JPH0816653B2 - Defect inspection method and device for transparent container - Google Patents

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method and apparatus for inspecting a transparent container such as a glass container for defects by using image processing technology.

[0002]

2. Description of the Related Art Conventionally, various methods and devices have been used for inspecting the presence or absence of a light-shielding or refractive defect on the bottom or body of a transparent container such as a glass drinker or glass bottle using an image sensor. Is provided. For example, JP-A-1
In Japanese Patent Publication No. 1413411, in order to inspect whether there is a defect at the bottom of the bottle, light is obliquely applied to the bottom of the bottle from the outside.
A technique is disclosed in which light transmitted through the bottom of the bottle is received by an image sensor installed outside the mouth of the bottle, and the output of the image sensor is subjected to image processing. However, the conventional inspection method and device, including those disclosed in this publication, do not assume that a large uneven pattern is formed on the bottom or body of the transparent container, and therefore the transparent container itself. No technical consideration was given to distinguish between the uneven pattern and the defective portion.

On the other hand, as an image processing technique for the purpose of product inspection, "Image Lab" 199 issued by Nippon Kogyo Publishing Co., Ltd.
The products to be inspected are C
Take a picture with a CD camera.
After storing in the image memory as a grayscale value of 256 gradations per pixel with a resolution of 2 pixels and 448 pixels in the vertical direction, the stored image is subjected to image processing by a grayscale morphology method of expansion and contraction of the grayscale, and thus the quality of the product. A method of performing an inspection is disclosed.

The algorithm of this method is summarized as follows (1) to (5). (1) For the original image data of one screen stored in the image memory, find the maximum gradation value (the brightest gradation value) in each of the judgment pixel areas of a predetermined number of vertical and horizontal pixels, The expansion process of replacing the gradation value of each pixel with the obtained maximum gradation value for all pixels while sequentially shifting the determination pixel area. By this process, pixels that are brighter than the surroundings expand, while pixels that are darker expand. (2) For the image data after the shadow contraction processing, find the smallest gradation value in the judgment pixel area, and replace the gradation value of each pixel in the judgment pixel area with the found maximum gradation value. This is a contraction process that is performed for all pixels while sequentially shifting the determination pixel area. In this process, conversely to the process of (1) above, pixels darker than the surroundings expand while bright pixels contract. (3) Difference processing for calculating the gradation difference between the image data after the above (2) processing and the original image data stored in the image memory for each pixel. (4) Binarization processing in which each pixel data after the difference processing is binarized to determine the presence or absence of a defect for each pixel. (5) A defect determination process in which the number of pixels that have been regarded as defects in the binarization process is counted, and when the number of pixels is a certain value or more, it is determined that there is a defect.

[0005]

However, this method is
Since the small dark defects existing inside the bright image in the first place and the small bright defects existing inside the dark image in the first place were proposed with the intention of removing the original image pattern without destroying it, inspection When a large uneven pattern is formed on the target transparent container, a relatively small dark or bright defect existing in the concave or convex portion of the pattern (for example, a foreign substance having a refractive property or a light shielding property against light) ) Can be detected, but a large defect cannot be detected because it is difficult to distinguish it from the uneven pattern.

An object of the present invention is to provide a transparent container having an uneven pattern formed on its outer surface, regardless of the size of the defect existing in the concave portion or the convex portion of the transparent container. And to allow easy detection.

[0007]

In order to achieve this object, the present invention provides a large uneven surface formed on the outer surface of a transparent container.
A method for inspecting a defect existing in a pattern, wherein a convex portion of the concave-convex pattern is formed so that a rising angle from the concave portion is 70 degrees or less, and diffused light from the projector is inside the transparent container. Image from a solid-state image sensor, and the image of the one screen is digitally processed.
In theory, set a large determination pixel area with a predetermined number of vertical and horizontal pixels
Then, each large judgment pixel is moved while shifting the large judgment pixel area.
Shadow contraction processing that contracts the shadow due to the uneven pattern for each area,
For the pixel data after the shadow contraction processing,
Set a small pixel area with a specified number of vertical and horizontal pixels that is smaller than the area
Then, while moving the small judgment pixel area, each small judgment pixel
Defect determination processing that detects dark areas due to defects in each area
It is characterized by applying .

Further, in the present invention, in order to detect a large defect, image analysis processing is performed in the following procedure. (1) Storage processing for storing the gradation value of each pixel from the image sensor of the solid-state image sensor camera in the image memory. (2) For the original image data of one screen stored in the image memory, the maximum gradation value is determined for each large determination pixel area M having a predetermined number of vertical and horizontal pixels, and each of the large determination pixel areas M is determined. A shadow contraction process that replaces the gradation value of a pixel with the obtained maximum gradation value for all pixels while sequentially shifting the large determination pixel region M. (3) Difference processing for calculating, for each pixel, the gradation difference between the image data after the shadow contraction processing and the original image data stored in the image memory. (4) With respect to the pixel data after the difference processing, the gradation value of each pixel in each small judgment pixel area N having a predetermined number of vertical and horizontal pixels smaller than the number of pixels in the large judgment pixel area M is averaged gradation value. The smoothing process for replacing all the pixels by sequentially shifting the small determination pixel region N. (5) Binarization processing in which each pixel data after the smoothing processing is binarized to determine the presence or absence of a defect for each pixel. (6) A defect determination process that counts the number of pixels that are considered to be defects in the binarization process and determines that there is a defect when the number of pixels is a certain value or more.

