JPH0886633A - Surface defect inspecting device - Google Patents

Abstract

PURPOSE: To closely inspect an object to be inspected for surface defect by providing an image pickup means which converts a received light image into image data, bright- and-dark pattern discriminating means which discriminates the brightness and darkness of the image by processing the image data, bright-and-dark area processing means which enlarges obtained bright areas and dark areas, image emphasizing means, defect detecting means, etc. CONSTITUTION: A camera control unit 4 generates the image signal of a received light image picked up with a video camera 2 and outputs the image signal to an image processor 5. The processor 5 performs a bright-and-dark pattern discriminating process, image emphasizing process, image area process, and defect detecting process on the original image. In order to recognize stripes from the original image, low-frequency components are extracted from the original image and binarized as a threshold to the original image and the brightness and darkness of the stripes are separated and extracted. In the image emphasizing process, signals from which only defective components are removed by smoothing are subtracted from the original image signal so as to offset the low-frequency and stripe components. In the defect detecting process, the image-emphasized results and bright-and-dark pattern discriminated results are ANDed so that only defects can be surely detected.

Description

Detailed Description of the Invention

[0001]

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

[0002]

2. Description of the Related Art As a conventional surface defect inspection apparatus, for example, JP-A-2-73139 and JP-A-5-45142 are available.
There is one disclosed in Japanese Patent No. 45144 and the like. These are light-receiving images that show a predetermined light and dark stripe (stripe) pattern on the surface to be inspected, and if there are defects such as irregularities on the surface to be inspected, the difference in lightness (luminance) and the change in lightness (luminance) due to the defect. Is used to detect defects on the surface of the surface to be inspected. Further, in the above-mentioned conventional example, there is something that is not a defect such as a processed portion on the surface to be inspected, and when it is in the received light image, the brightness level at the defect and the brightness level at a non-defective portion such as the processed area A technique for distinguishing a defect from other parts by utilizing the difference in

[0003]

However, the conventional surface defect inspection apparatus as described above has the following problems. For example, in painting automobile bodies,
What is called a surface defect is a convex portion on the surface of the coating that is produced as a result of coating on which dust or the like has adhered, for example, a diameter of about 0.5 mm to 2 mm and a thickness of about several tens of μm. . The height (thickness) of this degree of protrusion is small, though the diameter is small.
Is relatively large, the angle of diffuse reflection of light is large, and it is easy to see.

On the other hand, there are irregularities that do not become defects. That is, since the concentration of the paint is not strictly constant due to eddy convection that occurs in the process of evaporation of the paint solvent, extremely thin (low) unevenness is periodically generated in the thickness of the coating film. This unevenness has, for example, a mountain-to-mountain interval of 1 to
The height of the unevenness is about 10 μm, and the height of the unevenness is about several μm. Such extremely thin irregularities are usually not noticeable and do not become defects. However, since it may look like "Yuzu" or the surface of orange under the control of light, it is so-called "Yuzu skin" or "Orange skin". In the conventional example as described above, since the change in brightness is emphasized and detected, extremely thin unevenness that does not cause a defect such as the above-mentioned "discolored skin" is also detected, and this may be erroneously determined to be a defect.

For example, in the above-mentioned Japanese Laid-Open Patent Publication No. 2-73139, the stripe pattern has a narrow interval (1.5 mm).
(Below) Since the defect detection processing is performed using illumination, the stripe itself and its boundary (line) are greatly disturbed when the above-mentioned "Yuzu skin" is formed on the surface to be inspected. The black part becomes an isolated point,
False detection is likely to occur when the stripe width on the image is not uniform. As mentioned above, in the prior art,
There is a problem in that extremely thin unevenness that does not cause a defect such as so-called “citrus skin” may be erroneously detected as a defect.

The present invention has been made to solve the problems of the prior art as described above, and is what is called "Yuzu skin".
It is an object of the present invention to provide a surface defect inspection apparatus capable of performing more accurate defect detection without erroneously detecting an extremely thin unevenness that does not cause such a defect as a defect.

[0007]

In order to achieve the above object, the present invention is constructed as described in the claims. FIG. 1 is a diagram corresponding to the claims of the present invention, and (a) corresponds to claim 1. FIG.
In (a), 100 is a surface to be inspected, for example, a painted surface. Reference numeral 101 denotes an illuminating device that projects a predetermined bright and dark pattern on the surface to be inspected, and is, for example, an illuminating device shown in FIG. 4 described later. Reference numeral 102 denotes an image pickup means for picking up an image of the surface to be inspected and converting the light / dark pattern into image data of an electric signal, which is, for example, a video camera such as a CCD camera. Further, 103 is a light and dark pattern identifying means for processing the image data obtained by the image pickup means to identify a light portion and a dark portion, and 104 is processing for enlarging the area of the dark portion obtained by the light and dark pattern identifying means. Bright and dark area processing means,
Reference numeral 105 denotes an image enhancing means for extracting only a component having a high frequency component out of the frequency components in the image data and having a level equal to or higher than a predetermined value, and 106 denotes a bright portion in the bright and dark region processing means and a component extracted by the image enhancing means. This is a defect detection means for detecting a portion in which both of and exist as a defect, and these portions 103 to 106 are composed of, for example, a computer. The above-described configuration corresponds to, for example, the embodiment described with reference to FIGS.

Further, as described in claim 2, the light and dark area processing means enlarges the area of the dark portion by a predetermined value determined in advance according to the roughness of the surface to be inspected. Alternatively, as described in claim 3, the light and dark area processing means enlarges the area of the dark portion by a value corresponding to the roughness of the surface to be inspected obtained from the image data obtained by the image pickup means. . Alternatively, as described in claim 4, the light / dark area processing means temporarily enlarges the area of the dark portion and then reduces the area of the dark portion. Further, as described in claim 5, the image enhancing unit obtains a difference between an original image of the image data obtained by the image capturing unit and an image obtained by performing a predetermined smoothing process on the original image, The result is binarized with a predetermined threshold value. Alternatively, as described in claim 6, the image emphasizing means smoothes the original image of the image data obtained by the imaging means with a normal function, and then second-order differentiates the result to be 2 with a predetermined threshold value.
It is to be valued. Alternatively, as described in claim 7, the image emphasizing means performs a predetermined maximum value filter processing on the original image of the image data obtained by the image capturing means, and then performs an minimum value filter processing, The difference from the original image is obtained, and the result is binarized with a predetermined threshold value. Note that the maximum value filtering process and the minimum value filtering process are a kind of smoothing process, as will be described later in detail, and the maximum value filtering process is the maximum value of the luminance in the peripheral pixels including the target pixel. And the minimum value filtering process is a process in which the minimum value of the brightness in the peripheral pixels including the target pixel is set as the new brightness value of the target pixel.

Further, as described in claim 8, the light and dark pattern identifying means performs a smoothing process on the original image of the image data obtained by the image pickup means, and the result is used as a threshold value for lightening. Binarization, that is, recognition of a portion and a dark portion is performed.

Alternatively, as described in claim 9, the light and dark pattern identifying means performs maximum value filtering processing or minimum value filtering processing on the original image of the image data obtained by the imaging means, and the result is obtained. The threshold value is used as a threshold value to binarize the bright part and the dark part, that is, to recognize.
Alternatively, as described in claim 10, the light and dark pattern identifying means performs digital low-pass filter processing of a predetermined coefficient on the original image of the image data obtained by the image capturing means, and the result is set as a threshold value. Is for binarizing the bright part and the dark part, that is, recognizing. According to the invention of claim 11, in the binarization of the image emphasizing means and the bright and dark pattern identifying means, the luminance histogram of the original image of the image data obtained by the imaging means or the maximum of one predetermined line is obtained. The brightness value is obtained, and the binarization threshold value is adjusted based on the result.

