a surface defect detection apparatus 1 according to an embodiment of the present invention is an apparatus that detects a surface defect of a cylindrical steel pipe P that is conveyed in the direction of an arrow in the figure, and includes light sources 2a and 2b, functions A generator 3, area sensors 4a and 4b, an image processing device 5, and a monitor 6 are provided as main components.

  The light sources 2 a and 2 b irradiate the illumination light L that can be discriminated against the same site to be inspected on the surface of the steel pipe P in accordance with a trigger signal from the function generator 3. The light sources 2a and 2b are arranged symmetrically with respect to the inspection target part. That is, the light sources 2a and 2b are shifted by the same incident angle with respect to the normal vector on the surface of the steel pipe P, and are arranged so that the irradiation direction vector of the illumination light L and the normal vector of the steel pipe P surface are on the same plane. Has been.

  Here, the purpose of making the incident angle of the illumination light L the same is to make the optical conditions as equal as possible when discriminating light sources of different incident directions, and to reduce the signal of a healthy part including a scale and a harmless pattern by differential processing described later. There is to be able to do it. However, the signal of the healthy part greatly depends on the surface property of the examination target part, and it is difficult to guarantee the identity of the signal of the healthy part at the same incident angle. Therefore, within the range of 25 to 55 °, even if the incident angles are somewhat different, the same incident angle is expressed as long as the signal of the healthy part can be reduced by the differential processing described later.

  In this embodiment, the number of light sources is two, but the number of light sources may be three or more as long as discrimination is possible. The discriminable light source described here means a light source capable of obtaining the amount of reflected light for each light source with respect to the reflected light obtained from the region to be inspected.

  The area sensors 4 a and 4 b capture a two-dimensional image of the reflected light of the illumination light L emitted from the light sources 2 a and 2 b according to the trigger signal from the function generator 3. The area sensors 4 a and 4 b input captured two-dimensional image data to the image processing device 5. The area sensors 4a and 4b are installed on the normal vector of the region to be inspected as much as possible in a state where each imaging field of view is secured.

  The image processing device 5 is a device that detects a surface defect in a region to be inspected by performing difference processing described later between two two-dimensional images input from the area sensors 4a and 4b. The image processing device 5 outputs to the monitor 6 information related to the two-dimensional images and surface defect detection results input from the area sensors 4a and 4b.

  The surface defect detection apparatus 1 having such a configuration discriminates scales and harmless patterns and uneven surface defects in a region to be inspected by executing the following surface defect detection process. The scale and harmless pattern means a part having a surface film or surface properties with different optical characteristics from a part having a thickness of several to several tens of μm, which becomes a noise factor in surface defect detection processing. Part.

[Surface defect detection processing]
In the surface defect detection process according to an embodiment of the present invention, the image processing apparatus 5 performs calibration using the camera parameters previously derived for the two two-dimensional images input from the area sensors 4a and 4b. After performing image processing such as shading correction and noise removal, a difference image is generated by performing difference processing between the two-dimensional images, and uneven surface defects in the inspection target part are detected from the generated difference image To do.

  Specifically, the luminance value of each pixel constituting the two-dimensional image Ia obtained when the illumination light L is emitted from the light source 2a is Ia (x, y) (where the number of pixels is X × Y, and the x coordinate is 1 ≦ x ≦ X, y coordinate is 1 ≦ y ≦ Y), and the luminance value of each pixel constituting the two-dimensional image Ib obtained when the illumination light L is irradiated from the light source 2b is expressed as Ib (x, y). , The luminance value I_diff (x, y) of each pixel of the difference image I_diff obtained by the difference processing is expressed by the following formula (1).

  Here, examples of the two-dimensional images Ia and Ib and the difference image I_diff obtained by imaging a rough surface defect and a healthy scale and harmless pattern that is not a surface defect are shown in FIGS. 2A, 2B, and 2C, respectively. Shown in As shown in FIGS. 2 (a), 2 (b), and 2 (c), in the healthy part, the surface normal vector, the angle formed by the light source 2a, the surface normal vector, and the light source regardless of the presence or absence of the scale or harmless pattern. Since the angle formed by 2b is equal, luminance value Ia (x, y) = luminance value Ib (x, y), that is, luminance value I_diff (x, y) = 0.