Further, in order to detect both large and small defects, image analysis processing is performed in the following procedure. (1) A storage process of storing a gradation value of each pixel from the image sensor of the solid-state image sensor in an image memory. (2) For the original image data of one screen stored in the image memory, the maximum gradation value in the large determination pixel area M of a predetermined number of vertical and horizontal pixels is obtained, and each of the large determination pixel areas M is determined. A shadow contraction process that replaces the gradation value of a pixel with the obtained maximum gradation value for all pixels while sequentially shifting the large determination pixel region M. (3) With respect to the image data after the shadow contraction processing, the smallest gradation value in the large determination pixel area M is obtained, and the gradation value of each pixel in the large determination pixel area M is the obtained minimum gradation value. The shadow dilation processing is performed for all pixels while sequentially shifting the large determination pixel area M. (4) A first difference process for calculating, for each pixel, a gradation difference between the image data after the shadow expansion process and the original image data stored in the image memory. (5) A first binarization process in which each pixel data after the difference process is binarized to determine the presence or absence of a defect for each pixel. (6) Counting the number of defective pixels in the binarization process, and determining that there is a defect when the number of pixels is a certain value or more.
Defect determination processing. (7) A second difference process for calculating, for each pixel, a gradation difference between the image data after the shadow contraction process and the original image data stored in the image memory. (8) With respect to the pixel data after the difference processing, the gradation value of each pixel in each small judgment pixel area N having a predetermined number of vertical and horizontal pixels smaller than the number of pixels in the large judgment pixel area M is averaged gradation value. The smoothing process for replacing all the pixels by sequentially shifting the small determination pixel region N. (9) A second binarization process in which each pixel data after the smoothing process is binarized to determine the presence or absence of a defect for each pixel. (10) A second defect determination process in which the number of pixels that have been determined to be defects in the binarization process is counted and a defect is determined when the number of pixels is a certain value or more.

The first and second binarization processes may be carried out only for a certain inspection range within one screen.

The defect inspection apparatus for a transparent container according to the present invention comprises the following means. (1) A light projector that projects diffused light to a transparent container. (2) A solid-state image sensor camera in which light transmitted through a transparent container is received by an image sensor. (3) An image memory that stores the gradation value of each pixel from the image sensor. (4) For the original image data of one screen stored in the image memory, the maximum gradation value is determined for each large determination pixel area M having a predetermined number of vertical and horizontal pixels, and each of the large determination pixel areas M is determined. A shadow contraction processing unit that replaces the gradation value of a pixel with the obtained maximum gradation value for all pixels while sequentially shifting the large determination pixel region M. (5) With respect to the image data after the shadow contraction processing, the smallest gradation value in the large determination pixel area M is obtained, and the gradation value of each pixel in the large determination pixel area M is the obtained minimum gradation value. A shadow expansion processing unit that performs the replacement for all the pixels while sequentially shifting the large determination pixel region M. (6) First difference processing means for calculating, for each pixel, a gradation difference between the image data after the shadow expansion processing and the original image data stored in the image memory. (7) First binarization processing means for binarizing each pixel data after the difference processing and determining the presence or absence of a defect for each pixel. (8) Counting the number of defective pixels in the binarization process, and determining that there is a defect when the number of pixels is a certain value or more.
Defect determination processing means. (9) Second difference processing means for calculating, for each pixel, a gradation difference between the image data after the shadow contraction processing and the original image data stored in the image memory. (10) With respect to the pixel data after the difference processing, the gradation value of each pixel in each small judgment pixel area N having a predetermined number of vertical and horizontal pixels smaller than the number of pixels in the large judgment pixel area M is averaged gradation value. Smoothing processing means for performing replacement of all the pixels while sequentially shifting the small determination pixel region N. (11) Second binarization processing means for binarizing each pixel data after the smoothing process and determining the presence or absence of a defect for each pixel. (12) A second defect determination processing unit that counts the number of pixels that are regarded as defects in the binarization process and determines that there is a defect when the number of pixels is equal to or more than a certain value. (13) Exclusion signal output means for outputting a signal for excluding a product when one of the first and second defect determination processing means determines that there is a defect.

[0012]

In the method according to the present invention, in order to suppress the uneven pattern formed on the outer surface of the transparent container as a shadow in the image picked up by the solid-state image pickup device camera as much as possible, The rising angle of the convex portion is shaped so as to be 70 degrees or less from the Snell-Fresnel calculation formula regarding refraction and reflection, and diffused light from the projector is transmitted from the inside to the outside of the transparent container.
The inner surface of the transparent container is the light entrance surface, and the outer surface with the uneven pattern is the light exit surface.

The image analysis processing method according to the present invention is as follows. Explaining grayscale values of shades as shades, if the original image data is subjected to shadow contraction processing, then shadow expansion processing is performed, and the shadow expansion processed image data is subjected to difference processing with the original image data, a light shadow due to a pattern, And, the shadow due to the dark and large defect disappears, but the shadow due to the small dark defect and the shadow due to the relatively light and small defect darker than the pattern shadow remain and are detected as a defect by the subsequent binarization processing. .

After the original image data is subjected to the shadow contraction processing, and the shadow contraction processed image data is subjected to the difference processing with the original image data,
The light shadow due to the pattern disappears in the central part and remains in the peripheral part, and is further thinned by smoothing it, so that it will not be erroneously detected as a defect by the subsequent binarization process. On the other hand, the shadow due to the dark and large shade is likewise the central part disappears and the peripheral part remains due to the difference processing, and although it is slightly lightened by smoothing this, it still has sufficient darkness. It is detected as a defect by the binarization process. Further, the shadow due to the dark and small defect remains almost as it is in the difference processing and is detected as a defect by the binarization processing.

[0015]

EXAMPLES Examples of the present invention will be described in detail below. FIG. 1 shows an outline of a defect inspection apparatus according to the present invention applied for inspecting the bottom of a transparent glass container 1 for defects. In this device, an inspection hole 3 is provided at a predetermined position of an opaque table 2, and a transparent glass substrate 4 and a positioning plate 6 having a light transmitting hole 5 thereon are stacked in the inspection hole 3. The transparent glass container 1 to be inspected is placed on the positioning plate 6 in an upright state. The setting of the transparent glass container 1 at the inspection position in this way is detected by a sensor (not shown). In order to irradiate the transparent glass container 1 with highly diffused light from directly above, a light diffusing plate 7a for diffusing light from a built-in halogen lamp (not shown in FIG. 1), a ring-shaped fluorescent lamp 7b, and A projector 7 having a light diffusion plate 7c for diffusing the light of the fluorescent lamp is supported by a stand 8 downward. On the other hand, an L-shaped lens barrel 9 is installed below the inspection hole 3, and a CCD camera 12 having a reflecting mirror 10 at a corner bent at a right angle in the lens barrel 9 and a two-dimensional image sensor 11 at a tip portion thereof. Are arranged.