Next, FIG. 1 (b) corresponds to claim 12. In FIG. 1B, 100 is a surface to be inspected, for example, a painted surface. Reference numeral 101 denotes an illuminating device that projects a predetermined light and dark pattern on the surface to be inspected, and is, for example, an illuminating device shown in FIG. 3 described later. Reference numeral 102 denotes an image pickup means for picking up an image of the surface to be inspected and converting the light / dark pattern into image data of an electric signal, which is, for example, a video camera such as a CCD camera. Further, 107 is a smoothing means for smoothing the image data obtained by the image pickup means, 10
Reference numeral 8 denotes an emphasizing means for extracting a portion having a brightness change in the image data of the light-dark pattern smoothed by the smoothing means, 109
Is a binarizing unit for binarizing the result of the emphasizing unit, 110
Is an area processing means for enlarging the area of the value portion corresponding to the portion having the luminance change in the binary signal obtained by the binarizing means, and 111 is the area obtained by the area processing means. Is an area determination processing means for calculating the area of each area in the screen where there is a change in brightness and detecting the area whose area is less than a predetermined value as a defect. These 107-111
The part of is composed of, for example, a computer. The above configuration corresponds to, for example, the embodiment described later with reference to FIGS. 27 to 32.

Further, according to a thirteenth aspect, the area processing means forms an area of a value portion corresponding to a portion where the luminance changes by a predetermined value which is predetermined according to the roughness of the surface to be inspected. It will expand. Alternatively, as described in claim 14, the area processing means is a value portion corresponding to a portion having the brightness change by a value corresponding to the roughness of the surface to be inspected obtained from the image data obtained by the image pickup means. To expand the area of. Further, as described in claim 15, the area processing means temporarily enlarges the area of the value portion corresponding to the portion having the luminance change, and then performs the processing of reducing the area.

According to a sixteenth aspect of the present invention, the area determination means performs the area determination processing after removing a predetermined area in contact with the peripheral boundary of the screen in the binarized image. Alternatively, as described in claim 17, the area determination means makes the area determination after making one pixel, which is in contact with a peripheral boundary of the screen in the binarized image, equal to the portion having the luminance change. Is.

Further, as described in the eighteenth aspect, the illuminating means projects a pattern consisting of parallel stripes and a line diagonally crossing the stripes as a light and dark pattern.

According to a nineteenth aspect of the present invention, the illuminating means projects a bright and dark pattern in stripes on the surface to be inspected. This structure corresponds to, for example, the embodiment shown in FIGS. Alternatively, as described in claim 20, the illuminating means projects a grid-like bright-dark pattern on the surface to be inspected. This structure corresponds to, for example, the embodiment shown in FIG. 17 described later. Moreover, as described in claim 21,
The illuminating means is such that the distance between the bright and dark parts of the light-dark pattern, the ratio and the distance from the illuminating means to the surface to be inspected are based on the curvature of the surface to be inspected and the angle between the expected defect and the surface to be inspected It is set. In addition, claim 2
In the invention described in item 2, there is provided means for performing a labeling process and a defect area and barycentric coordinate calculation process based on the defect data obtained by the defect detection means. Further, in the invention described in claim 23, the plurality of image pickup means and the plurality of illumination means are arranged in an arch shape around the object to be inspected so that a large surface to be inspected of the object to be inspected is simultaneously inspected. It is configured in. This structure corresponds to, for example, the embodiment shown in FIG.

[0016]

As described above, according to the first aspect of the invention, a predetermined light and dark pattern is projected on the surface to be inspected by the illumination means, and the light and dark pattern is picked up by the image pickup means, and the light and dark pattern is converted into image data of electric signals. Convert. Next, the light and dark pattern identifying means processes the light and dark pattern image to identify the light portion and the dark portion. In this processing, for example, by binarizing the image signal with a predetermined threshold value, it is possible to distinguish between a bright portion above the threshold value and a dark portion below the threshold value. next,
The bright / dark area processing means performs the processing of enlarging the area of the dark portion distinguished as described above (the same is true for the processing of reducing the area of the bright portion). This will be explained in detail later,
The reason why “Yuzu skin” is erroneously detected as a defect is that the boundary between the light and dark stripes is disturbed by the “Yuzu skin” and an isolated point is generated. The dark portion prevents the defect from being detected in such a portion. Next, the image enhancing means extracts only the high frequency component of the frequency components in the image data of the light and dark pattern and the level of which is equal to or higher than a predetermined value. The high frequency components in the above image data are noise and a portion with a change in brightness such as a boundary portion of a defect or stripe,
As described above, by extracting only the component whose level is equal to or higher than the predetermined value, the noise component can be removed and only the boundary between the defect and the stripe can be obtained. Next, the defect detection unit detects, as a defect, a portion in which both the bright portion in the light and dark area processing unit and the component extracted by the image enhancement unit are present. As described above, the bright portion in the light and dark area processing means is obtained by removing the affected portion due to “Yuzu skin”, so if the AND of the light portion of the light and dark area processing means and the extracted component of the image enhancing means is obtained. , It is possible to eliminate the influence of "Yuzu skin", and to distinguish only regions such as processed parts and extract only true defects. As stated above, claim 1
The invention described in (1) detects defects that appear black in the bright portion of the bright-dark stripe image, and does not detect defects that exist in the dark portion of the stripe image. Therefore, in order to inspect the entire surface to be inspected, the object to be inspected or the illumination means and the image pickup means are sequentially moved so that the bright portion scans the entire surface to be inspected.

Next, claims 2 to 4 show a configuration example of the light and dark area processing means, and claim 2 is only a predetermined value which is predetermined according to the roughness of the surface to be inspected. According to the third aspect, the area of the dark portion is enlarged, and the area of the dark portion is enlarged by a value corresponding to the roughness of the surface to be inspected obtained from the image data obtained by the image pickup means. In addition, claim 4
Is a process of once expanding the area of the dark portion and then reducing the area of the dark portion. As described above, once the area of the dark part is enlarged and the boundary line is disturbed by the effect of "Yuzu skin", or the area where an isolated point is generated is taken into the area of the dark part, Even if the image processing for reducing the area is performed, the above disorder and isolated points are not reproduced.
Therefore, by performing the enlarging process and the reducing process as described above, the effect of "discolored skin" can be erased and the stripe portion can be returned to the initial state.

Next, claims 5 to 7 show examples of the structure of the image enhancing means, and claims 8 to 1
Reference numeral 0 indicates an example of the structure of the light / dark pattern identifying means. The eleventh aspect of the present invention shows a configuration example of the binarization threshold values of the image enhancing means and the light and dark pattern identifying means. Next, in the invention according to claim 12, a predetermined bright and dark pattern is projected on the surface to be inspected by the illumination means,
The image is picked up by an image pickup means to convert the light-dark pattern into image data of an electric signal. Next, the smoothing means smoothes the image data obtained by the imaging means. This smoothing removes a minute luminance change component due to noise or the like. Next, the emphasizing means extracts a portion having a brightness change from the image data of the light-dark pattern smoothed by the smoothing means. This emphasis process is, for example, a differentiation process. Then 2
The binarizing means binarizes the result of the emphasis processing. This 2
By the binarization process, a region having a change in brightness such as a defective portion or a stripe boundary portion is extracted. Next, the area processing means performs processing of enlarging the area of the value portion corresponding to the portion having the luminance change in the binary signal obtained by the binarizing means. This is “Yuzu skin”, as will be described later in detail.
The reason for erroneously detecting a defect as "defect" is that the bright and dark boundaries of the stripes are disturbed by "Yuzu skin" and an isolated point is generated. I decided not to do it. Next, the area determination processing means calculates each area obtained by the area processing means to obtain an area for each portion in the screen where there is a change in luminance, and the area is below a predetermined value. The part that was there is detected as a defect. That is, as a portion having a change in luminance, a bright / dark boundary portion of a stripe and a defective portion are detected,
Since the area of the defective portion is significantly smaller than the area of the light-dark boundary portion of the stripe, only the defective portion can be extracted by distinguishing by the predetermined area.
Further, since the area where the "discolored skin" is likely to occur is removed by the area processing means, there is no possibility of erroneously detecting the "discolored skin" portion as a defective portion. Similarly, even if there is a processed portion or the like on the surface to be inspected, since the area of the processed portion is sufficiently large for the defect, it is not erroneously detected.

