JP2006189294A - Inspection method and device of irregular defect - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an inspection method and a device of an irregular defect capable of acquiring high inspection performance without being influenced by the shape or the size of the irregular defect. <P>SOLUTION: This inspection method of the irregular defect has a noise removal process relative to an input image; a primary differential process for applying a primary differential filter in the four directions, namely, in the longitudinal direction, the lateral direction and two oblique directions, relative to a noise removed image; an absolute-value acquiring process for acquiring an absolute-value image from a primary differential image; a maximum-value acquiring process for acquiring a maximum-value image from each absolute-value image in the four directions; a binarization process for applying a prescribed threshold to the maximum-value image; a labeling process relative to a binary image; a domain extraction process for extracting a label A domain from a labeling image; an irregularity value operation process for operating an irregularity value which is a difference of pixel values in an irregularity domain and a non-irregularity domain in the label A domain; and a quality determination process for determining the quality of the label A domain based on the irregularity value. This device to which the method is applied is also provided. <P>COPYRIGHT: (C)2006,JPO&NCIPI

Description

  The present invention belongs to the technical field of inspecting whether or not optical characteristics of an object are uniform. In particular, the present invention relates to a mura defect inspection method and apparatus capable of obtaining high inspection performance without being affected by the shape and size of the mura defect.

In many conventional inspection apparatuses that inspect a mura defect, a secondary differential filter is applied in a process of extracting a mura defect from an input image obtained by imaging an object (Patent Documents 1 and 2). ). In order to accurately extract unevenness using a secondary differential filter, it is necessary to apply a secondary differential filter that conforms to the shape and size of the uneven defect, as shown in FIG. However, it is impossible to know the shape and size of the mura defect generated in the object before inspection. In addition, there are limits to the types of secondary differential filters that can be prepared and applied in advance, and they are not necessarily compatible. Therefore, sufficient inspection performance could not be obtained.
JP 2000-199745 A JP 2002-28059 A

  The present invention has been made to solve the above problems. An object of the present invention is to provide a method and apparatus for inspecting a mura defect capable of obtaining a high inspection performance without being affected by the shape and size of the mura defect.

According to a first aspect of the present invention, there is provided a method for inspecting a mura defect, wherein a noise removal process is performed in which a noise removal filter is applied to an input image to obtain a noise removal image; Applying a first-order differential filter in two directions to obtain a first-order differential image in the four directions by applying a first-order differential filter in two directions, and replacing the pixel values of the pixels in the first-order differential image in the four directions with their absolute values The absolute value process for obtaining the absolute value image in the four directions and the pixel value of the pixel at the same position in the absolute value image in the four directions are compared to obtain a maximum value image having the largest pixel value as the pixel value of the pixel. A maximumization process, a binarization process for obtaining a binary image by applying a predetermined threshold to the maximum value image, a labeling process for obtaining a labeling image by labeling the binary image, and the labeling Label from image An area extraction process for extracting a label A area having a group number A, a mura value calculation process for calculating a mura value which is a difference between pixel values of a mura area and a non-mura area in the label A area, and the mura value. And a pass / fail determination process for determining pass / fail of the label A area based on the pass / fail.
The mura defect inspection method according to claim 2 of the present invention is the mura defect inspection method according to claim 1, wherein the mura value calculation step extracts a pixel B constituting a boundary of the label A region and extracts a boundary image. , A coordinate extraction process for extracting the coordinates (X [B], Y [B]) of the pixel B constituting the boundary, and the coordinates from the absolute value image in the four directions by focusing on the coordinates An image extraction process for extracting the absolute value image having the largest pixel value of the pixel, a change direction extraction process in which the direction of the primary differential filter applied to the extracted absolute value image is the change direction of the pixel value, and the coordinates To extract the coordinates of the pixel from the first to the second boundary of the label A area in the change direction to obtain the intersection area coordinates, and the maximum pixel value and the minimum pixel value of the intersection area coordinates in the noise-removed image A pixel value difference calculation process for calculating a pixel value difference which is a difference between elementary values, an average value of the pixel value differences obtained for all the pixels B constituting the boundary, and an average value thereof as the unevenness value And an average value calculating process.
A mura defect inspection apparatus according to claim 3 of the present invention is a mura defect inspection apparatus comprising a line sensor camera, a conveying means, and a processing means, wherein the line sensor camera is a main part of a linear imaging region. Scanning is performed to pick up an image of the inspection target article and output an imaging signal, the transporting means transports the inspection target article in the sub-scanning direction with respect to the main scanning, and the processing means is synchronized with the main scanning and the sub-scanning. Then, the imaging signal is input to generate an input image, and the input image is subjected to data processing to which the mura defect inspection method according to claim 1 is applied to determine whether the inspection target article is good or bad. It is a thing.