The diffused light from the projector 7 enters from the inside of the transparent glass container 1 to the inner surface of the bottom portion 1a thereof and is transmitted to the outer surface thereof, and further is transmitted through the transparent glass substrate 4 as it is from the light transmitting hole 5 for inspection. The light passes through below the hole 3, reflects at a right angle on the reflecting mirror 10 and enters the CCD camera 12, and the two-dimensional image sensor 11 makes X (for example, 750) in the horizontal direction (horizontal direction) per frame in the vertical direction. The image is decomposed (vertically) into Y (for example, 500) pixels. Then, the image data is stored in the image memory in the image analysis processing device 13 by the computer with, for example, 256 gradation values for each pixel, and is subjected to image analysis processing in real time as described later. In this example, since it is intended to mainly detect defects of 0.5 mm or more, which are problems in quality assurance as a product of the transparent glass container 1, the optical system has a length of 1 m.
The image magnification is assumed to be 7 pixels per m.

Next, an example of the transparent glass container 1 to be inspected in this embodiment will be described. FIG. 2 shows a half section thereof, and FIG. 3 shows a bottom surface thereof. This glass container 1 has a large convex portion 14 radially integrally formed on the outer surface of the bottom portion 1a, and a narrow gap concave portion (or groove) 15 deeply depressed in a V shape between the convex portions 14 is formed. Large petal-shaped uneven patterns are formed when viewed from the whole. Each of the convex portions 14 projects from the circular central concave portion 16 at the center of the outer surface of the bottom so that each portion rises in a semicircular shape while gradually widening the width to both sides and gradually increasing the height.

Since such a large uneven pattern is formed on the bottom portion 1a of the transparent glass container 1, when the light from the projector 7 is transmitted through the bottom portion 1a, it is largely refracted at the boundary of the unevenness, and the unevenness is caused. The pattern is directly captured by the CCD camera 12 as a shadow. In addition, the convex portion 14 or the concave portion 1
Refractive or reflective defects in 5 and 16, such as so-called falling powder, loading marks, transparent foreign substances such as bubbles and polka dots,
Alternatively, if there is rust, dirt, or an opaque foreign substance such as a slag, these defects are also photographed as shadows. Therefore, in the image analysis by the image analysis processing device 13, it is a shadow due to the uneven pattern or a shadow due to the defects. Is difficult to determine.

Therefore, the inventor of the present invention cannot prevent the shadow due to the uneven pattern of the bottom portion 1a of the transparent glass container 1 from appearing in the image of the CCD camera 12, or reduce the density even if it cannot be completely erased. As a result of repeated testing and research, I found that the following can be done to prevent the occurrence of as much as possible.

Light from the projector 7 is transmitted from the inside of the transparent glass container 1 to the bottom thereof as shown in FIG. 1, rather than being incident from the outside of the bottom 1a of the transparent glass container 1 to the outside thereof and transmitted to the inside thereof. It is better to let light enter the inner surface of 1a and transmit it to the outer surface. This is because the concavo-convex pattern is formed on the outer surface of the bottom portion 1a and there is no concavity and convexity on the inner surface. Therefore, the shadow of the concavo-convex pattern becomes lighter when the inner surface is the light incident surface.

It is preferable that the light projector 7 has high diffusivity and can emit a large amount of light in an oblique direction.
The projector 7 has a structure as shown in FIG. 4, and in addition to diffusing the light from the halogen lamp 7e housed in the case 7d by the light diffusing plate 7a which also serves as the bottom lid of the case 7d, the light diffusing plate 7a Place a ring-shaped fluorescent lamp 7b around the
The light from the fluorescent lamp 7b is also diffused by the ring-shaped light diffusing plate 7c and applied to the bottom 1a of the transparent glass container 1.

For comparison with the projector 7 having such a structure, a projector 7X as shown in FIG. 5 and a projector 7Y as shown in FIG. The projector 7X of FIG.
The structure in which the light from the halogen lamp 7e is guided to the convex lens 7g in the case 7d by the glass fiber 7f and further diffused by the light diffusing plate 7a, and the projector 7Y in FIG. 6 is different from the projector 7 in FIG. The structure does not include the diffusion plate 7c. According to the experiment, the projector 7X is the best in terms of the image clarity of the defect portion by the CCD camera 12,
Next, the projector 7Y and the projector 7 are the last, but the projector 7 is the best in that the shadow due to the uneven pattern of the transparent glass container 1 can be thinned and false detection of a defect portion can be reduced. Next is the projector 7Y, and the last is the projector 7X. This is because the projector 7 has the largest diffusion area, and moreover, the fluorescent lamp 7b can irradiate a large amount of light obliquely. Further, according to such a projector 7, the light amount can be easily adjusted by controlling the power supply voltage, and the degree of diffusion can be easily adjusted.

The degree of the shadow due to the uneven pattern mainly depends on the rising angle θ from the concave portions 15 and 16 to the convex portion 14 as shown in FIG. FIG. 8 is a graph obtained by simulating the relationship between the rising angle θ and the relative transmittance of light based on Snell-Fresnel calculation formulas regarding refraction and reflection. However, the positional relationship between the projector 7 and the transparent glass container 1 is as shown in FIG. As can be seen from this graph, the relative transmittance sharply decreases when the rising angle θ exceeds about 60 degrees. Therefore, if the rising angle θ is 70 degrees or less, preferably 60 degrees or less, a shadow due to the uneven pattern does not appear, or even if it does, it can be made thin.