According to the twelfth aspect of the present invention, both a defect appearing black in the bright portion and a defect appearing white in the dark portion of the bright-dark stripe image are detected. However, since it is not possible to detect defects in the bright and dark boundary areas, in order to inspect the entire surface to be inspected, the object to be inspected or the illuminating means and the image pickup means are sequentially moved in the same manner as in claim 1.
The bright part and the dark part (the part excluding the boundary region) are configured to scan the entire surface to be inspected. However, since the area in which defect inspection can be performed in one screen is wider than in the case of claim 1, the inspection can be performed quickly.

Further, claims 13 to 15 show an example of the area processing means, and are the same as the claims 2 to 4. In addition, claims 16 to 18,
The invention according to claim 12 relates to means for eliminating an error at the time of area determination when the surface to be inspected is a curved surface, and claim 16 deletes a portion where erroneous detection is likely to occur, Reference numeral 17 is for connecting all stripe boundary portions by image-processing one pixel around the screen, and claim 18 is for connecting each stripe in advance in the stripe pattern of the lighting device. .

[0021] Claims 19 to 21 show examples of the structure of the illuminating means. In addition, claim 22
Shows the configuration of post-processing means after defect detection.
A twenty-third aspect of the present invention shows a structure for simultaneously inspecting a wide surface to be inspected of an object to be inspected, for example, a structure for performing a coating surface inspection in a coating process of an automobile body.

[0022]

DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will be described below with reference to the drawings.
FIG. 2 is a diagram showing an embodiment of the present invention. In FIG. 2, reference numeral 1 denotes an illuminating device, which is arranged so as to irradiate the surface 3 to be inspected with light of a predetermined bright and dark pattern. Reference numeral 2 denotes a video camera (for example, a CCD camera), which is arranged so as to capture an image of the surface 3 to be inspected on which the light and dark patterns are displayed. Further, 4 is a camera control unit, in which an image signal of a light-receiving image captured by the video camera 2 is generated and output to the image processing device 5. Reference numeral 6 denotes a host computer, which has a function of externally displaying or outputting control of the image processing apparatus 5 and processing results. A monitor 7 displays a screen imaged by the video camera 2.

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

Next, FIG. 4 is an exploded perspective view showing the details of the illuminating device 1. In the present embodiment, description will be made on the assumption that illumination with a bright and dark stripe pattern is used. In FIG. 4, reference numeral 1a is a light source, and the light is diffused by the diffusion plate 1b and is irradiated onto the surface to be inspected through the stripe plate 1c. The diffusion plate 1b is, for example, like frosted glass, and uniformly irradiates the surface 3 to be inspected with light. Stripe board 1
c is a transparent or diffuser plate with black stripes arranged at predetermined intervals.

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

Next, the principle of displaying a defect on the surface of the object to be inspected in the received light image by using the above stripe illumination will be described with reference to FIGS. 6 to 8. In FIG. 6, it is assumed that there is a convex defect 13 on the surface 3 to be inspected.
The point P1 is an arbitrary point on the normal surface having no defect, and the point P2 is an arbitrary point on the defect 13. When the defect 13 is convex as shown in the figure, each arbitrary point on the defect 13 has an angle formed by a tangent line of the point and a normal surface to be inspected, and this angle at the point P2 is referred to as an inclination angle θ. To do. In this case, the vicinity of the defect 13 and the point P1 is in the bright stripe of the stripe illumination (the portion where light is irradiated).
It is assumed that When the angle formed by the video camera 2 and the surface 3 to be inspected is an incident angle θi, a point P which is a normal plane
In 1, the light is specularly reflected (θi), and since there is a bright portion of the stripe illumination in that direction, the point P1 is projected by the video camera 2 as a bright (white) portion. However, at the point P2 on the defect 13, the incident light from the video camera 2 is not specularly reflected but diffusely reflected due to the inclination angle θ, and since there is a black stripe in that direction, the point P2 is displayed as black by the video camera 2. If the irregular reflection angle at this time is θh, the defect point P
Since the tilt angle θ at 2 and the angle (incident angle) in a certain direction of the video camera 2 are θi, θh = θi + 2θ (Equation 1) (see FIG. 7). That is, if there is a bright (white) portion of the stripe in the direction of the angle θh, it is displayed as white, and if there is a black portion of the stripe, it is displayed as black (dark). Also,
As described above, since it is specularly reflected on a normal plane, it can be expressed as θh = θi (Equation 2). Therefore, from the equations (1) and (2), the irregular reflection due to the defect can be expressed as 2θ based on the regular reflection direction θi. In the above description, the video camera 2 is used as the starting point, but the lighting device 1 is also used as the starting point. Further, FIG. 7 is an example of a case where the point P2 is on the slope on the right side of the defect, but the concept is the same even when P2 is on the slope on the left, and therefore the description is omitted.

Next, the case where the surface to be inspected is a curved surface is shown in FIG.
Will be explained. As shown in FIG. 8, the surface 3 to be inspected is a curved surface having a radius of curvature R, and the center thereof is C. Also, let P1 'be an arbitrary point on this curved surface, and let the central angle of that point be θc.
And Here, the reflection angle θh at the point P1 ′ on the curved surface is expressed as θh = θi + 2θc (Equation 3), which is the regular reflection angle on the curved surface. Furthermore, when there is a defect at the point P1 ′, the reflection angle θh is θh = θi + 2θc + 2θ (Equation 4), considering the same as in the case of the above-mentioned plane. Therefore, the diffuse reflection direction at the defect is 2θ regardless of whether it is a flat surface or a curved surface, with reference to the regular reflection direction at that point.
Becomes

As described above, since the reflection angle on the surface to be inspected can be obtained by the inclination angle θ of the defect, it is desired to measure and detect the distribution of the inclination angle θ of the defect in advance.
In other words, if you decide the range that you want to appear black in the image (match the minimum rank of the size that should be judged as a defect depending on the type of the object to be inspected), the stripe darkness (black 4) and the distance between the illuminating device 1 and the surface 3 to be inspected (D in FIG. 6; the angle between the illuminating device and the surface to be inspected is also included). At this time, typical defects are sampled according to the type of the object to be inspected and set so as to match them. In addition,
Contrary to the above description, it is possible to project the defects white in the stripes which are projected black on the surface to be inspected using the above-mentioned principle, but since the light of the illumination device 1 is diffused light, the defects are bright. It is not used in this embodiment because it does not appear large (it appears dark gray instead of white, and the area of the bright image is small).

As described above, in this embodiment, the irregular reflection of light at the defect is used to detect the defect appearing black in the bright stripe. Therefore, for example, if the ratio of the bright and dark portions of the stripe is 1: 1, the bright portion in the received light image becomes 1/2 of the entire image, and the area that can be inspected in one image becomes narrow. Therefore, the width of the light portion of the stripe is widened so that the light portion in the image is as wide as possible (for example, the ratio of light and dark is set to light: dark = 2: 1 or more). The distance T and the distance D may be determined. In order to inspect the entire surface to be inspected, as will be described later with reference to FIG. 25, the object to be inspected or the illumination device and the video camera are moved so that the bright portion of the stripe sequentially scans the entire surface to be inspected. To do.

Next, the operation will be described. It should be noted that this embodiment corresponds to the block diagram of FIG. If a bright and dark stripe is projected on the surface 3 to be inspected and there is a defect 13 in the bright portion of the stripe, the received light image is as shown in FIG.
Is projected black in the bright stripe. In the received light image of FIG. 9A, when the coordinate axes x and y are taken with the upper left corner of the screen as the origin, the luminance level in the x direction of the defect 13 is as shown in FIG.
It becomes like (b). Note that in FIG. 9B, the large unevenness corresponds to the bright portion and the dark portion of the stripe, and the fine unevenness is noise. Then, the defect is shown as a depression that is larger than the noise and that falls from the convex portion (bright portion). The image processing device 5 takes in a light-receiving image as shown in FIG. 9 as the signal S0 of the original image. For example, the image signal from the video camera 2 is A / D converted,
When the brightness level is converted into an 8-bit digital value, as shown in FIG. 9B, 255 is the maximum value on the vertical axis, which is bright, 0 is black, and one screen is captured with a resolution of 512 × 512 pixels. In that case, the horizontal axis represents the number of pixels of 0 to 512. The signal S0 shown is a signal for a certain value (value marked with *) in the y-axis direction, and such a signal exists for each value on the y-axis.