According to the method for inspecting a mura defect according to claim 1 of the present invention, a noise removal filter is applied to an input image in a noise removal process to obtain a noise removal image, and a noise removal image is obtained in a primary differentiation process. A four-dimensional first-order differential image is obtained by applying four-direction first-order differential filters in the vertical direction, the horizontal direction, and the two diagonal directions, and the pixel values of the four-way first-order differential images are obtained in the absolute value conversion process. The absolute value image in the four directions is obtained by replacing the absolute value, and the pixel value of the pixel at the same position in the absolute value image in the four directions is compared in the maximization process, and the largest pixel value is set as the pixel value of the pixel. A maximum value image is obtained, a predetermined threshold value is applied to the maximum value image in the binarization process, and a binary image is obtained. In the labeling process, the binary image is labeled and the labeled image In the area extraction process, the label A area with the label ring number A is extracted from the labeling image, and the unevenness value which is the difference between the pixel values of the uneven area and the non-mura area in the label A area is calculated in the unevenness calculation process. In the quality determination process, the quality of the label A area is determined based on the unevenness value. That is, the primary differential filter is applied without applying the secondary differential filter affected by the shape and size of the mura defect. The conventional secondary differentiation process detects the uneven area itself, whereas the primary differential process extracts the boundary (pixel value (brightness) change area) between the uneven area and the non-uniform area. Irregularity can be extracted regardless of (see FIG. 6). Therefore, there is provided a method for inspecting a mura defect that can obtain high inspection performance without being affected by the shape and size of the mura defect.
According to the method for inspecting a mura defect according to claim 2 of the present invention, the mura value calculation process includes a boundary extraction process, a coordinate extraction process, an image extraction process, a change direction extraction process, a crossing area extraction process, and a pixel value difference calculation process. And an average value calculation process, a pixel B constituting the boundary of the label A region is extracted in the boundary extraction process to obtain a boundary image, and the coordinates (X [B] of the pixel B constituting the boundary in the coordinate extraction process are obtained. , Y [B]) are extracted, and the absolute value image having the largest pixel value of the coordinate pixel is extracted from the four-direction absolute value image by paying attention to the coordinates in the image extraction process, and the absolute value extracted in the change direction extraction process The direction of the first-order differential filter applied to the image is the change direction of the pixel value, and in the intersection region extraction process, the coordinates of the pixel from the coordinates to the boundary of the label A region in the change direction are extracted and the intersection region is extracted. All the pixels that constitute the boundary in the average value calculation process, in which the pixel value difference, which is the difference between the maximum pixel value and the minimum pixel value of the pixel of the intersection area coordinate in the noise-removed image, is calculated in the pixel value difference calculation process An average value of pixel value differences obtained for B is calculated, and the average value is set as a nonuniformity value. That is, a pixel value change direction is searched from the extracted boundary image, and a mura value that is a difference between the mura region and the non-mura region is obtained as a difference between the maximum pixel value and the minimum pixel value in the direction. Therefore, the unevenness value can be calculated with high accuracy.
According to the mura defect inspection apparatus of the third aspect of the present invention, the main scanning of the linear imaging region is performed by the line sensor camera, the inspection target article is imaged and the imaging signal is output, and the main scanning is performed by the conveying means. An inspection target article is conveyed in the direction of sub-scanning with respect to the image, and an imaging signal is input in synchronization with main scanning and sub-scanning by the processing means to generate an input image. Data processing to which the inspection method is applied is performed, and the quality of the inspection target article is determined. That is, the primary differential filter is applied without applying the secondary differential filter affected by the shape and size of the mura defect. The conventional secondary differentiation process detects the uneven area itself, whereas the primary differential process extracts the boundary (pixel value (brightness) change area) between the uneven area and the non-uniform area. Irregularity can be extracted regardless of (see FIG. 6). Therefore, a mura defect inspection apparatus capable of obtaining high inspection performance without being affected by the shape and size of the mura defect is provided.

Next, embodiments of the present invention will be described with reference to the drawings. An example of the configuration of the mura defect inspection apparatus of the present invention is shown in FIG. In FIG. 1, 1 is a line sensor camera, 2 is a light source, 3 is an image processing unit, 4 is an input unit, 5 is an output unit, 6 is a conveyor, and 100 is an object.
The object 100 may be any article and is not particularly limited. For example, it is an article having a surface to be inspected, such as a web, a sheet, and a panel. The traveling direction of the object 100 is a direction indicated by an arrow in FIG.