  However, since the surface has a concavo-convex shape in the uneven surface defect portion, there is always a place where the angle formed by the surface normal vector and the light source 2a is not equal to the angle formed by the surface normal vector and the light source 2b. , Luminance value Ia (x, y) ≠ luminance value Ib (x, y), that is, luminance value I_diff (x, y) ≠ 0. Therefore, by generating the difference image I_diff of two two-dimensional images by the differentiator 11, it is possible to remove a sound scale or harmless pattern image that is not a surface defect.

  Next, logic for detecting uneven surface defects from the difference image I_diff will be described. FIGS. 3A and 3B are diagrams showing shadows when illumination light is irradiated from one light source to the inspection target part when the surface shape of the inspection target part is a concave shape and a convex shape. As shown in FIG. 3A, when the surface shape of the inspection target part is a concave shape, the near side of the light source becomes dark due to a decrease in the amount of irradiation light per unit area, and the back side of the light source approaches the regular reflection direction. It becomes brighter. On the other hand, as shown in FIG. 3B, when the surface shape of the region to be inspected is a convex shape, the front side of the light source approaches the specular reflection direction so that it becomes brighter, and the back side of the light source has a convex shape. It becomes a shadow and darkens.

  That is, the brightness / darkness pattern of the reflected light of the illumination light differs depending on whether the surface shape of the region to be inspected is concave or convex. Therefore, the presence or absence of uneven surface defects can be detected by recognizing the light / dark pattern of the reflected light. Therefore, in the following, a method for detecting uneven surface defects by recognizing a light / dark pattern of reflected light will be described. In the following description, it is assumed that concave surface defects are detected among uneven surface defects, but convex surface defects can also be detected by the same logic. The bright part described below means a blob having an area of a predetermined value or more obtained by performing a concatenation process on a pixel whose luminance is a predetermined threshold or more in the difference image I_diff. The dark part described below refers to a blob having an area greater than or equal to a predetermined value obtained by performing concatenation processing on pixels whose luminance is equal to or lower than a predetermined threshold in the difference image I_diff. A blob means a set of labeled pixels.

  In this embodiment, a light / dark pattern is recognized by extracting a bright part and a dark part by performing threshold processing. Specifically, in the surface defect detection apparatus 1 shown in FIG. 1, the light sources 2 a and 2 b are arranged symmetrically with respect to the normal vector of the inspection target part, so that the reflected light caused by the uneven surface shape is reflected. Light and dark patterns occur in the left-right direction. Since the right and left of the light and dark are reversed depending on the order of the difference processing, here, the right is bright and the left is dark, and the right is dark and the left is bright is the convex shape. Accordingly, the difference image I_diff of the concave surface defect is as shown in FIG. Therefore, when the images of the bright part and the dark part are binarized by the luminance threshold values The and -The, respectively, the binarized images I_blight and I_dark of the bright part and the dark part are respectively expressed by the following formula (2).

  Then, after binarizing the image of the bright part and the dark part in this way, performing connection / isolation point removal as necessary, calculating the positional relationship between the bright part and the dark part, the presence or absence of uneven surface defects Is detected. There are various methods for calculating the positional relationship between the bright part and the dark part. In the following, three representative calculation methods will be described, but the positional relation between the bright part and the dark part is calculated even with other calculation methods. I can do it.

  The first positional relationship calculation method is a method of calculating the positional relationship between the bright part and the dark part by performing an expansion / contraction process in a specific direction on the bright part and the dark part. A flowchart of this calculation method is shown in FIG. In the present embodiment, a case will be described in which a bright and dark pattern in which the right is bright and the left is dark in order to detect a concave surface defect is recognized. Light on the right and dark on the left means that there is always a dark part on the left side of the bright part, and there is always a bright part on the right side of the dark part. Therefore, in this calculation method, first, the image processing apparatus 5 performs an expansion process in the right direction with respect to the dark part and performs an expansion process in the left direction with respect to the bright part (steps S1a and S1b). Here, if the images of the bright part and the dark part on which the expansion process has been performed are I_blight_extend and I_dark_extend, respectively, and the expansion length is W, the expansion process is expressed as the following Expression (3). However, the upper left of the two-dimensional image is the origin, the lower direction is positive in the y-axis direction, and the right direction is positive in the x-axis direction.

