JP2003156451A - Defect detecting device - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a defect detecting device that can detect a defect of low contrast having a small concentration difference with respect to a circumference, without using a memory and a computing element of a large data width. SOLUTION: This defect detector 1 of the present invention for detecting the defect of a subject to be inspected S is characterized in that it has an image picking-up means 4 for image-picking up the subject to be inspected, an information compressing means 16 for compressing information by substituting a data of concentration values in respective picture elements in the inspected subject image-picked up by the image picking-up means to at least two kinds of central values, based on a prescribed threshold value, a filter processing means 18 for integration-filter-processing the data of the concentration values substituted with the central values by the information compressing means, in a prescribed block unit, and a defect determining means 2 for comparing the data of the concentration values processed by the filter processing means with a prescribed reference data, so as to determine the presence of the defect in the subject to be inspected.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a defect detection apparatus for detecting defects on the surface of an object, and more particularly to a defect detection apparatus capable of detecting low-contrast defects such as light stains, spots and shallow scratches.

[0002]

2. Description of the Related Art At present, a defect detecting apparatus which is widely used to optically detect a defect on an object surface irradiates an object to be inspected with light and picks up reflected light or transmitted light of the light by an image pickup means. By doing so, defects such as irregularities on the surface of the object, foreign matter, and optical distortion are detected. Among the defects to be detected by the defect detection device, defects such as light stains, spots, and shallow scratches have a large defect area, but the difference in shade between the defective portion and the normal portion is small, so it is difficult to detect. is there.
In particular, in the case of an object with spots, it is more difficult to detect defects due to the influence of spots.

It is difficult to detect such a low-contrast defect having a low contrast by performing a determination process on the image data picked up by the image pickup means for each single pixel. Therefore, in order to detect a low-contrast defect, an integrating filter is used which sets a filter block in which an appropriate number of pixels are collected and integrates the density values of the pixels in the filter block. By using such an integrating filter, it is possible to emphasize the difference in shade and to suppress the influence of the change in the texture of the surface of the inspection object.

Next, with reference to FIG. 5, detection of a low contrast defect using an integrating filter will be described. Figure 5 (a)
Indicates data obtained by imaging a normal portion (a portion having no defect) of the object to be inspected by the image capturing means and digitalizing the grayscale of each pixel. FIG. 5B shows data of a light stain defect portion of the inspection object, and a range surrounded by a double line in the data is a portion to be detected as a light stain defect. The vertical arrangement of the data corresponds to the moving direction of the inspection object, and the horizontal arrangement corresponds to the arrangement of the pixels imaged by the image pickup means, and both the vertical and horizontal directions are imaged. This is a part of the image data extracted.

FIG. 5 (c) shows the result of subtracting the data of (a) from the data of FIG. 5 (b). Referring to FIG. 5C, the difference in the density value of each pixel between the normal portion of the inspection object and the light stain defect portion is small, and the difference from the individual value of each pixel in FIG. It can be seen that it is difficult to reliably recognize the stain defect portion. Therefore, the density values of the respective pixels are integrated within a predetermined block, and the normal portion and the light stain defect portion are compared by the integrated value. Here, as shown by broken lines in FIGS. 5A and 5B, nine pieces of data in three columns and three rows are integrated as one block.

When all the data of the portion surrounded by the broken line in FIG. 5A, which is a normal portion, is added up, the integrated value becomes 900, and the portion surrounded by the broken line in FIG. Is 880. Therefore, the integrated value of the normal portion is 900, and the integrated value of the light stain defect portion is 8
There are 20 differences between 80 and 900 and 880
By setting a threshold value to an appropriate value between and, it becomes possible to reliably identify the thin stain defect portion. It is possible to detect a thin stain defect existing in the inspection object by sequentially performing such an integration filter calculation in each part of the inspection object.

[0007]

However, in reality, the thin stain defect portion which is usually assumed in the defect inspection is larger than the above-described block of 3 columns by 3 rows and in some cases 128 columns by 128 columns. , It is necessary to integrate pixel data of 128 rows or more in the horizontal direction as one block. For example, when the density value of each pixel is digitized with 8 bits and 256 gradations, if the pixel data block has 4 columns in the vertical direction and 4 rows in the horizontal direction, the maximum integrated value is 4 × 4 × 256 = 4096, vertical 128.
If there are 128 rows and columns, the maximum integrated value is 128 x 128
X256 = 4194304. Therefore, if the block is 4 columns vertically and 4 rows horizontally, 12 bits, 128 columns vertically, 1 horizontally
In the case of 28 rows, there is a problem that a memory and a calculator having a data width as large as 22 bits are required to integrate the density values.