Therefore, the concavo-convex pattern on the bottom 1a of the transparent glass container 1 is such that the rising angle from the gap concave portion 15 to the convex portion 14 and the rising angle from the central concave portion 16 to the convex portion 14 are not more than 60 degrees. It is molded at the time of manufacture.

By the way, it is relatively easy to mold the convex portion 14 so as to have such a rising angle, when the width of the concave portion is large, but the gap concave portion 15 of the transparent glass container 1 is formed. In the case of such a narrow space, it is difficult, and due to problems such as the attraction of the glass material by the mold and poor heat dissipation conditions, the gap recess 15 originally shaped as shown in FIG.
However, a small bulge 15a is formed in a part thereof as shown in FIG. 10, and this appears as a clear shadow in the captured image.

Therefore, as a result of repeated testing and research on this point, the present inventor was able to solve the above problem by devising the shape of the mold. That is, as shown in FIG. 11, in the mold 17, the convex portion 1 for forming the gap concave portion 15 is formed.
When the tip of No. 8 was rounded, a small bulge 15a was sometimes formed, but when the tip of the convex portion 18 was made sharp as shown in FIG. 12, it disappeared, and it was possible to mold according to the target shape shown in FIG. .

As described above, also in the transparent glass container 1 itself to be inspected, the uneven pattern is a shadow and the CCD
If the image analysis processing device 13 is formed so as not to be easily imaged by the camera 12, the unevenness pattern and the defect can be very easily identified in the image analysis processing device 13, and the defect detection accuracy is improved.

Next, the image analysis and defect inspection processing by the image analysis processing device 13 will be described. FIG. 13 shows an outline of the processing. When the computer of the image analysis processing device 13 confirms from the signal from the sensor that the transparent glass container 1 is set at the inspection position (step S1),
The captured image of the CCD camera 12 is captured and stored in the image memory (step S2), and the first image analysis process (hereinafter referred to as "original image") of one frame of image data stored in the image memory is performed. , Which is referred to as T1 processing) (step S3). In this T1 process, a small defect is detected as described later, and each pixel is binarized as to whether or not it is a defective pixel. Then, the number of pixels as the defect is counted and it is determined whether or not the number is a predetermined number or more (step S4).

Next, a second image analysis process (hereinafter referred to as T2 process) is performed on the original image (step S).
6). In this T2 processing, a large defect is detected as described later, and each pixel is binarized as to whether or not it is a defective pixel. Then, the number of pixels as the defect is counted and it is determined whether or not the number is a predetermined number or more (step S7). In either of step S8 and step S5,
If there is a defect, an exclusion signal is output to exclude the transparent glass container 1 (step S9).

FIG. 14 shows the first image analysis process (T1 process).
3 is a flowchart showing a specific example of the algorithm of FIG.
When one pixel in the original image of one frame is set as a pixel of interest, a pixel region including, for example, three pixels in each of the left, right, up, and down directions with respect to the pixel of interest, that is, the vertical and horizontal 7
After imagining the large determination pixel area of × 7 = 49 pixels and obtaining the maximum gradation value in this (step S11), the gradation value of each pixel in the large determination pixel area is determined to be the maximum rank. The value is replaced with the key value (step S12). That is, when viewing the screen of one frame as the coordinates of the X and Y axes, for example, when the coordinates of the pixel of interest are (4, 4), 49 pixels of the coordinates of (1, 1) to (7, 7) Is the determination target, and the pixel at the coordinates (3, 3), for example, is the maximum gradation value “160”.
Then, the gradation values of 49 pixels in the coordinate area of (1, 1) to (7, 7) are all set to "160".

Then, the operations of steps S11 and S12 are carried out for all the pixels of one frame while sequentially shifting the target pixel one by one (step S13). By such an operation, the dark part in the original image contracts, while the bright part expands. That is,
Shadows shrink due to defects. Regarding the pixels in the peripheral portion of the screen of one frame, the determination target area is less than 7 × 7 = 49 pixels, so an appropriate number of pixels less than 49 is set as the determination target area. The number is as large as 750 × 500 as described above, and the peripheral portion is actually outside the inspection target range for defect detection as described later, so that the processing at the peripheral portion does not adversely affect the defect detection.

Next, with respect to the image after the replacement with the maximum gradation value as described above, the vertical and horizontal 7 × 7 = 4 is reversed, contrary to the above operation.
After the minimum gradation value is calculated in the large determination pixel area of 9 pixels (step S14), the gradation value of each pixel in the large determination pixel area is replaced with the calculated minimum gradation value (step S14).
15). Then, the operations of steps S14 and S15 are performed for all the pixels in one frame while sequentially shifting the target pixel one by one (step S1).
6). By such an operation, contrary to the above, the dark part expands, while the bright part contracts. That is, the shadow due to the defect expands.

Next, the gradation values of the image after the minimum gradation value replacement as described above and the original image are calculated for each pixel (step S17). By this difference processing, a small dark portion remains in the original image, and a large dark portion disappears. In other words, small defects that appear as small shadows remain, but large defects that appear as large shadows disappear. After that, the image after the difference processing is binarized for each pixel only within a specific inspection range, that is, the gradation value based on a predetermined threshold is compared for each pixel, and a defect is generated according to the size. It is determined whether or not the pixel is a pixel (step S18). Then, the number of defective pixels is counted (step S1).
9).

Next, FIG. 15 shows the second image analysis processing (T2
It is a flowchart which shows the specific example of the algorithm of (processing). The steps S21 to S23 in the T2 process are the same as the steps S11 to S13 in the T1 process described above, and the shadow due to the defect is contracted by substituting the maximum gradation value. In the next step S24, the tone values of the image after the maximum tone value replacement and the original image are obtained for each pixel, and then the following smoothing processing is performed on the image after the difference.