Next, FIG. 10 is a flowchart showing the processing contents in the image processing apparatus 5. Image processing device 5
In FIG. 10, as shown in FIG. 10, a defect is detected from the original image P1 by performing the light and dark pattern identification processing P2, the image enhancement processing P3, the light and dark area processing P4, and the defect detection processing P5. Further, as post-processing P6 after detecting a defect as described above, there are labeling, area and barycentric coordinate calculation, and the results thereof are displayed on a monitor or the like or output to a subsequent device.

The contents of each of the above processes will be described in detail below. First, the light and dark pattern recognition process P2 will be described. FIG. 11 is a waveform diagram of each component included in the signal S0 of the original image. As shown in FIG. 11, the signal S0 of the original image includes a shading component (low frequency component) due to uneven illuminance,
The brightness change component (intermediate component) due to the stripe illumination and the brightness change component (high frequency component) due to the defect 13 or noise 3
Divided into two. In order to recognize stripes from such an original image, low-frequency components are extracted from the original image and binarized as a threshold value (threshold surface) for the original image to determine the brightness (black and white) of the stripe. Separation / extraction or recognition. The above-mentioned frequency is a spatial frequency in image processing.

A specific example of the light and dark pattern recognition processing P2 will be described below. As shown in FIG. 10, the light and dark pattern recognition processing P2 includes smoothing P21, smoothing P22, and binarization P23. First, the original image is smoothed (low-pass filter) to remove only high frequency components due to defects and noise. This is because it is necessary to extract only the stripe component for recognizing the stripe, and it is necessary to extract only the defect component for emphasizing only the luminance change due to the defect 13 in the image enhancement process described later.

The smoothing described above will be described using a smoothing filter. For example, when the smoothing filter performs local calculation of a 3 × 3 mask on each pixel of 512 × 512 pixels on one screen as shown in FIG. 12, the pixel of interest Q0 (of 9 pixels arranged in a square shape (A pixel in the middle of the pixel) and a surrounding average of Q1 to Q8, that is, a simple average value of luminance values of a total of 9 pixels is set as a new luminance value of the target pixel Q0. Since the simple average value is calculated using the eight peripheral pixels in this way, minute noises and the like are removed.
The size of the mask is arbitrary, such as the 5 × 5 mask or the 7 × 7 mask, and this is called the mask size. The number of times the smoothing filter is processed and the mask size may be set according to the stripe interval and the size of the defect 13. As a result of such smoothing filter processing, the signal S0 of the original image has a defect (high frequency) like the signal S1 shown in FIG.
The signal has only the components removed.

Next, the smoothing process (P22) is repeated for the signal S1 in order to remove the low frequency components and extract only the stripe components. For this, the smoothing filter may be repeatedly used, or the mask size of the smoothing filter may be increased according to the stripe interval to shorten the processing time. As a result, only the low frequency component is taken out and becomes the signal S2. When the signal S1 after smoothing is binarized by using this S2 as a threshold value (threshold surface), it becomes S3, and the brightness of the stripe (black and white)
Can be recognized. Here, the signal S0 of the original image may be binarized, but when the signal S0 has many high-frequency components such as noise, it is more stable to use the signal S1 after smoothing as described above. It can be valued. In this embodiment, since the defect 13 that appears black in the area illuminated brightly by the stripe illumination is detected, the bright portion of the stripe recognized as described above (the area which becomes "1" by binarization). Only need to process. As another method of the light-dark pattern recognition processing, a method of subtracting the smoothed image S2 from the smoothed signal S1 to obtain the signal S4 and binarizing 0 as a threshold value may be used.

Next, the image enhancement process P3 will be described. In the present embodiment, a method using the signal S0 of the original image and the signal S1 from which only the defect component has been removed by smoothing will be described. This is a process of subtracting the signal S1 from the signal S0, whereby the low-frequency component and the stripe component are canceled out, and only the high-frequency component due to a defect, a boundary between light and dark, noise, etc. is extracted like the signal S5. The above result is binarized with a threshold value that ensures that defects are extracted. However, in places other than the defective part, areas where there is a change in brightness such as the boundary part of light and darkness are also emphasized,
If the brightness value of that portion exceeds the threshold value, the result is that the bright / dark boundary portion is also detected as shown in S6. Therefore, in the next defect detection process P5, the AND (logical product) of the image enhancement result S6 and the bright / dark pattern recognition result S3 is taken, so that a stripe boundary or an inspected surface other than the erroneous detection is likely to occur. It is possible to reliably detect only the defects by masking the areas and the like and without being affected by shading or the like. The defect signal thus detected is as in S7.

Basically, the defect detection is carried out as described above. However, if there is the above-mentioned "discolored skin", there is a risk of erroneous detection. The process P4 will be described. If the surface to be inspected has thin irregularities such as "Yuzu skin", it may remain unmasked even with the above mask processing, depending on the degree of roughness, and false detection of points other than defects may occur. .
For example, as shown in FIG. 14, the bright and dark boundaries of the stripe image are disturbed according to the surface roughness, and an isolated point as shown in (c) appears or a range α in which the brightness level becomes unstable is generated. To do. The portion in this range α causes the above-mentioned erroneous detection. And since this increases in proportion to the surface roughness,
The larger the range α, the more the area remains without being masked, and erroneous detection is more likely to occur. In order to solve the above-mentioned problem, in the present embodiment, the mask area is enlarged by enlarging the area of the dark portion of the signal S3 of the stripe recognition (binarization) result, and the portion of the range α is also masked. I am trying. In this way, the portion that may cause erroneous detection is processed as a dark portion, so that there is no risk of erroneously detecting "discolored skin" as a defect. It should be noted that the same processing is performed even if the process of reducing the area of the bright portion is performed instead of enlarging the area of the dark portion.

Next, a specific method of the light / dark area processing P4 will be described. FIG. 15 is a diagram for explaining the principle of bright and dark area processing. The area enlargement processing of the dark portion is performed by using Q1 to Q1 surrounding the pixel of interest Q0 in FIG.
When at least one figure pixel (dark part of figure) is present in the pixels of Q8, the pixel of interest Q0 is set to the same value as the figure pixel. As a result, as shown in FIG. 15B, the connected component of the graphic image in the image is enlarged outward by one pixel, and the black portion is enlarged to the shaded portion. The area reduction process is the reverse of the enlargement process described above. When there is at least one background pixel (bright part of the figure) among the pixels Q1 to Q8 surrounding the pixel of interest Q0,
This is a process of setting the pixel of interest Q0 to the same value as the background pixel. As a result, the dark part of the figure is reduced and the bright part is enlarged. In addition, the bright and dark area processing in the pixel figure as described above is 1
The amount of processing is proportional to the number of times of processing, because the surrounding one pixel is enlarged / reduced by one time of processing.

As a method of setting the number of times of processing in the actual light and dark area processing (that is, the amount of enlarging the dark portion),
For example, there is the following method (1) or (2). (1) If the surface to be inspected has a surface roughness, such as a painted surface of an automobile, within a substantially fixed range, the erroneous detection part due to the roughness (range α) is adjusted to the maximum value of the roughness range. The number of times required for completely masking (filling in) can be obtained in advance by experiments and set to that value. By doing so, the amount of enlargement of the dark portion can be easily set at the time of actual inspection. (2) Measure the surface roughness of the surface to be inspected for each input image,
The optimum number of enlargement processes may be automatically obtained from the result. By doing this, it is possible to enlarge the dark part by the optimum amount according to each inspected object,
A precise inspection can be performed. As a method of measuring the surface roughness, there are (a) a method using a normal surface roughness measuring instrument, (b) a method of obtaining the amount of stripe disorder of an input image from the spatial frequency of the image, and the like.