  In the present embodiment, the bright portion and the dark portion are expanded by the same length W, but the expanded length W is not necessarily the same. In extreme cases, only the bright portion and the dark portion are included. On the other hand, an expansion process may be performed. Further, the expanding length W depends on the size of the surface defect to be detected.

  Next, the image processing apparatus 5 performs an expansion process by performing an AND process on the bright and dark images I_blight_extend and I_dark_extend that have been subjected to the expansion process as shown in Equation (4) below. The overlapping part of the bright part and dark part images I_blight_extend and I_dark_extend is extracted as a defect candidate part image I_defect (steps S2a and S2b).

  Next, the image processing apparatus 5 generates a defect candidate blob I_defect_blob by performing a labeling process on the obtained defect candidate part images I_defect after performing a connection / isolated point removal process as necessary. (Step S3). Then, the image processing apparatus 5 extracts the feature amount of each defect candidate blob I_defect_blob, and determines whether each defect candidate blob I_defect_blob is a concave surface defect based on the extraction result (steps S4a and 4b). . Note that in order to investigate the feature amount of the defect candidate blob I_defect_blob, information on the bright part and the dark part is necessary, so the bright part and the dark part are restored from the defect candidate blob I_defect_blob.

  Specifically, since the bright part always exists on the right side of the defect candidate part and the dark part always exists on the left side, the image processing apparatus 5 starts from the center of gravity of the defect candidate blob I_defect_blob as the starting point, and the dark part binarized image I_dark. To the left, and the first found blob is a dark defect candidate blob I_dark_blob. Similarly, the image processing apparatus 5 searches the right part binarized image I_blight on the right side starting from the center of gravity of the defect candidate blob I_defect_blob, and sets the first found blob as the bright part defect candidate blob I_blight_blob. Then, the image processing apparatus 5 extracts the feature amount from the bright part defect candidate blob I_blight_blob and the dark part defect candidate blob I_dark_blob restored in this way, and each defect candidate blob I_defect_blob is a concave surface defect based on the extracted feature quantity. It is determined whether or not. Since the specific feature amount varies depending on the defect, an example will be given in an embodiment described later without being described here.

  In the second positional relationship calculation method, after performing the above-described threshold processing and performing connection / isolated point removal processing as necessary, the bright portion and the dark portion are extracted and labeling is performed, and the positions of the bright portion and the dark portion are determined. Recognizing the relationship detects a concave surface defect. Specifically, first, the image processing apparatus 5 individually recognizes the bright part and the dark part by labeling, and obtains the gravity center information of the bright part and the dark part. Next, the image processing apparatus 5 determines whether the center of gravity of the dark part exists within a predetermined range on the right side of each bright part from the center of gravity information of the bright part and the dark part. And when the gravity center of a dark part exists, the image processing apparatus 5 recognizes the combination of a bright part and a dark part which are a pair as a bright and dark pattern, and performs feature quantity analysis of the bright and dark pattern, thereby detecting a concave surface defect. It is determined whether or not there is. Here, the light / dark pattern is recognized using the center-of-gravity information. However, as long as the information of the bright part and the dark part can be grasped (for example, the upper end position and the lower end position), the information used for recognizing the light / dark pattern is not necessarily the center of gravity information Not necessary.

  In the third positional relationship calculation method, a concave surface defect is detected by recognizing a light / dark pattern using a filter without performing the above threshold processing. Specifically, in the surface defect detection apparatus 1 shown in FIG. 1, since the light sources 2a and 2b are arranged symmetrically with respect to the normal line of the inspection target part, the light and dark pattern resulting from the surface unevenness is in the horizontal direction. Occurs. FIGS. 6A and 6B are diagrams showing an example of a difference image and a one-dimensional profile of a light and dark pattern in the line segment L shown in FIG.

  As shown in FIGS. 6 (a) and 6 (b), the concave surface defects are bright on the right and dark on the left, so the one-dimensional profile of the light / dark pattern is characteristic of a mountain shape on the right side and a valley shape on the left side. Become a one-dimensional profile. Therefore, in the present embodiment, a filter H having a mountain shape on the right side and a valley shape on the left side is created in advance, and the filter H is applied to the difference image I_diff as shown in the following formula (5), whereby a high frequency is obtained. A two-dimensional image I_cont in which noise is reduced and only a light / dark pattern is enhanced is generated.