Therefore, it is an object of the present invention to provide a defect detecting apparatus capable of detecting a low contrast defect having a small density difference from the surroundings without using a memory having a large data width and a computing unit. There is.

[0009]

In order to solve the above-mentioned problems, the present invention provides a defect detecting apparatus for detecting a defect of an object to be inspected, and an imaging means for imaging the object to be inspected. Information compression means for compressing information by replacing the data of the density value of each pixel in the inspection object imaged by the imaging means with at least two types of representative values based on a predetermined threshold value, and the information compression means. The density value data replaced by the representative value is subjected to integration filter operation processing in predetermined block units, and the density value data processed by the filter operation processing means is compared with predetermined reference data. And a defect determining unit for determining the presence or absence of a defect in the inspection object by performing the above.

In the present invention thus constructed,
The object to be inspected is imaged by the imaging means. The density value of each pixel of the image data captured by the image capturing means is
It is replaced by at least two types of representative values by the information compression means. For example, the density value of each pixel is 0 to 2
If the threshold value is set to 100 in the range of 55, the information compressing means sets the density value of 0 to 100 to
Replace with a representative value, for example, 0, 101 to 255
The density value of is replaced with another representative value, for example, 1. Next, the filter calculation processing means integrates the representative values replaced by the information compression means in predetermined block units.
The defect determination means determines whether or not there is a defect in the inspection object by comparing the integrated value calculated by the filter calculation processing means with predetermined reference data.

With this configuration, since the density value of each pixel is replaced by the representative value by the information compression means, the range of possible density values of each pixel is wide, and even when the number of pixels to be integrated is large, Accumulation can be performed by a memory having a relatively small data width and an arithmetic unit.

Further, the information compression means can be configured to replace the data of the density value of each pixel with three kinds of representative values based on two threshold values. In the present invention thus configured, if the density value of the defective portion of the inspection object is higher than the density value of the normal portion of the inspection object, and in any case, the defect Can be detected.

Further, the defect detecting apparatus of the present invention further comprises peak density detecting means for detecting the maximum value and / or the minimum value of the data of the density value in a predetermined block unit, and the defect determining means is the peak density. The defect type of the inspection object can be determined based on the maximum value and / or the minimum value of the density value data detected by the detection means.

In the present invention thus constructed,
Even when a defect having a small density difference with the surroundings and a defect having a large density difference with the surroundings cannot be identified only by the integrated value of the density values calculated by the filter calculation processing means, a defect having a small density difference with the surroundings is detected. , Can be surely identified.

[0015]

DESCRIPTION OF THE PREFERRED EMBODIMENTS Next, embodiments of the present invention will be described with reference to the accompanying drawings. FIG. 1 is a schematic configuration diagram of a defect detection device according to an embodiment of the present invention. As shown in FIG. 1, a defect detection device 1 according to an embodiment of the present invention includes an irradiation unit 2 for irradiating a traveling inspection target S with light.
An image pickup unit 4 for picking up the light emitted from the irradiation unit 2 and reflected by the inspection target S, and the image data captured by the image pickup unit 4 are processed to obtain the inspection target S
And an image processing device 6 for detecting the defect.
In the present embodiment, the light emitted from the irradiation means 2 and reflected by the inspection object S is imaged by the image pickup means 4, but the transmitted light is used as the irradiation means 2 to detect the inspection object S. It is also possible to use an image pickup means to pick up the light that has passed through.

In the present embodiment, the object S to be inspected is a sheet-like object traveling on the production line, but the object S to be inspected may be something other than the sheet-like object, and the object S to be inspected is fixed. However, the image pickup means 4 may be moved. Examples of the sheet-like material include a continuous or sheet-shaped metal plate, film, paper, nonwoven fabric, resin plate, glass plate and the like. The irradiation unit 2 is configured to irradiate linear light in a direction orthogonal to the moving direction of the inspection object S. As the irradiation unit 2, a high-frequency lighting fluorescent lamp, a rod illumination, a fiber illumination in which optical fibers are arranged in a line, an LED (light emitting diode) illumination, or the like can be used. The image pickup means 4 converts the incident light into an electric signal and picks up an image C
A device using a solid-state image sensor such as a CD camera can be used. When the inspected object S is run, a line sensor in which the light receiving portions are arranged in one row can be used as the image pickup device of the image pickup unit 4. A two-dimensional image can be obtained by sequentially capturing a one-dimensional image in a direction orthogonal to the moving direction of the inspected object S during traveling with a line sensor.