The above method (b) will be briefly described below. When the stripe pattern is displayed on the surface to be inspected and the image is picked up by the video camera 2, as shown in FIG. 16A, the stripe pattern is disturbed according to the unevenness of the surface. When this signal is subjected to FFT (Fast Fourier Transform) to obtain the power spectrum, it becomes as shown in FIG. In the characteristic curve of FIG. 16B, the large convex region on the left end is the fundamental wave corresponding to the light and shade of the stripe, and the long wavelength region on the right thereof is the portion corresponding to the irregularities having a relatively large area such as “Yuzu skin”. The middle-wavelength region and the short-wavelength region on the right are portions corresponding to irregularities having a smaller area. Therefore, if the surface roughness due to "Yuzu skin" is large, the area of the long wavelength region (the area of the shaded area) becomes large,
From this value, the degree of "Yuzu skin" can be measured.

The area of the erroneously detected portion is sufficiently smaller than the original area of the defective portion and is constant (the roughness is measured in advance,
If it can be determined that it is almost constant), after the dark part is enlarged in the binarized signal S6 of FIG. 13, only the original defect is processed to the original size before the process. In order to restore the area, a dark part contraction process (bright part enlargement process) may be performed. That is, as described above, once the dark area is enlarged and the boundary line is disturbed due to the effect of "Yuzu-skin", or an area where an isolated point is generated is captured in the dark area, Even if the image processing for reducing the partial area is performed, the above-mentioned disorder or isolated point is not reproduced. Therefore, by performing the enlargement processing and the reduction processing as described above, it is possible to eliminate the influence of "discolored skin" and restore the defect or stripe portion to the initial state.

Next, as post-processing after the defect detection is completed as described above, based on the defect detection signal S7,
Labeling (labeling) of defects, area calculation, and barycentric coordinate calculation. And host computer 6
Identifies the defect type, calculates the defect occurrence position, ranks, etc. from the result, and outputs it to an output device such as a display device or a printer. Alternatively, if there is a defect, control the line such as switching the inspection object from a normal line to a repair-only line (for example, in the case of a car body painting process, only the defective part is repair-painted), or the defect rank. It may be used for (size), statistical processing for the occurrence location, and the like.

Further, in the above description, the illumination device 1 has been described as a device that projects a stripe-shaped bright and dark pattern, but as shown in FIG. 17, the bright and dark pattern may be a grid pattern. Specifically, it can be realized by forming the stripe plate 1c of FIG. 4 into a lattice pattern as shown in FIG. The operation and effect of using such a grid pattern are similar to those of the stripe pattern described above. Further, the intervals and ratios of the grid pattern may be set in the same manner as described above. However, if the horizontal grid pattern is in the same direction as the moving direction of the object to be inspected, if there is a defect in the horizontal grid pattern (dark part), the defect is not reflected in the bright part even once, so it cannot be detected. It is expected that. Therefore, the grid pattern may be arranged obliquely with respect to the moving direction of the inspection object.

Next, another embodiment of the image enhancement process P3 will be described. When only the second derivative is directly applied to the original image, electrical noise and noise such as minute luminance change component are also emphasized too much. Therefore, in this embodiment, the signal of the original image is first smoothed by a normal function (Gaussian function), and then the second derivative is applied. In addition,
The final point of binarization is the same as above. A Gaussian function having an average of 0 and a variance σ in the x direction of the original image is represented by the following (Equation 5). The function form is shown in FIG.

[0045]

(Equation 5)

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

[0047]

(Equation 6)

In the above equation (6), the parameter σ
Since the emphasizing ability can be adjusted by σ, it is sufficient to set σ so as to emphasize only the defect without reacting to a noise component smaller than the defect or a stripe component larger than the defect. Actually, since the image is a digital signal having discrete values, the function of the equation (6) shown in FIG. 19 also becomes as shown in FIG. FIG. 20 shows an example in which the mask size is applied as a filter of 9 pixels, and the coefficients a _{1 to} a ₅ may be adjusted according to the size of the defect to be emphasized. In the image emphasizing process of this embodiment, it is possible to ignore only minute points smaller than a defect and also include a smoothing function, and emphasize only the defect, and these functions / processes can be performed at high speed. , And so on.

Next, another embodiment of the image enhancement process P3 will be described. In this embodiment, the maximum value filtering process and the minimum value filtering process are performed on the original image. The maximum value filtering process or the minimum value filtering process is a kind of smoothing process in which the maximum value or the minimum value of the brightness values of the target pixel and its neighboring pixels is set as the value of the target pixel. For example, when the maximum value filtering process is performed on the original image, the dark portion of the low-luminance stripe is replaced with the luminance value of the surrounding bright portion. The filter size and the number of times of processing at this time may be determined according to the interval between stripes in the original image. On the contrary, in the case of the minimum value filter, the bright portion of the stripe is replaced with the luminance value of the dark portion.

A detailed description will be given below with reference to FIG. FIG. 21 is a diagram showing images corresponding to image signals at the time of maximum value filtering and minimum value filtering.
In the maximum value filtering process, for example, in the case of the 3 × 3 size shown in FIG. 12, the maximum value of the brightness among the nine pixels Q0 to Q8 is set as the new brightness value of the target pixel Q0. Therefore, a high-luminance region, that is, a bright portion spreads outward by one pixel, and when the maximum value filtering process is performed, the bright portion spreads as shown in S11 of FIG. Therefore, if the maximum value filtering process is performed until all the black defective portions in the bright portion are eliminated, the defective portions can be erased. However, when such a process is performed, the area of the bright portion is widened and the width of the black stripe is narrowed. Therefore, the minimum value filtering is performed to restore the width of the black stripe to the initial state.

The minimum value filter process is the reverse of the maximum value filter process, and the minimum value of the brightness among the nine pixels Q0 to Q8 is set as the new brightness value of the target pixel Q0.
As a result, a high-luminance region, that is, a bright portion is narrowed by one pixel.
As shown in S12, the bright part becomes narrower, and if the minimum value filter processing is performed the same number of times as the maximum value filter processing,
The stripe width of the original image S10 can be restored. In this case, the once-erased defect is not restored even if the minimum value filtering process is performed. Therefore, by performing the maximum value filtering process and the minimum value filtering process, an image in which only the defective portion is deleted from the original image is obtained. . By subtracting the image S12 from which only the defective portion is erased as described above from the original image S10, only the defective portion can be extracted, that is, emphasized, as shown in S13. The image of S13 is shown by reversing black and white.

In the method of detecting or emphasizing a defect by simply using the differential processing like the conventional image emphasizing processing, the boundary between the light and dark portions of the light and dark stripe pattern, the area other than the surface to be inspected and the boundary with the area. Or, since there is a change in brightness even at a portion such as a concavo-convex or a processed portion which is provided as a design on the surface to be inspected, if there is a portion having a change in luminance in the received light image, which is not a defect, Although there is a problem that points other than the above defects are also emphasized, the above problem can be solved in this embodiment as described above.

Next, another embodiment of the light and dark pattern recognition processing P2 will be described. In this embodiment, the maximum value filtering process or the minimum value filtering process is performed on the original image to remove the stripe components and only the low frequency components are extracted, and the result is used as a threshold value (plane) to recognize the stripes. It is something to do. When the maximum value filtering process as described above is performed a number of times, as shown in FIG. 22, the dark portion of the stripe with low luminance is replaced with the luminance value of the bright portion around it, resulting in a smoothed image signal. The filter size and the number of times of processing at this time may be determined according to the interval between stripes in the original image. For minimum value filtering,
On the contrary, the bright part of the stripe is replaced with the brightness value of the dark part. Although the original image is binarized by using such a result as a threshold value (plane), it is necessary to add an appropriate bias to this result in practice, as is clear from FIG. Further, as will be described later with reference to FIGS. 24 and 25, when the necessary bias value changes according to the difference in the brightness level due to the color of the surface to be inspected, the bias is adjusted according to the brightness histogram of the original image. It may be set and adjusted.

Further, the maximum value filtering process and the minimum value filtering process are a kind of smoothing, and thus may be used as the smoothing process. As described in the section of the image enhancement processing, first, the maximum value filter processing is performed to completely erase the defect appearing black in the bright portion of the stripe by the brightness value of the surrounding bright portion. As a result, a part of the dark part of the stripe is also replaced with the brightness value of the bright part, so that the width of the dark part becomes narrower. However, by applying the minimum value filter process of the same size as the maximum value filter process the same number of times. , Only the stripes should be restored to their original width. At this time, since the defect has been erased, it does not appear again, and only the defect can be finally erased.