That is, in the image after the difference, when a certain pixel is set as a pixel of interest, a pixel area including, for example, one pixel in each of the left, right, upper, and lower directions with respect to this pixel, that is, vertical and horizontal 3 × 3 = 9 pixels After virtualizing the small determination pixel area of, and obtaining an intermediate gradation value in this (step S
25), the gradation value of each pixel in the small determination pixel area is replaced with the obtained intermediate gradation value (step S26). For example, if the coordinates of the pixel of interest are (4,4), (3,3)
9 pixels of coordinates (5, 5) are subject to the determination, and if, for example, a pixel of coordinates (3, 3) has an intermediate gradation value of “25”, (3, 3) to ( The gradation values of nine pixels in the coordinate areas 5 and 5 are all set to "25".

Then, the operations of steps S25 and S26 are carried out for all the pixels of one frame while sequentially shifting the pixel of interest one by one (step S27). By such an operation, only the dark dark part in the original image remains and the other parts disappear. That is, the dark large defect and the dark small defect remain, but the light large defect and the light small defect are assimilated into the bright portion. After this,
The image after the replacement of the intermediate gradation value is binarized for each pixel only within a specific inspection range, that is, the gradation value based on a predetermined threshold value is compared for each pixel, and a defect occurs according to the size. After determining whether or not the pixel is a pixel (step S28), the number of defective pixels is counted (step S29).

Next, the algorithms of the above-mentioned T1 processing and T2 processing will be illustrated and explained in an easy-to-understand manner based on actual examples.

Now, the boundary (rise) of the uneven pattern on the bottom of the transparent glass container 1 is made into a straight line as shown in FIG. 16A, and the shadow in the original image (hereinafter referred to as pattern shadow) is Assuming that it appears as a light straight line with a thickness of about 1 mm as shown in FIG. 3B, the operation from step S11 to step S13 in the T1 process will result in (T
The same applies to the operations from step S21 to step S23 in the two processes), and the pattern shadow shrinks in size as shown in FIG.

When the operation from the next step S14 to step 16 in the T1 process is performed, the size of the pattern shadow is the same as that in FIG. 9A and the degree of shading is also in this figure. It is almost the same as (C). When this is subjected to the difference processing with the original image in step S17, the pattern shadow disappears as shown in FIG. Therefore, the pattern shadow is determined in step S1.
Since it has disappeared in this way in step 7, if it is binarized in the next step S18, it is naturally not judged as a defect.

Further, when the pattern shadow as shown in FIG. 6C is subjected to the difference processing with the original image in step S24 in the T2 processing, the central portion of the pattern shadow appearing as shown in FIG. It disappears like F) and the peripheral part remains. Next, when the operations of step S25 to step S27 are performed, the peripheral portion of the remaining pattern shadow becomes thin as shown in FIG.
Thereafter, in the T2 process, 2 is set based on the threshold value in step S28.
Since the value is digitized, the peripheral portion of the thinned pattern shadow disappears and is not judged as a defect.

On the other hand, the transparent glass container 1 has a defect as shown in FIG. 17A, and a part of the edge of the defect is as small as 1 mm or less as shown in FIG. Hereinafter, the small small defect shadow will appear as a dark small defect shadow, and the dark small defect shadow disappears by the operation from step S11 to step S13 in the T1 process (the same as the operation from step S21 to step S23 in the T2 process). However, the whole image is slightly brightened.

When the operations from step S14 to step 16 in the T1 process are performed, the dark small defect shadow remains disappeared, and the entire image becomes slightly dark. When this is subjected to the difference processing with the original image in step S17, a dark small defect shadow appears again as shown in FIG.
It is detected as a defect at 18.

When the pattern shadow as shown in FIG. 6C is subjected to the difference processing with the original image in step S24 in the T2 processing, a dark small defect shadow appears again as shown in FIG. When the operations from step S25 to step S27 are performed, as shown in FIG. 7G, although it is slightly thin, it remains as a dark small defect shadow. Therefore, if this is binarized in step S28, it is detected as a defect.

On the other hand, as shown in FIG. 18B, a part of the edge of the defect as shown in FIG. 18A has a sufficiently thick shadow with a thickness exceeding 1 mm (hereinafter referred to as a dark large defect shadow). ) Appears by the operation from step S11 to step S13 in the T1 process (the same as the operation from step S21 to step S23 in the T2 process), the dark large defect shadow is as shown in FIG. Shrinks with the same thickness.

When the operations from the next step S14 to step 16 in the T1 process are performed, the dark large defect shadow has the same size as that in FIG. It is almost the same as in FIG. When this is subjected to the difference processing with the original image in step S17, the dark large defect shadow disappears as shown in FIG. Therefore, since the dark large defect shadow has disappeared in this way in step S17, it cannot be detected even if it is binarized in the next step S18.

However, when the dark large defect shadow as shown in FIG. 7C is subjected to the difference processing with the original image in step S24 in the T2 process, the central portion of the dark large defect shadow appearing as shown in FIG. Disappears as shown in FIG. 6F, and the peripheral portion remains. Next, when the operations from step S25 to step S27 are performed, the peripheral portion of the remaining dark large defect shadow is shown in FIG.
Although it becomes a little thin as shown in, it still remains as a dark defect shadow. Therefore, if this is binarized based on the threshold value in step S28, it can be detected as a defect.

Next, the transparent glass container 1 has a defect as shown in FIG. 19 (A), and a part of the edge of the defect is darker than the pattern shadow but relatively light as shown in FIG. 19 (B). Moreover, a small shadow of 1 mm or less (hereinafter referred to as a light small defect shadow)
, The operation of steps S11 to S13 in the T1 process (T2
The same applies to the operations from step S21 to step S23 in the processing), the light small defect shadow disappears, and the entire image becomes slightly brighter.

When the following steps S14 to S16 in the T1 process are performed, the light small defect shadow remains disappeared, and the entire image becomes slightly dark. When this is subjected to the difference processing with the original image in step S17, a light small defect shadow appears slightly darker than the pattern shadow as shown in FIG. 8E, and therefore it can be detected as a defect in step S18.