Next, the internal structure of the image processing apparatus 6 will be described with reference to FIG. The image processing device 6 includes an image input unit 8 for converting an analog image signal obtained by the image pickup unit 4 into a digital value, and correction of uneven irradiation of light with respect to the digital signal obtained by the image input unit 8. Image pre-processing unit 10 for performing the above, a high-contrast defect detection unit 12 for detecting a defect with a high contrast such as a black dot defect from the corrected digital signal, and a defect with a low contrast such as a light stain defect. Low-contrast defect detection unit 14 for displaying the defects detected by the high-contrast defect detection unit 12 and the low-contrast defect detection unit 14, or a data output unit 24 for outputting defect information to another external device. And. The high contrast defect detection unit 12 can be omitted if the detection of the high contrast defect is not required.

In the present embodiment, the image input section 8 includes an A / D converter that converts an analog image signal into a digital signal of 8 bits, that is, 256 density gradations. Image input section 8
Also has a built-in buffer memory for matching the timing with the subsequent image preprocessing unit 10. In addition, the image input unit 8
May be integrated with the imaging means 4. Image preprocessing unit 1
0 indicates correction of unevenness in the amount of light emitted by the irradiation unit 2, shading correction for correcting a decrease in the amount of light around the image pickup lens of the image pickup unit 4, offset voltage of each image pickup device included in the image pickup unit 4, and variation in sensitivity. Correction, correction of noise generated by fixed patterns, smoothing filter to reduce the influence of the texture of the inspected object whose texture change is large even in normal areas, and other processing functions to facilitate subsequent inspection processing Equipped with. The high-contrast defect detection unit 12 detects only defects with high contrast such as black spot defects by a conventional detection method.

The low contrast defect detection section 14 is arranged in parallel with the high contrast defect detection section 12. The low-contrast defect detection unit 14 compresses information by replacing the density value, which is a digital signal processed by the image preprocessing unit 10, with three types of values based on a predetermined threshold value, and an information compression unit 16. A filter calculation processing means 18 for performing integration filter calculation processing on the density value data replaced by the three values by the means 16, and a maximum value and a minimum value among the density values in the block for performing the integration operation. And a peak concentration detecting means 20.

The low-contrast defect detecting section 14 having the information compressing means 16, the filter arithmetic processing means 18, and the peak density detecting means 20 can be constituted by a computer and software for operating the computer. The data output unit 24 is a display such as a display or a printer for displaying the detection result of the defect with high contrast detected by the high contrast defect detection unit 12 and the defect with low contrast detected by the low contrast defect detection unit 14. It is provided with a device, an alarm device that warns the occurrence of a defect based on the detection result, and a notification device that notifies the marking device of the result.

Next, the operation of the defect detecting device according to the embodiment of the present invention will be described. First, the irradiation means 2 is activated to irradiate the inspection object S with linear light, and the inspection object S is caused to travel at a predetermined speed. The light emitted from the irradiation means 2 and reflected by the inspection object S is imaged by the imaging means 4. The line sensor built in the image pickup means 4 acquires one-dimensional analog data. The data acquired by the image pickup unit 4 is sent to the image input unit 8 built in the image processing apparatus 6. In the image input unit 8, the one-dimensional analog data sent from the image pickup unit 4 is converted into 8-bit digital data indicating the density value of each pixel. This digital data is subjected to various corrections such as light amount unevenness correction and shading correction in the image preprocessing unit 10.

The digital data corrected by the image preprocessing unit 10 is sent to the high contrast defect detection unit 12, and the defect with high contrast is detected there. In the high-contrast defect detection unit 12, the measured density value of each pixel is
Defects are detected by comparing a preset threshold value for each single pixel. This threshold value is set to a value that is sufficiently distant from the fluctuation width due to the formation of the defect-free portion of the inspection object S depending on the application. In this embodiment, the density value is 9
Pixels of 0 or less are determined as high contrast defects.

Next, the operation of the low contrast defect detection section 14 will be described with reference to FIGS. 3 and 4. The same digital data sent to the high-contrast defect detection unit 12 is also sent to the low-contrast defect detection unit 14, and the defect with low contrast is detected there. First, the digital data is sent to the information compression means 16 of the low contrast defect detection unit 14. 3A and 3B show a part of the density values sent to the information compression means 16. The row of the density values in the horizontal direction is the data of the density values that are temporarily acquired by the line sensor of the imaging unit 4, and the data of each row is sequentially acquired as the inspection target S moves. FIG. 3A is an example of the density value of the normal portion, and FIG. 3B is an example of the density value of the light stain defect portion.