In the conventional bright and dark pattern recognition processing, the relationship between the brightness levels is not always constant due to the influence of the light quantity variation of the illumination, the effect of shading (luminance unevenness), etc. Therefore, a non-defective portion is erroneously detected as a defect. However, in the present embodiment, such a problem can be solved as described above.

Next, another embodiment of creating the binarized threshold value in the bright and dark pattern identifying process P2 will be described. In this embodiment, a binarized threshold value (plane) is created by smoothing an original image by applying a digital low-pass filter. FIG. 23 is a block diagram of an IIR (infinite impulse response) filter, which is an example of a digital low-pass filter. This can be expressed by a mathematical expression as shown in the following (Equation 7). y (n) = (1-k) * x (n) + k * y (n-1) (Equation 7) where x (n): original image brightness value y (n): new brightness value k: coefficient (0 <k <1) Since the transfer function H (z) is H (z) = (1-k) / (1-kz) (Equation 8), the steeper the attenuation as k → 1. It operates as a low pass filter (smoothing filter) with characteristics. In addition,
In FIG. 23, D is a 1-pixel delay circuit. Also,
The coefficient k may be adjusted according to the interval between stripes shown in the original image. The present embodiment has the advantages that the calculation formula is simple, high-speed processing is possible, and the memory usage is small.

Next, an example in which the binarization threshold value changes depending on the color of the surface to be inspected in the image enhancement processing P3 and the light and dark pattern identification processing P2 will be described. As shown in FIG. 24, the high luminance (bright) color and the low luminance (dark) color of the surface to be inspected have different luminance levels of the entire signal and stripe contrast,
It may be necessary to slightly change the binarization threshold value in the image enhancement process depending on the color. Therefore, in this embodiment, by taking the luminance histogram of the original image, the difference in color of the surface to be inspected is detected, and the binarization threshold value in the image enhancement processing is finely adjusted accordingly. For example, in FIG.
As shown in Fig. 4, if the luminance histogram of the original image is taken, the frequency of pixels with high luminance value is high when the surface to be inspected is bright color, and conversely, the frequency of pixels with low luminance value is dark when the surface is dark. Therefore, the difference in color of the surface to be inspected is detected and judged, and the binarization threshold value in the image enhancement processing is finely adjusted. As described above, in this embodiment, the color of the surface to be inspected can be detected and determined without using a special device. The same process can be performed by using the maximum luminance value of a predetermined one line (for example, one line in the x direction of the image center y = 256) instead of the luminance histogram of the original image. Further, as described above, the binarization threshold value in the bright and dark pattern recognition processing can be adjusted according to the brightness level as described above.

Next, practical examples of the present invention will be described. FIG. 26 is a perspective view of an embodiment in which the present invention is applied to a coating inspection process of an automobile manufacturing line. As shown in FIG. 26, the painted automobile body 14 flows along the coating inspection line in the direction of the arrow. A plurality of lighting devices 1 and video cameras 2 are arranged in an arched shape so that a predetermined light and dark pattern is projected on a part of the painted surface of the body 14 and the part where the light and dark pattern is projected is imaged. Has been done. Therefore, defects are detected on the surface to be inspected while the body 14 moves on the coating inspection line and passes under the arched video camera and stripe illumination. With such a configuration, the body 1
It is possible to thoroughly inspect the painted surface of No. 4. Moreover, it is not necessary to scan the video camera and the illumination along the surface to be inspected by using a robot or the like, and the entire body can be easily inspected.

Next, another embodiment of the present invention will be described. This embodiment corresponds to the block diagram of FIG. 1B and detects both a black defect in a bright portion of a bright and dark pattern and a white defect in a dark portion. In this embodiment, parts such as the illumination device 1, the video camera 2, the camera control unit 4, the image processing device 5, and the host computer 6 are the same as those in the embodiment of FIG. 1A, and only the processing contents are different. ing.

The operation will be described below. If bright and dark stripes are projected on the surface 3 to be inspected and there is a defect in the bright portion and the dark portion, the received light image is as shown in FIG.
As shown in (a), the defect appears as black in the light stripe and as white in the dark stripe. In the received light image of FIG. 27A, when the coordinate axes x and y are taken with the upper left corner of the screen as the origin, the luminance level in the x direction at the y coordinate (value of * mark) of the defective portion is as shown in FIG. 27B. become. The image processing device 5 takes in the received light image of FIG. 27A as a signal S20 of the original image as shown in FIG. 27B. For example, when the image signal is A / D converted and the brightness level is converted into a digital value of 8 bits, 255 is the maximum value on the vertical axis, white is 0 and 0 is black, and one screen is captured with a resolution of 512 × 512 pixels. In this case, the horizontal axis represents the number of pixels of 0 to 511.

Next, FIG. 28 is a flow chart showing the processing contents in the image processing apparatus 5. Image processing device 5
28, from the original image P1, the smoothing process P11, the enhancement process P12, the binarization process P13,
A region process P14 and an area determination process P15 are performed to detect a defect. Further, as post-processing after detecting defects as described above, there are labeling, area and barycentric coordinate calculation P16, and the results thereof are displayed on a monitor or the like at P17 or output to a subsequent device.

The contents of each of the above processes will be described in detail below. FIG. 29 is a diagram showing each image and its signal (at a certain y-coordinate value, in this example, at the value marked with * in FIG. 27). The contents of image processing will be described below with reference to FIG. First, smoothing (smoothing) of a predetermined mask size is performed on the original image S20, and only a minute luminance change component due to noise or the like is removed to obtain S21. This smoothing processing is, for example, a simple averaging filter that obtains an average value of the brightness values of the pixel of interest and pixels in the vicinity thereof and sets it as a new brightness value of the pixel of interest. The mask size of the smoothing filter at this time may be determined according to the size of noise generated in the original image. Next, S21
By emphasizing the area, the area having the brightness change is emphasized.
This emphasis processing is, for example, differential processing, which is the sum of the absolute values of the differential results in the x and y directions, or the square root of the sum of squares, and outputs all the brightness change regions to the positive (+) side. Then, it becomes as shown in S22. The image of S22 is shown by reversing white and black. As described above,
In the present embodiment, the order of performing the emphasizing process after the smoothing process is, for example, as described with reference to FIGS. 18 to 20, the smoothing and the differentiating process are simultaneously executed to obtain the original image S20.
Alternatively, the processed image S22 may be directly obtained from. Next, the result of S22 is set to a predetermined threshold value so that the brightness change region is black (“0”) and the others are white (“255”).
When the value is converted, it becomes as in S23. As shown in the image of S23, areas having a luminance change such as a defective portion and a stripe boundary portion are extracted. Depending on the condition of the surface to be inspected, the boundary line of the stripe is distorted and extracted,
By smoothing the original image as described above, the original image is extracted as one continuous region, so that the surface to be inspected is hardly affected. However, near the border of the stripe,
Since there is a possibility that noise will be left due to the effect of the "skinned skin" on the surface to be inspected, in order to remove it, area processing is performed in the same manner as described above. That is, by performing the process of enlarging the dark area (corresponding to the area where the brightness changes) in the image in S23, the stripe boundary area and the noise due to the "discolored skin" are connected into one area.

Next, the area of each part is determined based on the above result. As is clear from the image in S23, the area (size) of the defective portion is significantly smaller than the area of the stripe boundary portion, and therefore the defect processing is performed by performing a determination process such as determining a region having a predetermined area or less as a defect. Only can be extracted. This area determination result is as shown in S24, and it is possible to detect both the defect that appears black in the bright portion and the defect that appears white in the dark portion of the bright-dark stripe image. However, since it is not possible to detect defects in the boundary portion of brightness and darkness (the portion where there is a change in luminance), the above-mentioned FIG.
Similar to the embodiment of (a), in order to inspect the entire surface to be inspected, the object to be inspected or the illumination means and the image pickup means are sequentially moved so that the bright part scans the entire surface to be inspected (for example, 26). However, as compared with the embodiment of FIG. 1 (a), the area in which the defect inspection can be performed is wider in one screen, so that the inspection can be performed quickly.