When the pattern shadow as shown in FIG. 6C is subjected to the difference processing with the original image in step S24 in the T2 processing, a light small defect shadow appears again as shown in FIG. When the operations from step S25 to step S27 are performed, as shown in (G) of the same figure, a slight thin defect shadow remains. If this is binarized in step S28, it is possible to detect when there is a gradation difference from the pattern shadow,
If there is almost no difference, it cannot be detected as a defect.

As described above, the possibility of detecting large and small defects by the T1 process and the T2 process is summarized as follows. (1) Detection of defects with dark shadows of 1 mm or less is possible with either T1 processing or T2 processing. (2) The detection of a defect of a light shadow of 1 mm or less is possible in the T1 process and not possible (sometimes possible) in the T2 process. (3) Detection of dark defects exceeding 1 mm is not possible with T1 processing, but is possible with T2 processing. (4) The detection of faint defects exceeding 1 mm is impossible with either T1 treatment or T2 treatment, but the probability of such defects occurring is very small considering the manufacturing method of the transparent glass container. Not detected.

20 to 23 show the above-described processing in the case of each of the pattern shadow, the dark small defect shadow, the dark large defect shadow, and the light small defect shadow, the gradation value of the gradation of the image and the gradation by the difference processing. The difference is symbolized and illustrated.

In the case of the pattern shadow of FIG. 20, since the gradation differences after the difference processing in step S17 are all less than 10 gradations in the T1 processing, the threshold value in the binarization processing in step S18 is set to 20 with a margin, for example. Then, the pattern shadow will not be erroneously detected as a defect. In addition, after the smoothing operation from step S25 to step S27 in the T2 process, the maximum gradation difference may be close to 20.
If the threshold value of the binarization process in step S28 is set to about 30 in consideration of variations in sensitivity due to differences in the pixels of the camera 12, the pattern shadow will not be erroneously detected as a defect.

In the case of the dark small defect shadow shown in FIG. 21, since the gradation difference of the defect portion after the difference processing in step S17 is 30 to 80 in the T1 processing, the threshold value in the binarization processing in step S18 is set to the above value. If the number is 20, the defect can be sufficiently detected. Further, after the smoothing operation from step S25 to step S27 in the T2 process, since the gradation difference of the defect portion is 40 to 60, the threshold value of the binarization process at step S28 is set to 30 as described above.
If it is a degree, it can be sufficiently detected as a defect.

In the case of the dark large defect shadow shown in FIG. 22, since the gradation difference of the defect portion after the difference processing in step S17 is less than 10 in the T1 processing, the threshold value in the binarization processing in step S18 is set as described above. If it is set to 20, the disadvantage is that detection is impossible. On the other hand, after the smoothing operation from step S25 to step S27 in the T2 process, since the gradation difference of the defect portion is 40 to 70, the threshold value of the binarization process at step S28 is set to 30 as described above. If it is a degree, it can be sufficiently detected as a defect.

In the case of the light small defect shadow of FIG. 23, since the gradation difference of the defect portion after the difference processing in step S17 is 15 to 25 in the T1 processing, the threshold value in the binarization processing in step S18 is set to the above value. If the number is 20, the defect can be detected. However, after the smoothing operation from step S25 to step S27 in the T2 process, the gradation difference of the defect portion is 15 to 25, so that the threshold value of the binarization process at step S28 is about 30 Is impossible to detect.

As described above, in the present embodiment, the concave and convex pattern itself of the transparent glass container 1, that is, the rising angle from the concave portion to the convex portion is set so that the image captured by the CCD camera 12 is less likely to have a shadow. Even if the shadow appears, the light from the projector 7 is allowed to enter from the inside of the transparent glass container 1 to the inner surface of the bottom 1a and to be transmitted to the outer surface, and the structure of the projector 7 is also diffused. The transparent glass container 1 is highly transparent and capable of irradiating a large amount of light even from an oblique direction, and detects mainly small defects in the T1 process and large defects in the T2 process.
Even if a large concavo-convex pattern is formed on the bottom of the, the large and small defects can be accurately detected.

In the described embodiment, the transparent glass container 1 is used as the inspection object, the light from the projector 7 is transmitted to the bottom 1a of the transparent glass container 1, and an image of the transmitted light is taken by the CCD camera 12 to inspect for defects. However, it is possible to inspect for defects other than the bottom portion in the same manner. Further, it can be similarly applied to a transparent container made of a material other than glass.

Further, in the above-described embodiment, in the smoothing process, after obtaining the intermediate gradation value in each of the small determination pixel areas N, the gradation value of each pixel is replaced with the intermediate gradation value. Although the method of averaging is used, a method of using another gradation histogram (for example, replacement with mth gradation of N or less) or a method of smoothing by calculation (for example, N simple average ranks) The same effect can be obtained by substituting the key value or substituting the average gradation value calculated by N weighting coefficients). For example, the effect of replacing with the simple average gradation value is shown in FIGS.

[0059]

The effects of each claim of the present invention are as follows. (Claim 1) As for the uneven pattern itself of the transparent container, the rising angle of the convex part is shaped so as to be 70 degrees or less from the Snell-Fresnel calculation formula regarding refraction and reflection, and the diffused light from the projector is also formed. Is transmitted from the inside to the outside of the transparent container, and the inner surface of the transparent container is the light-entering surface and the outer surface having the uneven pattern is the light-exiting surface, so that the uneven pattern formed on the outer surface of the transparent container is In addition to minimizing the appearance of shadows in the captured image, a single screen image
In the digital processing of the
For each large judgment pixel area while shifting the judgment pixel area
After shrinking the shadow due to the uneven pattern, move to the small judgment pixel area
While inspecting each small judgment pixel area, the dark area due to the defect is detected.
As it appears, the irregularity pattern prevents erroneous detection and
The defect in the pattern can be accurately detected .