The density value data sent to the information compression means 16 is classified by two threshold values and replaced with three kinds of data. In the present embodiment, the average of the density values of the pixels in the normal portion measured in advance is 100, so the first threshold value is set to 101 and the second threshold value is set to 99. Therefore, the data of the density value of 101 or more which is the first threshold value is replaced with the representative value "1", and the data of the density value of 99 or less which is the second threshold value is replaced with the representative value "-1". The data of the density value of 100, which is a value between the threshold value of 1 and the second threshold value, is replaced with the representative value “0”. FIG. 3 (c) is the result of replacing the density value of the normal portion in FIG. 3 (a) with three representative values, and FIG. 3 (d) is the density value of the light stain defect portion in FIG. 3 (b). Is the result of ternary conversion.

The data ternarized by the information compression means 16 is sent to the filter calculation processing means 18. The filter calculation processing means 18 integrates the ternary data in a predetermined data block. In the present embodiment, FIG.
As indicated by the broken line, vertical 3 columns and horizontal 3 rows are used as data blocks for integration. The value in the broken line in FIG. 3C, which is the data of the normal portion, is 0, and the value in the broken line in FIG. 3D, which is the data of the light stain defect portion, is -9.

On the other hand, FIG. 4 shows an example of data of defects other than the light stain defect. FIG. 4A is an example of a black dot defect having a large density difference from the surroundings and a small defect size.
(B) is an example of a black defect having a large density difference from the surroundings and a large defect size. The portions surrounded by double lines in FIGS. 4A and 4B are the black spot defect and the black defect portion, respectively. The data of the density values shown in FIGS. 4A and 4B are converted into the information compression means 1.
The results of ternarization by 6 are shown in FIGS. 4 (c) and 4 (d). When the data within the broken line in FIGS. 4C and 4D is integrated by the filter calculation processing means 18, it is 0 for a black dot defect.
And a black defect is -9.

Therefore, it can be seen that the normal portion and the black spot defect, and the light stain defect and the black defect have the same integrated value by the filter calculation processing means 18, respectively. From the above, if only the integrated value by the filter calculation processing means 18 is used,
It can be seen that it may be difficult to discriminate between the normal portion and the black spot defect, and also to discriminate between the light stain defect and the black defect. Therefore, in order to distinguish these defects, the peak density detecting means 20 is provided in the low contrast defect detecting section 14.

Data having the same density value as that sent to the information compressing means 16 is sent from the image preprocessing section 10 to the peak density detecting means 20. The peak density detecting means 20 detects the maximum value and the minimum value of the data in the data block, and calculates the difference between these values and the average density value in the normal part. In the normal part data shown in FIG. 3A, the maximum value “102” and the minimum value “97” are detected from the data block surrounded by the broken line, and these values and the average density value in the normal part are obtained. Differences “2” and “3” from 100 are calculated. The maximum value "99" is shown in the lightly soiled area in FIG. 3 (b).
And the minimum value "96" is detected and the differences "1" and "4" are calculated. Similarly, in the black dot defect of FIG. 4A, the maximum value “102”, the minimum value “80”, the differences “2” and “20”,
In the black defect of FIG. 4B, the maximum value is "99", the minimum value is "80", and the differences are "1" and "20".

Next, in the defect judgment means 22, the integrated value obtained by the filter calculation processing means 18 and the difference between the maximum value and the minimum value obtained by the peak density detection means 20 and the average density value in the normal portion. The type of defect is determined based on That is, the absolute value of the integrated value is large,
Defects with a small difference between the maximum and minimum values and the average density value,
It is judged as a light stain defect. In the present embodiment, when the absolute value of the integrated value is 5 or more, and the difference between the maximum value and the minimum value and the density average value is 7 or less, it is determined as a thin stain defect.

Since the absolute value of the integrated value of the data of the normal portion in FIG. 3 (a) is smaller than 5, it is not judged as a thin stain defect. 3B, the absolute value of the integrated value is larger than 5, and the difference between the maximum value and the density average value and the difference between the minimum value and the density average value are both smaller than 7. For,
It is judged as a light stain defect. Further, in the black dot defect of FIG. 4A, the absolute value of the integrated value is smaller than 5, and the difference between the minimum value and the average density value is larger than 7, and therefore, it is not determined as a thin stain defect. Further, in the black defect of FIG. 4B, the difference between the minimum value and the density average value is larger than 7, and therefore, it is not determined as the thin stain defect. Therefore, only the thin stain defective portion as shown in FIG. 3B is determined as the thin stain defective portion by the defect determining means 22.