Next, special processing when the surface to be inspected is a curved surface will be described. In the above-mentioned embodiment, since the defect is detected by the area determination, the following problems occur. That is, as shown in FIG. 30, when the surface to be inspected is a curved surface, the stripe is deformed along the curved surface. Therefore, there may be a case where the stripe boundary line is cut off at the corner of the screen and the area becomes small (a portion surrounded by a circle). In such a case, this portion may be erroneously detected as a defect in the area determination. Therefore, the property that the stripe boundary line is always in contact with one of the windows (four sides of the screen) is used. When the detection area is in contact with the window, if the area is excluded from the processing target, Good. Specifically, if one screen has 512 × 480 pixels, and the maximum and minimum points of the x and y coordinates of the detection area are 0, 479 or 511, the area is in contact with the window. It is judged that there is one, and that part can be removed.

Another method for solving the above problem is as follows. That is, as shown in FIG. 31, by setting the same brightness value (black in FIG. 31) as the detection area around the screen for one pixel (hatched portion in FIG. 31),
Since all the stripe boundary lines are connected to form one region, the area becomes large, and the area determination is performed more reliably.

Further, there is the following method as another method for solving the above problem. On the stripe plate 1c of the illuminating device 1, a line that is diagonal to the stripe, is black in the white stripe, and is white in the black stripe, and intersects with this line. Put another one in advance. A stripe image when such an illuminating device is used is as shown in FIG. If such an original image is processed, FIG.
As shown in (1), the stripe boundary lines are all connected to form a single region, so that the area becomes large and the area determination is performed more reliably.

However, as shown in FIG. 26, when the object to be inspected is sequentially moved to inspect a large area, if the moving direction of the object to be inspected and the direction of the above-mentioned oblique line match, the line is moved. If there is a defect in the boundary part of, the defect will not be detected. Therefore, it is necessary to set so that the above-mentioned diagonal line and the moving direction do not match. In addition,
In the embodiment corresponding to FIG.
Configuration of the illumination means described in the embodiment corresponding to (a), 2
The setting method of the threshold value, the specific method of the area processing, the practical application device and the like can be applied in the same manner as described above.

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

In FIG. 33, the distance from the video camera 2 composed of a lens and a light receiving element (for example, CCD) to the surface 3 to be inspected is a, the inspection range (camera field of view) on the surface to be inspected L is L, and the viewing angle is. When θg, the focal length is b, and the element size is c, the relationship of L: a = c: b holds. Therefore, if the lens of the video camera to be used and the detection range are determined, the distance a is also determined. For example, when used in the coating inspection process of automobiles, considering inspection efficiency and space, L = 100 to 200 mm, a
== about 50 to 100 cm is suitable. In addition, as shown in FIG.
When arranged in the i direction, the inspection range L is slightly wider than in the case of FIG. Assuming that the end portions of the inspection range L on the plane of the surface 3 to be inspected are J ₁ and J ₂ , respectively, as shown in FIG. 34, if the illuminating device 1 is arranged in the regular reflection directions at J ₁ and J ₂ . Stripe light can be applied to the entire inspection range L. The distance to the lighting device 1 at this time is D.

Next, consider the case where there is a defect at point J ₁ in FIG. In FIG. 35, the regular reflection direction is set to 0 (deg) as a reference, and the irregular reflection angle due to the unevenness of the defect is ± α.
(Deg) (however, the sign of ± indicates the direction). FIG. 36 is a diagram showing a measurement result of a convex defect called "dust". If the distribution of angles θ at arbitrary points on the defect is obtained from such measurement results,
The diffuse reflection angle α at each point is obtained. Next, FIG.
As shown in FIG. 3, assuming that the point J ₁ is on the left slope of the defect, when there is a black stripe of stripe illumination in the direction in which the diffused reflection angle is larger than + α, the angle θ at that point
Areas with a larger tilt angle appear darker than the surroundings. Letting this range be W, the relationship between W and α is shown in FIG.
As shown in 8. Therefore, from FIG. 38, it is necessary to determine how many times or more the inclination angle θ of the defect appears as a black dot in the white stripe. For example, when a field of view of L = 150 mm is imaged with a resolution of 512 pixels, a defect having a width of 1 mm appears in a width of about 3 pixels, and it can be determined that the defect can be detected. Therefore, α can be found from FIG. 38 so that W = 1 mm. For example, in FIG. 39, if the ratio of white to black in the stripe is 2: 1, the white stripe has -5 to 5 (deg), -10 to 20 (deg),
It is assumed that the black stripe is (W ₁ −W ₂ ) + W ₃ ≧ 1 mm at a ratio of the angle α of 5 to 10 (deg) and 20 to 25 (deg). Based on this, as shown in FIG. 40, J ₁ , J ₂
From the above, draw the line of the angle α of the above ratio. Then, it suffices that the black stripes exist at the intersections of the black stripe directions (the above-mentioned range of 5 to 10 deg and 20 to 25 deg).
Thus, from FIG. 40, the approximate spacing T of the stripes is
And the distance D can be determined. As described above, if the inclination angle distribution as shown in FIG. 36 is obtained for the smallest defect to be detected and the stripe illumination is set based on this, the optimal illumination for the defect inspection can be obtained.

Furthermore, in order to obtain the strict interval T and the distance D, the angle calculation in the detection range L may be performed using the above-mentioned diffuse reflection principle formulas (Formula 1) and (Formula 2). . In the above description, the end portion J of the detection range L is
_{Although I} considered ₁ and J ₂ , more rigorous illumination settings can be made by considering other points. Further, it can be seen from FIG. 34 that the smaller θg is, the smaller the illumination device 1 is. When the surface 3 to be inspected is a curved surface, calculation and design may be performed in the same manner as in the case of the above-mentioned plane, based on the equation (4) in consideration of the radius of curvature R and the central angle θc. The method for setting the stripe illumination is not limited to the above example.

[0072]

As described above, according to the present invention, the region where the influence of extremely thin unevenness which is not a defect such as so-called "discolored skin" is likely to occur is excluded from the defect detection region. , "Yuzu skin"
There is no possibility of erroneously detecting a defect as a defect, and it is possible to obtain a more accurate defect detection.
In addition, in the case where the amount of area processing for eliminating the above-described areas that are likely to be affected from the defect detection area is set to a value obtained in advance by experiments, etc., the amount of processing can be simplified at the time of actual inspection. Can be set to
In the case of measuring the surface roughness of the surface to be inspected for each input image and automatically finding the optimum amount of processing from the result, it is possible to set the optimum amount according to each inspected object, A precise inspection can be performed. Further, in the case where the binarization threshold value is adjusted based on the brightness histogram of the light and dark pattern image, accurate inspection can be realized without being influenced by the change in light and dark due to color. Also,
In the case where a defect is detected by area determination and the stripe boundary line portion is formed as a continuous region, the influence of the curved surface can be eliminated and an accurate inspection can always be realized. Further, in a factory line in which a plurality of illumination means and imaging means are arranged so as to surround an object to be inspected in an arch shape, the entire surface of the object to be inspected can be easily inspected without using an expensive device such as a robot. It is possible to obtain effects such as.

[Brief description of drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

FIG. 14 is a view showing a disorder of a stripe image due to roughness of a surface to be inspected.

FIG. 15 is a pixel diagram for explaining bright and dark area processing.

FIG. 16 is an image diagram and a characteristic diagram for explaining the surface roughness measurement.

FIG. 17 is a diagram showing another bright and dark pattern of the lighting device.

FIG. 18 is a diagram showing a function form of a normal function.

FIG. 19 is a diagram showing a functional form obtained by quadratic differentiating a normal function.

20 is a diagram showing the digitized characteristic of FIG. 19;

FIG. 21 is a diagram showing an image signal and an image for explaining image enhancement processing using maximum value filtering processing and minimum value filtering processing.

FIG. 22 is a diagram showing a result of maximum value filter processing.

FIG. 23 is a block diagram of an example of a digital low pass filter.