(Claims 2 and 5) In a transparent container having a large concavo-convex pattern formed therein, even if there is a large defect existing in the concave portion or the convex portion of the transparent container, a high speed can be achieved by a simple image processing method. Moreover, it can be detected accurately and easily. (Claims 3 and 6) Not only large defects but also small defects can be detected similarly.

(Claim 4) Since the binarization process is performed only for a certain inspection range in one screen, the invalid parts in the shadow contraction process, the shadow expansion process and the difference process which are the preceding operations (the periphery of the screen). Part) can be eliminated and defect detection can be performed without any trouble.

(Claim 7) Since a light projector having a high diffusivity and capable of irradiating a large amount of light in an oblique direction is used, erroneous detection due to an uneven pattern can be prevented more accurately.

(Claim 8) The flexibility of selection of the installation position of the solid-state image pickup device camera is high, and it can be protected.

[Brief description of drawings]

FIG. 1 is a schematic configuration diagram of an example of a defect inspection apparatus of the present invention.

FIG. 2 is a diagram showing a cross section in the left half of a transparent glass container, which is an example of an inspection target, and a diagram showing the outer surface in the right half.

FIG. 3 is a bottom view of the transparent glass container.

FIG. 4 is a diagram showing a structure of a light projector used in the defect inspection apparatus of the present invention.

FIG. 5 is a view showing a structure of a projector for comparison with the above projector.

FIG. 6 is a view showing the structure of another floodlight that was similarly subjected to a comparative experiment.

FIG. 7 is a diagram illustrating a rising angle from a concave portion to a convex portion having an uneven pattern in the transparent glass container.

FIG. 8 is a graph showing the relationship between the rising angle and the relative transmittance.

FIG. 9 is an enlarged cross-sectional view showing a normal shape of the recess.

FIG. 10 is an enlarged cross-sectional view showing an abnormal shape in which a part of the recess is raised.

FIG. 11 is an enlarged cross-sectional view of a part of a conventional mold for forming the uneven pattern.

FIG. 12 is an enlarged cross-sectional view of a portion of the improved mold.

FIG. 13 is a flowchart showing an outline of image analysis and defect inspection processing by the image analysis processing apparatus.

FIG. 14 is a flowchart showing a specific example of an algorithm of a first image analysis process of the above.

FIG. 15 is a flowchart showing a specific example of an algorithm of second image analysis processing.

FIG. 16 is a schematic diagram illustrating a transformation process of a pattern shadow by the first and second image analysis processes.

FIG. 17 is a schematic diagram illustrating the transformation process of a dark small defect shadow.

FIG. 18 is a schematic diagram illustrating a metamorphosis process of a dark large defect shadow.

FIG. 19 is a schematic diagram illustrating a metamorphosis process of a light small defect shadow.

FIG. 20 is an explanatory diagram illustrating the gradation change process of the pattern shadow by the above algorithm by symbolizing the gradation value of the light and shade of the image and the gradation difference by the difference processing.

FIG. 21 is an explanatory diagram illustrating a gradation change process of a dark small defect shadow similarly.

FIG. 22 is an explanatory diagram illustrating a gradation change process of a dark large defect shadow similarly.

FIG. 23 is an explanatory diagram illustrating a gradation change process of a small light defect shadow similarly.

FIG. 24 is an explanatory diagram illustrating a gradation change process of a pattern shadow when performing smoothing processing by replacing with a simple average gradation value.

FIG. 25 is an explanatory diagram illustrating a gradation change process of a dark small defect shadow similarly.

FIG. 26 is an explanatory diagram illustrating a gradation change process of a dark large defect shadow similarly.

FIG. 27 is an explanatory diagram illustrating a gradation change process of a small light defect shadow similarly.

[Explanation of symbols]

 1 Transparent glass container 7 Floodlight 7a Light diffusing plate 7b Fluorescent lamp 7c Light diffusing plate 7e Halogen lamp 11 Image sensor 12 CCD camera 13 Image analysis processing device 14 Concavo-convex convex part 15 Concavo-convex concave part

Claims (8)