  FIGS. 7A and 7B are diagrams showing an example of a two-dimensional image of the filter H created in advance and a one-dimensional profile thereof in the horizontal direction. FIGS. 8A and 8B are views showing a difference image subjected to the filter processing using the filter H shown in FIGS. 7A and 7B and a one-dimensional profile thereof in the left-right direction. As shown in FIGS. 8A and 8B, it can be seen that a two-dimensional image in which high-frequency noise is reduced and only the bright and dark pattern is enhanced is obtained.

  If necessary, it may be possible to cope with many surface defect sizes by preparing several types of filters having different ranges in the width direction. The image processing apparatus 5 performs the threshold processing after performing the connection / isolated point removal processing on the binary image in which the light / dark pattern is emphasized in this way as necessary, thereby obtaining the defect candidate portion image I_defect. Extract. Then, the image processing apparatus 5 detects a concave surface defect by performing the same processing as the first positional relationship calculation method on the extracted defect candidate portion image I_defect.

  As is apparent from the above description, the surface defect detection processing according to an embodiment of the present invention uses the two light sources 2a and 2b that can be distinguished to substantially the same incident angle from different directions to the same inspection target site. The illumination light L is irradiated and the images of the reflected light of each illumination light L are acquired, and the bright part and the dark part of the image obtained by performing the difference process between the acquired images are extracted and extracted. Since the presence or absence of uneven surface defects is determined from the positional relationship between the bright part and the dark part and the irradiation direction of the illumination light L, it is possible to accurately discriminate between scales and harmless patterns and uneven surface defects.

  In the present embodiment, the left and right light and dark patterns are recognized because the light sources are installed symmetrically. However, even if the installation position of the light sources is not left and right, and is not symmetrical or symmetric, the same processing is performed. Defects can be detected. Specifically, when the light sources are arranged vertically symmetrically, the light / dark pattern only changes from the left / right direction to the vertical direction. Defects can be detected.

  Also, as shown in FIG. 9, when the light sources 2a and 2b are installed so that the irradiation direction of the illumination light differs by 90 degrees, if the surface defect is concave, the near side of the light source is dark and the back side is bright, and the surface defect If is convex, the front side of the light source is bright and the back side is dark. Specifically, when the surface defect has a concave shape, the two-dimensional image obtained by the illumination light from the light source 2a is as shown in FIG. 10A, and the two-dimensional image obtained by the illumination light from the light source 2b. Is as shown in FIG. For this reason, the difference image becomes a bright and dark pattern having contrast from the lower left to the upper right as shown in FIG. Therefore, if the light and dark pattern is rotated by 45 degrees, a concave surface defect can be detected by the same method as the light and dark pattern in the left-right direction. Furthermore, since three or more patterns of difference images can be obtained by using three or more light sources, the surface defect detection accuracy can be further improved.

  Further, in the present embodiment, the uneven surface defect is detected when the illumination light is irradiated from the direction that is symmetric with respect to the normal line of the inspection target part, but the irradiation direction of the illumination light is not necessarily symmetric. . Moreover, the surface defect detection process of this embodiment can be applied to the entire production line of steel regardless of whether it is hot or cold.

  In the present embodiment, the surface defect detection process using the first positional relationship calculation method is applied to the inspection target portion where the pit ridge is formed and the healthy inspection target portion where the pit ridge is not formed. In this example, the brightness ratio, area ratio, and circularity of the bright part and the dark part were calculated as the feature amount. The circularity is a value obtained by dividing the area of the bright part and the dark part by the square of the circumference of the circumference, and is used when determining whether the shape of the bright part and the dark part is close to a circular shape. . If the surface defect has the same cause, it is unlikely that the luminance and area are significantly different between the left and right signals, and the detection accuracy of the surface defect is improved by evaluating the left and right balance using the luminance ratio and area ratio. Further, in order to evaluate the shadow, the bright part and the dark part rarely have a circular shape, and it is possible to determine that a thing close to the circular shape is caused by another, so the circularity is incorporated into the feature amount. In addition, the areas of the bright part and the dark part are calculated so that only surface defects whose area is a predetermined value or more can be detected. The detection results are shown in FIG. As shown in FIG. 11, according to the present Example, it was confirmed that the pit ridge and the healthy part in which the pit ridge was not formed can be distinguished with high accuracy.