FIG. 24 is a diagram showing an image signal in the case where there is a luminance change due to color.

FIG. 25 is a diagram showing the frequency of luminance when there is a luminance change due to color.

FIG. 26 is a perspective view showing an embodiment when the present invention is applied to an automobile body coating inspection line.

FIG. 27 is a diagram showing a received light image and its image signal in the second embodiment of the present invention.

FIG. 28 is a flowchart showing the processing contents in the second embodiment.

FIG. 29 is a diagram showing an image and its image signal in each processing of the second embodiment of the present invention.

FIG. 30 is a diagram showing a change in an image when the surface to be inspected is a curved surface.

FIG. 31 is a diagram showing a result of converting one pixel around the screen.

FIG. 32 is a diagram showing a light-receiving image and its image signal in a lighting device to which an oblique stripe is added.

FIG. 33 is a diagram showing the relationship between each numerical value of the video camera and the inspection range L.

FIG. 34 is a diagram for explaining an inspection range L.

FIG. 35 is a diagram for explaining the relationship between a defect and a detection angle.

FIG. 36 is a diagram showing a measurement result of a convex defect.

FIG. 37 is a view for explaining an example in which the point J ₁ is on the left slope of the defect.

FIG. 38 is a characteristic diagram showing the relationship between W and α.

FIG. 39 is a characteristic diagram showing an example of the relationship between W and α.

FIG. 40 is a view for explaining a method of determining the stripe interval T and the distance D.

[Explanation of symbols]

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

Claims (23)

[Claims] 1. A surface defect inspection apparatus for irradiating a surface to be inspected with light, creating a light-receiving image based on light reflected from the surface to be inspected, and detecting defects on the surface to be inspected based on the light-receiving image. In, an illumination means for displaying a predetermined light and dark pattern on the surface to be inspected, an imaging means for converting a light-receiving image obtained by imaging the surface to be inspected into image data of an electric signal, and image data obtained by the image capturing means. Of the frequency components in the image data, the bright and dark pattern identifying means for processing the light portion and the dark portion to identify the bright portion and the dark portion, the bright and dark area processing means for expanding the area of the dark portion obtained by the light and dark pattern identifying means, There are both an image enhancing means for extracting only a component in a high frequency region and a level equal to or higher than a predetermined value, and a bright portion in the bright / dark region processing means and a component extracted by the image enhancing means. Surface defect inspection apparatus characterized by comprising a defect detection means for detecting as a defect a portion. 2. The light and dark area processing means enlarges the area of the dark portion by a predetermined value which is predetermined according to the roughness of the surface to be inspected. Surface defect inspection device. 3. The light and dark area processing means enlarges the area of the dark portion by a value corresponding to the roughness of the surface to be inspected obtained from the image data obtained by the image pickup means. The surface defect inspection apparatus according to claim 1. 4. The light and dark area processing means, which temporarily enlarges the area of the dark portion and then reduces the area of the dark portion.
The surface defect inspection apparatus according to any one of 1. 5. The image enhancing means obtains a difference between an original image of the image data obtained by the image pickup means and an image obtained by subjecting the original image to a predetermined smoothing process, and the result is a predetermined threshold. The surface defect inspection apparatus according to any one of claims 1 to 4, wherein the value is binarized. 6. The image emphasizing means smoothes an original image of the image data obtained by the imaging means with a normal function and then second-order-differentiates the binarized result with a predetermined threshold value. The surface defect inspection apparatus according to any one of claims 1 to 4, wherein. 7. The image enhancing means determines the difference between the original image and the original image of the original image of the image data obtained by the image pickup means after the predetermined maximum value filtering process and the minimum value filtering process. The surface defect inspection apparatus according to any one of claims 1 to 4, wherein the obtained result is binarized at a predetermined threshold value. 8. The light / dark pattern identifying means performs a predetermined smoothing process on the original image of the image data obtained by the image pickup means, and the result is used as a threshold value to binarize a bright part and a dark part. The surface defect inspection apparatus according to any one of claims 1 to 7, wherein the surface defect inspection apparatus is configured to perform a change or recognition. 9. The light and dark pattern identifying means performs predetermined maximum value filtering processing or minimum value filtering processing on the original image of the image data obtained by the image capturing means, and the result is used as a threshold value for a bright portion. 8. The surface defect inspection apparatus according to claim 1, wherein the dark part is binarized, that is, recognized. 10. The light / dark pattern identifying means performs digital low-pass filter processing with a predetermined coefficient on the original image of the image data obtained by the image pickup means, and the result is used as a threshold value for a bright portion and a dark portion. 8. The surface defect inspection apparatus according to claim 1, wherein the surface defect inspection apparatus is for binarizing, that is, recognizing. 11. A binarization of the image emphasizing means and the light / dark pattern identifying means, a luminance histogram of an original image of image data obtained by the imaging means or a maximum luminance value of a predetermined line is obtained, 11. The surface defect inspection apparatus according to claim 1, wherein the binarization threshold value is adjusted based on the result. 12. A surface defect inspection apparatus for irradiating a surface to be inspected with light, forming a light-receiving image based on the light reflected from the surface to be inspected, and detecting a defect on the surface to be inspected based on the light-receiving image. In, an illuminating means for displaying a predetermined light and dark pattern on the surface to be inspected, an image pickup means for converting a received light image obtained by imaging the surface to be inspected into image data of an electric signal, and an image obtained by the image pickup means. Smoothing means for smoothing the data; enhancement means for extracting a portion having a brightness change in the image data smoothed by the smoothing means; binarization means for binarizing the result of the enhancement means; The area processing means for enlarging the area of the value portion corresponding to the portion having the luminance change in the binary signal obtained by the binarizing means, and the area obtained by the area processing means are respectively calculated. By doing Area determination processing means for obtaining an area for each portion having a brightness change existing in one screen and detecting a portion whose area is equal to or smaller than a predetermined value as a defect. Defect inspection equipment. 13. The area processing means enlarges the area of the value portion corresponding to the portion having the luminance change by a predetermined value determined in advance according to the roughness of the surface to be inspected. The surface defect inspection apparatus according to claim 12. 14. The area processing means enlarges the area of the value portion corresponding to the portion having the luminance change by a value corresponding to the roughness of the surface to be inspected obtained from the image data obtained by the image pickup means. The surface defect inspection apparatus according to claim 12, wherein 15. The area processing means temporarily enlarges the area of the value portion corresponding to the portion having the brightness change and then reduces the area. The surface defect inspection apparatus according to any one of claims 12 to 14. 16. The area determining means performs the area determining process after removing a predetermined region in contact with the peripheral boundary of the screen in the binarized image. Item 16. The surface defect inspection device according to any one of items 15. 17. The area determining means determines the area after making one pixel, which is in contact with the peripheral boundary of the screen in the binarized image, the same value as the portion having the luminance change. The surface defect inspection apparatus according to any one of claims 12 to 15. 18. The illuminating means displays a pattern consisting of parallel stripes and a line diagonally crossing the stripes as a light-dark pattern, according to any one of claims 12 to 17. The surface defect inspection device described. 19. The surface defect inspection apparatus according to claim 1, wherein the illuminating means projects a stripe-shaped bright-dark pattern on the surface to be inspected. 20. The surface defect inspection apparatus according to claim 1, wherein the illuminating means projects a grid-like bright-dark pattern on the surface to be inspected. 21. The illuminating means is such that the distance and ratio between the bright and dark parts of the light-dark pattern and the distance from the illuminating means to the surface to be inspected are the curvature of the surface to be inspected and the expected defects and the surface to be inspected. It is set based on the angle you make,
The surface defect inspection apparatus according to any one of claims 1 to 20, characterized in that. 22. A means for performing a labeling process and a defect area and barycentric coordinate calculation process on the basis of the data relating to the defect obtained by the defect detecting means. The surface defect inspection apparatus according to any one of 1. 23. A plurality of the image pickup means and a plurality of the illumination means are arranged in an arch shape around an object to be inspected, and a wide surface to be inspected of the object to be inspected is inspected at the same time. 23. The surface defect inspection apparatus according to claim 1.