In the present embodiment, the type of defect is determined based on the difference between the maximum and minimum values in the data block and the average density value of the normal portion, but the maximum and minimum values in the data block are determined. The difference between the minimum value and the maximum value and the minimum value in a predetermined data block in the normal portion may be calculated, and the type of defect may be determined based on those values. Alternatively, the defect type may be determined based on the difference between the maximum value and the minimum value in the data block.

Finally, high-contrast defects such as black spot defects and black defects detected by the high-contrast defect detection unit 12, and contrasts such as light stains and spots detected by the low-contrast defect detection unit 14 are detected. Low defects are displayed by the data output unit 24.

In the defect detecting apparatus according to the embodiment of the present invention, the image information in a predetermined data block imaged by the image pickup means is replaced by three representative values by the information compression means 16 and then integrated, so that Accumulation calculation can be performed using a memory with a small data width. As a result, the cost of a computer or the like used for the integration calculation can be reduced, and the calculation time required for the integration calculation can be shortened.

Although the embodiment of the present invention has been described above, various modifications can be made to the above embodiment. In particular,
The set value such as the threshold value set in the present embodiment can be arbitrarily changed according to the application. Further, in the present embodiment, the detection of the light stain defect is described, but the present invention can also be used for the detection of a low contrast defect other than the light stain defect. Further, in the present embodiment, the case where the density value of the defective portion is smaller than the density value of the normal portion has been described, but when the defective portion is brighter than the normal portion, that is, the density value of the defective portion is higher than that of the normal portion. The present invention can be applied in the same manner even when the density is larger than the density value. Further, in the present embodiment, the integration operation is performed on the data blocks of vertical 3 columns and horizontal 3 rows, but the present invention can be applied to the integration of data blocks of any size. In addition, if the shape of the assumed defect is elongated, the number of columns and rows may be different and elongated data blocks may be used.

Further, in the present embodiment, the density data is replaced with three representative values by the information compressing means, but two or more threshold values are set and the data is replaced with three or more representative values. May be. On the contrary, when the defect to be detected is limited to a defect that is darker than the normal part or a defect that is brighter than the normal part, the density value data is converted into two representative values by a single threshold value. You may replace it. Further, the value of each representative value can be arbitrarily determined depending on the application. Further, in the present embodiment, the density value data replaced with the representative value in the data block is directly integrated by the filter calculation processing means, but the density value data is integrated with a predetermined weighting. You may go. Further, in the present embodiment, the density value data in the data block is numerically added by the filter calculation processing means, but as the integration calculation,
The number of times each representative value appears in the data block may be counted.

[0036]

According to the defect detecting apparatus of the present invention, it is possible to detect a defect having a low contrast with a small density difference from the surroundings without using a memory and an arithmetic unit having a large data width.

[Brief description of drawings]

FIG. 1 is a schematic diagram showing an entire defect detection device according to an embodiment of the present invention.

FIG. 2 is a block diagram showing a configuration of an image processing apparatus of the defect detection apparatus according to the embodiment of the present invention.

FIG. 3 is a diagram showing an example of data of density values of a normal portion and a light stain defect portion of an inspection object.

FIG. 4 is a diagram showing an example of data of density values of a black spot defect portion and a black defect portion of an inspection object.

FIG. 5 is an explanatory diagram for explaining data processing in a conventional defect inspection apparatus.

[Explanation of symbols]

S Inspected object 1 Defect detection device according to an embodiment of the present invention 2 irradiation means 4 Imaging means 6 Image processing device 8 Image input section 10 Image preprocessor 12 High contrast defect detector 14 Low contrast defect detector 16 Information compression means 18 Filter calculation processing unit 20 Peak concentration detection means 22 Defect determination means 24 Data output section

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Claims (3)

[Claims] 1. A defect detection device for detecting a defect of an inspection object, comprising: imaging means for imaging the inspection object; and a density value of each pixel in the inspection object imaged by the imaging means. An information compression unit that compresses information by replacing the data with at least two types of representative values based on a predetermined threshold value, and the data of the density value replaced by the representative value by the information compression unit in predetermined block units. And a defect determination means for determining the presence or absence of a defect of the inspection object by comparing the density value data processed by the filter operation processing means with predetermined reference data. A defect detection device comprising: 2. The defect detecting apparatus according to claim 1, wherein the information compression means replaces the data of the density value of each pixel with three types of representative values based on two threshold values. 3. A peak density detecting means for detecting a maximum value and / or a minimum value of density data in the predetermined block unit is further provided, and the defect determining means detects the peak density detecting means. The defect detection apparatus according to claim 1, wherein the defect type of the inspection object is determined based on the maximum value and / or the minimum value of the data of the density value.