  The embodiment to which the invention made by the present inventors is applied has been described above, but the present invention is not limited by the description and the drawings that form part of the disclosure of the present invention according to this embodiment. That is, other embodiments, examples, operational techniques, and the like made by those skilled in the art based on this embodiment are all included in the scope of the present invention.

DESCRIPTION OF SYMBOLS 1 Surface defect detection apparatus 2a, 2b Light source 3 Function generator 4a, 4b Area sensor 5 Image processing apparatus 6 Monitor L Illumination light P Steel pipe

Claims (15)

A surface defect detection method for optically detecting a surface defect of a steel material,
An irradiation step of irradiating illumination light at substantially the same incident angle from different directions to the same inspection target site using two or more distinguishable light sources,
Acquiring an image by the reflected light of each illumination light obtained by extracting a bright portion and a dark portion adjacent the image obtained by performing difference processing between images, the bright portion and dark portion adjacent which said extracted A determination step of determining the presence or absence of uneven surface defects from the positional relationship and the irradiation direction of the illumination light,
A method for detecting surface defects, comprising:   The determination step includes calculating a positional relationship between the bright part and the dark part by performing an expansion process on the bright part and the dark part images and extracting an overlapping part of the bright part and dark part images that have undergone the expansion process. The surface defect detection method according to claim 1, comprising:   In the determination step, a binarization process and a labeling process are performed on the images of the bright part and the dark part, and a positional relationship between the bright part and the dark part is calculated by comparing the barycentric positions of the labeled images. The surface defect detection method according to claim 1, comprising:   The determining step includes a step of calculating a positional relationship between the bright part and the dark part by emphasizing the bright part and the dark part by performing a filtering process on the image of the bright part and the dark part. The surface defect detection method according to claim 1.   The determination step includes at least one of a brightness ratio, an area ratio, and a circularity of a bright part and a dark part from a combination of the bright part and the dark part obtained by calculating a positional relationship between the bright part and the dark part. The surface defect according to any one of claims 1 to 4, further comprising a step of calculating as a feature amount and determining the presence or absence of an uneven surface defect based on the calculated feature amount. Detection method. A surface defect detection device for optically detecting a surface defect of a steel material,
Irradiation means for irradiating illumination light at substantially the same incident angle from different directions to the same inspection target site using two or more distinguishable light sources;
Acquiring an image by the reflected light of each illumination light obtained by extracting a bright portion and a dark portion adjacent the image obtained by performing difference processing between images, the bright portion and dark portion adjacent which said extracted Determining means for determining the presence or absence of uneven surface defects from the positional relationship and the irradiation direction of the illumination light,
A surface defect detection apparatus comprising: A surface defect detection method for optically detecting a surface defect of a steel material,
An irradiation step of irradiating illumination light at substantially the same incident angle from different directions to the same inspection target site using two or more distinguishable light sources,
The image obtained by the reflected light of each illumination light is acquired, the difference between the acquired images is extracted, the bright part and the dark part of the image obtained are extracted, the positional relationship between the extracted bright part and the dark part and the illumination A determination step of determining the presence or absence of uneven surface defects from the light irradiation direction,
The determination step includes calculating a positional relationship between the bright part and the dark part by performing an expansion process on the bright part and the dark part images and extracting an overlapping part of the bright part and dark part images that have undergone the expansion process. A method for detecting surface defects, comprising: A surface defect detection method for optically detecting a surface defect of a steel material,
An irradiation step of irradiating illumination light at substantially the same incident angle from different directions to the same inspection target site using two or more distinguishable light sources,
The image obtained by the reflected light of each illumination light is acquired, the difference between the acquired images is extracted, the bright part and the dark part of the image obtained are extracted, the positional relationship between the extracted bright part and the dark part and the illumination A determination step of determining the presence or absence of uneven surface defects from the light irradiation direction,
In the determination step, a binarization process and a labeling process are performed on the images of the bright part and the dark part, and a positional relationship between the bright part and the dark part is calculated by comparing the barycentric positions of the labeled images. A method for detecting surface defects, comprising: A surface defect detection method for optically detecting a surface defect of a steel material,
An irradiation step of irradiating illumination light at substantially the same incident angle from different directions to the same inspection target site using two or more distinguishable light sources,