[Claims] 1. A large uneven pattern formed on the outer surface of a transparent container.
The present invention is a method of inspecting existing defects, wherein the convex portion of the concave-convex pattern is molded so that the rising angle from the concave portion is 70 degrees or less, and diffused light from the projector is stored inside the transparent container. Image from a solid-state image sensor camera, and digitally process the image of one screen.
In, set a large determination pixel area of a predetermined number of vertical and horizontal pixels,
Each large judgment pixel area while shifting the large judgment pixel area
Shadow contraction processing that contracts the shadow due to the uneven pattern for each
Regarding the pixel data after the shadow contraction processing,
Set a small judgment pixel area with a predetermined number of vertical and horizontal pixels, and
Each small judgment pixel area is moved while shifting the small judgment pixel area of
A defect inspecting method for a transparent container, which further comprises: a defect determination process of detecting a dark portion due to a defect . 2. (1) A storage process of storing a gradation value of each pixel from an image sensor of the solid-state image sensor in an image memory, and (2) original image data of one screen stored in the image memory. For each of the large determination pixel regions M having a predetermined number of vertical and horizontal pixels, the maximum gradation value therein is calculated, and the gradation value of each pixel in the large determination pixel region M is replaced with the calculated maximum gradation value. That is, the shadow contraction processing is performed for all pixels while sequentially shifting the large determination pixel area M, and (3) the gradation difference between the image data after the shadow contraction processing and the original image data stored in the image memory is calculated. Difference processing for each pixel, and (4) For the pixel data after the difference processing, each pixel in each small determination pixel area N having a predetermined number of vertical and horizontal pixels smaller than the number of pixels in the large determination pixel area M. Replace the grayscale value of with the averaged grayscale value And a smoothing process that is performed for all pixels while sequentially moving the small judgment pixel region N, and (5) Binarizing each pixel data after the smoothing process to judge the presence or absence of a defect for each pixel. And (6) a defect determination process for counting the number of defective pixels in the binarization process and determining that there is a defect when the number of pixels is a predetermined value or more. 1. The transparent container defect inspection method according to 1. 3. (1) A storage process of storing a gradation value of each pixel from an image sensor of the solid-state imaging device camera in an image memory, and (2) original image data of one screen stored in the image memory. For each of the large determination pixel regions M having a predetermined number of vertical and horizontal pixels, the maximum gradation value therein is calculated, and the gradation value of each pixel in the large determination pixel region M is replaced with the calculated maximum gradation value. That is, the shadow contraction processing is performed for all pixels while sequentially moving the large judgment pixel area M, and (3) the smallest gradation value in the large judgment pixel area M is obtained for the image data after the shadow contraction processing, A shadow expansion process that replaces the gradation value of each pixel in the large determination pixel area M with the obtained minimum gradation value for all pixels while sequentially moving the large determination pixel area M, (4) The image data after the shadow expansion process and the image memo A first difference process for calculating a gradation difference from the original image data stored in each pixel for each pixel, and (5) binarizing each pixel data after the difference process to determine whether there is a defect for each pixel. First binarization processing for determination, and (6) Counting the number of defective pixels in the binarization processing, and determining that there is a defect when the number of pixels is a certain value or more.
Defect determination processing of (7), second difference processing of calculating a gradation difference between the image data after the shadow contraction processing and the original image data stored in the image memory for each pixel, (8) Regarding the pixel data after the difference processing, the gradation value of each pixel therein is replaced with the averaged gradation value for each small judgment pixel area N having a predetermined number of vertical and horizontal pixels smaller than the number of pixels of the large judgment pixel area M. That is, smoothing processing is performed on all pixels while sequentially moving the small determination pixel area N, and (9) second pixel data after the smoothing processing is binarized to determine the presence or absence of a defect for each pixel. A binarization process, and (10) a second defect determination process that counts the number of defective pixels in the binarization process and determines that there is a defect when the number of pixels is a certain value or more. The transparent container according to claim 1, characterized in that Defect inspection method. 4. The defect inspection method for a transparent container according to claim 3, wherein the first and second binarization processes are performed only for a certain inspection range in one screen. 5. (1) A light projector for projecting diffused light to a transparent container, (2) a solid-state image sensor camera for receiving light transmitted through the transparent container by an image sensor, and (3) a sensor from the image sensor. An image memory that stores the gradation value of each pixel, and (4) the maximum gradation of the original image data of one screen stored in the image memory for each large determination pixel region M of a predetermined number of vertical and horizontal pixels. A shadow contraction processing unit that obtains a value and replaces the gradation value of each pixel in the large determination pixel area M with the obtained maximum gradation value for all pixels while sequentially shifting the large determination pixel area M. And (5) difference processing means for calculating, for each pixel, a gradation difference between the image data after the shadow contraction process and the original image data stored in the image memory, and (6) the pixel after the difference process. Regarding the data, the pixels of the large determination pixel area M Smoothing is performed for all the pixels by sequentially shifting the small determination pixel region N so as to replace the gradation value of each pixel in each small determination pixel region N with a smaller number of vertical and horizontal predetermined pixels. Processing means, (7) binarization processing means for binarizing each pixel data after the smoothing processing to determine the presence / absence of a defect for each pixel, and (8) a binarization process for identifying a defect. A defect inspection device for a transparent container, comprising: a defect determination processing unit that counts the number of pixels and determines that there is a defect when the number of pixels is a certain value or more. 6. (1) A projector for projecting diffused light to a transparent container, (2) a solid-state image sensor camera for receiving light transmitted through the transparent container by an image sensor, and (3) a sensor from the image sensor. An image memory that stores the gradation value of each pixel, and (4) the maximum gradation of the original image data of one screen stored in the image memory for each large determination pixel region M of a predetermined number of vertical and horizontal pixels. A shadow contraction processing unit that obtains a value and replaces the gradation value of each pixel in the large determination pixel area M with the obtained maximum gradation value for all pixels while sequentially shifting the large determination pixel area M. (5) For the image data after the shadow contraction processing, the smallest gradation value in the large determination pixel area M is calculated, and the gradation value of each pixel in the large determination pixel area M is calculated to the lowest rank. Substituting tonal values sequentially shifts the large determination pixel area M And (6) a first difference for calculating, for each pixel, a gradation difference between the image data after the shadow expansion processing and the original image data stored in the image memory. Processing means; (7) first binarization processing means for binarizing each pixel data after the difference processing to judge the presence or absence of a defect for each pixel; and (8) a defect by the binarization processing. The number of the determined pixels is counted, and if the number of pixels is a certain value or more, it is determined that there is a defect.
(9) second difference processing means for calculating, for each pixel, a gradation difference between the image data after the shadow contraction processing and the original image data stored in the image memory; 10) Regarding the pixel data after the difference processing, the gradation value of each pixel in each small judgment pixel area N having a predetermined number of vertical and horizontal pixels smaller than the number of pixels in the large judgment pixel area M is set as an averaged gradation value. Smoothing processing means for performing replacement on all pixels while sequentially moving the small determination pixel area N, and (11) binarizing each pixel data after the smoothing processing to determine the presence or absence of a defect for each pixel. Second binarization processing means, and (12) second defect determination processing means that counts the number of defective pixels in the binarization processing and determines that there is a defect when the number of pixels is a certain value or more. (13) The first and second Defect determination process while even with defect and determined to defect inspection apparatus of a transparent container, characterized by comprising and an elimination signal output means for outputting a signal for eliminating product when the means. 7. The light projector comprises a halogen lamp, a light diffusing plate for diffusing the light from the halogen lamp, a fluorescent lamp arranged around the diffusing plate, and a light for diffusing the light from the fluorescent lamp. The defect inspection device for a transparent container according to claim 5, comprising a diffusion plate. 8. The solid-state imaging device camera and a reflecting mirror for directing transmitted light from a transparent container installed at an inspection position to the solid-state imaging device camera are arranged in a lens barrel. 5. The transparent container defect inspection apparatus according to 5 or 6.