  The line sensor camera 1 includes a line sensor element, an output amplifier, a drive circuit for outputting signals in time series, an imaging lens, and the like, and has a linear imaging region. The line sensor element is a large scale integrated circuit (LSI) such as a charge coupled device (CCD) or a metal oxide semiconductor (MOS) in which a plurality of light receiving portions are arranged in a straight line. As shown in FIG. 1, the imaging area of the line sensor camera 1 extends in the width direction of the object 100 (a method perpendicular to the traveling direction of the object 100). Imaging of the surface (two-dimensional area) of the object 100 can be performed by main scanning by the line sensor camera 1 and sub-scanning by the object 100 traveling. The transporter 6 is a transporter that transports the object 100 in the sub-scanning direction.

  In the example shown in FIG. 1, the optical axis of the line sensor camera 1 (center line in the imaging direction) deviates from the vertical direction with respect to the surface of the object 100 and has a predetermined angle. That is, the line sensor camera 1 images the surface of the object 100 from an oblique direction. The angle formed by the optical axis of the line sensor camera 1 and the surface of the object 100 is an appropriate angle depending on the characteristics of the defect to be imaged. The angle is set such that light modulation peculiar to the defect can be imaged according to the characteristic of the defect.

  The light source 2 is an illumination unit that illuminates the imaging region of the line sensor camera 1. The light source 2 is preferably a light source capable of obtaining a linear illumination area so as to match the linear imaging area of the line sensor camera 1. In general, the illumination area by the light source 2 is often set so as to include the imaging area of the line sensor camera 1. In order to obtain a linear illumination region, a light source that emits linear light is used as the light source 2. As a light source that emits light in a straight line, for example, a straight tube fluorescent lamp, a straight tube halogen lamp, a light source in which LEDs (light emitting diodes) are arranged in a straight line, a light beam of a point light source is guided by an optical fiber, and is linear. An optical fiber light source adapted to irradiate a light source, a light source adapted to direct light from a point light source through a light guide tube and irradiate from a slit, and the like can be used.

  In FIG. 1, a light source 2 that emits light in a straight line is used as a light source of a reflective imaging system, and the light source 2 is disposed together with the line sensor camera 1 on the surface side of an object 100. The direction in which the light source 2 that performs linear light emission extends and the direction in which the linear imaging region extends are parallel to each other. The relationship between the optical axis of the line sensor camera 1 and the arrangement of the light sources 2 is determined from comprehensive judgment in consideration of facilitating imaging of defects as described above in the line sensor camera 1.

  The image processing unit 3 includes an image storage unit that inputs an imaging signal output from the line sensor camera 1 and stores it as an input image, that is, an image memory. The image processing unit 3 performs data processing such as image data processing such as extracting unevenness defects from the image stored in the image memory, data processing related to settings and operations related to the image input device, data processing related to user interface, etc. Process. In connection with the user interface, an input unit 4 such as a keyboard and a mouse and an output unit 5 such as a display and an alarm are connected to the image processing unit 3.

  The configuration of the mura defect inspection apparatus according to the present invention has been described above. Next, the operation of the method and apparatus for inspecting a mura defect according to the present invention, that is, the operation of the image processing unit 3 will be described with reference to the drawings. FIG. 2 shows an example of a data processing process of an inspection in which unevenness defects are extracted from an input image and quality is determined. FIG. 3 shows an example of a first-order differential filter used in inspection data processing.

  First, in step S1 (noise removal process) in FIG. 2, a noise removal filter is applied to the input image input to the image memory to perform noise removal processing to obtain a noise removal image. As a noise removal filter, there are a Gaussian filter, a median filter, and an average value filter, and noise removal is performed by applying a filter suitable for the noise to be removed. For example, a median filter is applied to salt and pepper noise, a Gaussian filter is applied to weak noise, and an average filter is applied to strong noise.

  Next, in step S2 (primary differentiation process), a primary differential filter such as a Sobel filter or a Prewitt filter is applied to the noise-removed image to perform a primary differential process to obtain a primary differential image. The change in the pixel value of the mura region, which is the mura defect region, and the non-mura region, which is not the mura defect region, is generally gentle, so that the positive coefficient pixel and the negative coefficient pixel in the primary differential filter Increase the spacing by 3 pixels or more. Further, the direction of the primary differential filter is set to four directions of the vertical direction, the horizontal direction, and the two diagonal directions, and the primary differential filter of the four directions is applied.