The image obtained by the reflected light of each illumination light is acquired, the difference between the acquired images is extracted, the bright part and the dark part of the image obtained are extracted, the positional relationship between the extracted bright part and the dark part and the illumination A determination step of determining the presence or absence of uneven surface defects from the light irradiation direction,
The determining step includes a step of calculating a positional relationship between the bright part and the dark part by emphasizing the bright part and the dark part by performing a filtering process on the image of the bright part and the dark part. Surface defect detection method. The determination step includes at least one of a brightness ratio, an area ratio, and a circularity of a bright part and a dark part from a combination of the bright part and the dark part obtained by calculating a positional relationship between the bright part and the dark part. The surface defect according to any one of claims 7 to 9, further comprising a step of calculating as a feature amount and determining the presence or absence of a concavo-convex surface defect based on the calculated feature amount. Detection method. A surface defect detection method for optically detecting a surface defect of a steel material,
An irradiation step of irradiating illumination light at substantially the same incident angle from different directions to the same inspection target site using two or more distinguishable light sources,
The image obtained by the reflected light of each illumination light is acquired, the difference between the acquired images is extracted, the bright part and the dark part of the image obtained are extracted, the positional relationship between the extracted bright part and the dark part and the illumination A determination step of determining the presence or absence of uneven surface defects from the light irradiation direction,
The determination step includes at least one of a brightness ratio, an area ratio, and a circularity of a bright part and a dark part from a combination of the bright part and the dark part obtained by calculating a positional relationship between the bright part and the dark part. As a feature quantity, and determining the presence or absence of uneven surface defects based on the computed feature quantity. A surface defect detection device for optically detecting a surface defect of a steel material,
Irradiation means for irradiating illumination light at substantially the same incident angle from different directions to the same inspection target site using two or more distinguishable light sources;
The image obtained by the reflected light of each illumination light is acquired, the difference between the acquired images is extracted, the bright part and the dark part of the image obtained are extracted, the positional relationship between the extracted bright part and the dark part and the illumination Determination means for determining the presence or absence of uneven surface defects from the light irradiation direction,
The determination means performs a dilation process on the bright part and dark part images, and calculates a positional relationship between the bright part and the dark part by extracting an overlapped part of the dilated bright part and dark part images. A surface defect detection apparatus comprising: A surface defect detection device for optically detecting a surface defect of a steel material,
Irradiation means for irradiating illumination light at substantially the same incident angle from different directions to the same inspection target site using two or more distinguishable light sources;
The image obtained by the reflected light of each illumination light is acquired, the difference between the acquired images is extracted, the bright part and the dark part of the image obtained are extracted, the positional relationship between the extracted bright part and the dark part and the illumination Determination means for determining the presence or absence of uneven surface defects from the light irradiation direction,
The determination means performs binarization processing and labeling processing on the images of the bright portion and the dark portion, and calculates a positional relationship between the bright portion and the dark portion by comparing the barycentric positions of the labeled images. A surface defect detection apparatus comprising: A surface defect detection device for optically detecting a surface defect of a steel material,
Irradiation means for irradiating illumination light at substantially the same incident angle from different directions to the same inspection target site using two or more distinguishable light sources;
The image obtained by the reflected light of each illumination light is acquired, the difference between the acquired images is extracted, the bright part and the dark part of the image obtained are extracted, the positional relationship between the extracted bright part and the dark part and the illumination Determination means for determining the presence or absence of uneven surface defects from the light irradiation direction,
The determination means includes means for calculating a positional relationship between the bright part and the dark part by emphasizing the bright part and the dark part by performing a filtering process on the image of the bright part and the dark part. Surface defect detection device. A surface defect detection device for optically detecting a surface defect of a steel material,
Irradiation means for irradiating illumination light at substantially the same incident angle from different directions to the same inspection target site using two or more distinguishable light sources;
The image obtained by the reflected light of each illumination light is acquired, the difference between the acquired images is extracted, the bright part and the dark part of the image obtained are extracted, the positional relationship between the extracted bright part and the dark part and the illumination Determination means for determining the presence or absence of uneven surface defects from the light irradiation direction,
The determination means includes at least one of a brightness ratio, an area ratio, and a circularity of a bright part and a dark part from a combination of the bright part and the dark part obtained by calculating a positional relationship between the bright part and the dark part. As a feature amount, and includes means for determining the presence or absence of uneven surface defects based on the calculated feature amount.