  Therefore, in step S2, each of the four-direction first-order differential filters is applied to the noise-removed image, and the first-order differential processing is performed to obtain four-direction (four) first-order differential images. FIG. 3A shows a first-order differential filter in the horizontal direction, FIG. 3B shows a first-order differential filter in the vertical direction, and FIG. 3C shows an oblique 45-degree direction. FIG. 3D shows a first-order differential filter in the oblique 135 degree direction.

Next, in step S3 (absolute value conversion process), for each of the four-way primary differential images, absolute value processing is performed to replace the pixel values of the pixels constituting the image with the absolute values of the pixel values. Obtain an absolute value image.
Next, in step S4 (maximization process), maximum value processing is performed based on the absolute value images in the four directions to obtain a maximum value image. The maximum value processing is processing in which pixel values of pixels at corresponding positions in a plurality of images are compared and the maximum pixel value is set as a pixel value of a pixel at a corresponding position in the maximum value image.

Next, in step S5 (binarization process), a binarization process is performed on the maximum value image to obtain a binarized image. The binarization threshold value is set in advance so that the boundary between the non-uniformity region and the non-uniformity region to be detected becomes the pixel value 1 in the binary image.
Next, in step S6 (labeling process), the binarized image is subjected to a labeling process to obtain a labeled image.
Next, in step S7 (region extraction process), region extraction processing for extracting a label A region having a labeling number A from the labeling image is performed.

Next, in step S8 (unevenness calculation process), unevenness value calculation processing is performed to calculate an unevenness value that is a difference in pixel values between the unevenness area and the non-unevenness area in the label A area.
Next, in step S9 (quality determination process), quality determination processing for determining quality of the label A area based on the unevenness value is performed.
Then, when all the labeling numbers in the labeling image have not been judged as good or bad, the labeling number A is changed to a labeling number that has not been judged good or bad, and the above-described steps after step S7 are repeated. When the pass / fail judgment is completed for all the labeling numbers in the labeling image, the data related to the above-described processing (pass / fail judgment, intermediate image, log, processing condition), particularly the pass / fail judgment data is stored in the memory.

The image processing in the mura defect inspection method and apparatus of the present invention has been described above with reference to the drawings. Next, step S8 (uneven value calculation process) will be described in detail with reference to the drawings. FIG. 4 shows details of an example of a mura value calculation process in the mura defect inspection method and apparatus of the present invention.
First, in step S7 (region extraction process) of FIGS. 2 and 4, the label A region is extracted.

  Next, in step S101 (boundary extraction process) in FIG. 4, pixels constituting the boundary of the label A region are extracted to obtain a boundary image. Note that the boundary is not a pixel because it determines whether or not a specific pixel is included in a specific region (set). However, here, a pixel adjacent to the boundary is referred to as a pixel constituting the boundary. The pixels constituting the boundary include a pixel existing inside a region determined by the boundary and a pixel existing outside. In the present invention, even if the pixels constituting the boundary are defined either inside or outside, usually, the determination is not greatly affected.

Next, in step S102 (coordinate extraction process), pixel coordinates (X [i], Y [i]) in the boundary image are extracted. However, i = 1 to n (n: total number).
Next, in step S103 (initialization process), i = 1 is set.
Next, in step S104 (focus process), the pixel Pi at the coordinates (X [i], Y [i]) is set as a processing target (focussed).
Next, in step S105 (image extraction process), the absolute value having the largest pixel value (luminance) of the pixel Pi at coordinates (X [i], Y [i]) from the absolute value image in four directions (see step S3). Extract images.

Next, in step S106 (change direction extraction process), change direction extraction processing is performed in which the direction of the primary differential filter applied to the extracted absolute value image is the change direction of the pixel value (luminance).
Next, in step S107 (straight line drawing process), a straight line that passes through the pixel Pi at the coordinates (X [i], Y [i]) and extends in the change direction of the pixel value is applied (drawn) to the label A region.
Next, in step S108 (intersection area extraction process), an area where the fitted straight line and the pixel in the label A area intersect is extracted. That is, the intersection area extraction process is performed by extracting the coordinates of the pixels from the pixel Pi at the coordinates (X [i], Y [i]) until the intersection with the boundary of the label A area in the change direction to obtain the intersection area coordinates.

Next, in step S109 (pixel value difference calculation process), a pixel value difference calculation that calculates a pixel value difference that is the difference between the maximum pixel value and the minimum pixel value of the pixels of the intersection region coordinates in the noise-removed image (see step S1). Process.
Next, in step S110 (total number?), It is determined whether or not the pixel value difference has been calculated for all the pixels constituting the boundary. That is, it is determined whether i = N. When i = N, the process proceeds to step S111. When i is not N, the process proceeds to step S112.

In step S111 (average value calculation process), an average value of pixel value differences obtained for all the pixels Pi (i = 1 to n) constituting the boundary is calculated, and an average value calculation using the average value as an uneven value. Process.
In step S112 (continuation process), i = i + 1 is set, the process returns to step S104, and the subsequent steps are repeated.

  As described above, in the present invention, the primary differential filter is applied without applying the secondary differential filter affected by the shape and size of the mura defect. In the secondary differentiation process, the uneven area itself is detected, whereas in the primary differential process, the boundary between the uneven area and the non-uniform area is extracted, so that unevenness can be extracted regardless of the shape and size. In addition, the pixel value change direction is searched from the extracted boundary image, and the mura value that is the difference between the mura region and the non-mura region is calculated as the difference between the maximum pixel value and the minimum pixel value in that direction. Is highly accurate. Accordingly, there is provided a method and apparatus for inspecting a mura defect that can obtain high inspection performance without being affected by the shape and size of the mura defect.

It is a figure which shows an example of a structure in the inspection apparatus of the nonuniformity defect of this invention. It is a flowchart which shows an example of the data processing process of the test | inspection which extracts a nonuniformity defect from an input image and determines quality. It is a figure which shows an example of the primary differential filter used in the data processing of a test | inspection. It is a figure which shows the detail about an example of the process of calculating the brightness | luminance difference in the data processing of a test | inspection. It is explanatory drawing which shows applying the secondary differential filter which adapts the shape and magnitude | size of a nonuniformity defect. It is explanatory drawing which shows the difference of a primary differentiation process and a secondary differentiation process.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Line sensor camera 2 Light source 3 Image processing part 4 Input part 5 Output part 6 Conveyor 100 Target object

Claims (3)

Applying a noise removal filter to the input image to obtain a noise removal image,
Applying a first-order differential filter in four directions of vertical, horizontal and diagonal directions to the noise-removed image to obtain a first-order differential image in the four directions;
An absolute value conversion process for obtaining an absolute value image in the four directions by replacing a pixel value of a pixel in the four-direction primary differential image with an absolute value thereof;
A maximization process in which pixel values of pixels at the same position in the absolute value images in the four directions are compared to obtain a maximum value image having the largest pixel value as the pixel value of the pixel;
A binarization process for obtaining a binary image by applying a predetermined threshold to the maximum value image;
A labeling process of labeling the binary image to obtain a labeled image;
A region extraction process for extracting a label A region having a label ring number A from the labeling image;
A mura value calculation process for calculating a mura value, which is a difference between pixel values of the mura area and the non-mura area in the label A area,
A pass / fail determination process for determining pass / fail of the label A area based on the unevenness value;
A method for inspecting a mura defect, characterized by comprising: The method for inspecting a mura defect according to claim 1, wherein the mura value calculation process is:
A boundary extraction process for extracting a pixel B constituting a boundary of the label A region to obtain a boundary image;
A coordinate extraction process for extracting the coordinates (X [B], Y [B]) of the pixel B constituting the boundary;
An image extraction process of focusing on the coordinates and extracting an absolute value image having the largest pixel value of the pixels of the coordinates from the absolute value images in the four directions;
A change direction extraction process in which the direction of the primary differential filter applied to the extracted absolute value image is the change direction of the pixel value;
An intersection region extraction process in which the coordinates of the pixels from the coordinates to the intersection of the label A region in the change direction are extracted and used as intersection region coordinates;
A pixel value difference calculation process for calculating a pixel value difference that is a difference between the maximum pixel value and the minimum pixel value of the pixels of the intersection region coordinates in the noise-removed image;
An average value calculation process of calculating an average value of the pixel value differences obtained for all the pixels B constituting the boundary and setting the average value as the unevenness value;
A method for inspecting a mura defect, characterized by comprising: A non-uniformity defect inspection apparatus comprising a line sensor camera, a conveying means, and a processing means,
The line sensor camera performs main scanning of a linear imaging region, images an inspection target article, and outputs an imaging signal;
The conveying means conveys the inspection target article in a sub-scanning direction with respect to the main scanning;
2. The data processing in which the processing means inputs the imaging signal in synchronization with the main scanning and the sub scanning to generate an input image, and applies the mura defect inspection method according to claim 1 to the input image. An inspection device for mura defects, wherein the quality of the inspection target article is determined.