JP2011053044A - Apparatus and method for inspection of mesh, program, and recording medium - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a mesh inspection apparatus capable of correctly extracting defects without being affected by a change in the thickness of a mesh width due to the unevenness of etching and printing. <P>SOLUTION: A processing section 3 of the mesh inspection apparatus 1 inputs an image from a line sensor 5 at a resolution at which an image of a mesh in a mesh sheet 10 is captured, preprocesses and corrects shading due to a white LED illumination 7 as a light source, smoothes the preprocessed image, blurs the image so as to prevent the defect from being erroneously detected, extracts the defect in the smoothed image by a threshold, and trims, for example, a 128×128 pixels from the image before smoothing which is centered at a barycenter of the extracted defect. After an area corresponding to a spatial frequency of the mesh in a FFT image of the trimmed image is replaced with 0 and a mesh frequency is removed, an IFFT process is applied. Brightness, a shape and an area of the defect are determined from the obtained image, and then the results are output. <P>COPYRIGHT: (C)2011,JPO&INPIT

Description

The present invention relates to a mesh inspection apparatus, and more particularly to a mesh inspection apparatus that inspects for adhesion of foreign matter to a mesh sheet such as a PDP (Plasma Display Panel) electromagnetic wave-preventing film and mesh slippage.

  An image display device used for image display such as PDP, when a voltage is applied between electrodes, excites gas molecules enclosed inside by discharge accompanying the application, and the fluorescence enclosed inside by generated ultraviolet rays. Excites matter. At this time, a light beam in the visible light region is generated to display an image, but electromagnetic waves are generated by this discharge, and the electromagnetic waves are slightly leaked to the outside. In order to shield this leaking electromagnetic wave, a mesh sheet is provided as an electromagnetic wave preventing film on the front plate of the PDP.

  In order to inspect such foreign matter adhesion and mesh omission of such a mesh sheet, an inspection apparatus that obtains an image by a line sensor and linear transmission illumination is effective. At that time, in order to perform an inspection process for extracting a defective portion from an input image using a threshold value, it is necessary to erase a mesh that is captured simultaneously with the defect. As a method for deleting the mesh, (1) the inspection is performed with a resolution at which the mesh does not appear in the image, (2) the smoothing process is performed on the image with the resolution at which the mesh appears in the image, and the mesh is blurred. It is conceivable to perform a longitudinal comparison test, (4) perform FFT (Fast Fourier Transform), extract and erase the spatial frequency peak, and perform IFFT (Inverse Fast Fourier Transform). However, in the method shown in (1), the inspection performance is lowered because smaller defects are not captured when the resolution is lowered. In the method shown in (2), defects are also blurred by the smoothing process that blurs the mesh, and it becomes difficult to determine the shape of the defects. In the method shown in (3), the input image is distorted due to the expansion and contraction of the filter and the flapping of the conveyance, so that it is difficult to perform pattern inspection by front-rear comparison as is done with a liquid crystal color film or PDP. Further, when the method shown in (4) is performed on the entire image, the number of pixels of the line sensor is 1000 or more, so the image size increases, and the processing time for FFT or IFFT increases.

  In Patent Document 1, each shape portion corresponding to each island portion of one-dimensional image data of a periodic structure object such as a mesh shape or a lattice shape is divided into a plurality of types of attributes using a one-dimensional line sensor. A method is disclosed in which a necessary defect extraction is performed by detecting a feature amount of each island portion divided into the above-mentioned attributes and comparing the detected value with a reference value of the corresponding feature amount.

Japanese Patent No. 3278512

  However, in the method of Patent Document 1 shown above, in the manufacturing process of a mesh sheet such as a PDP electromagnetic wave prevention film, it is difficult to accurately extract defects due to the influence of changes in the mesh width due to uneven etching and printing. Become. In the method of Patent Document 1, an image is binarized with a predetermined threshold without distinguishing between a defect and a mesh, and processing is performed using the binarized image (see paragraph 0014). Therefore, since the defect brightness information is lost and the defect cannot be distinguished from the mesh, the defect brightness, shape, and area cannot be determined even if the presence of the defect can be detected.

  The present invention has been made in view of these problems, and the object of the present invention is to accurately extract defects without being affected by changes in the mesh width due to uneven etching or printing. Providing a simple mesh inspection device. Another object of the present invention is to provide a mesh inspection apparatus and the like that can determine the brightness, shape, and area of defects in a mesh sheet.

  In order to achieve the above-mentioned object, the first invention is a mesh inspection apparatus for inspecting a mesh sheet from an image obtained by a line sensor and illumination, and an image input means for obtaining an input image of the mesh sheet; Smoothing means for smoothing the input image to obtain a smoothed image; defect extraction means for extracting a defect from the smoothed image by a first threshold value to obtain a barycentric coordinate of the defect; and Trimming means for obtaining a trimmed image in a predetermined range centered on the center of gravity coordinates of the defect, FFT means for performing FFT processing on the trimmed image to obtain an FFT image, and mesh frequency extracting means for extracting a mesh frequency from the FFT image And a mesh frequency removing means for removing the extracted mesh frequency from the FFT image, and removing the mesh frequency It performs IFFT processing on the FT image, a mesh inspection apparatus characterized by comprising an IFFT unit for obtaining a defect image.

  According to the first invention, accurate defect extraction can be performed without being affected by the change in the mesh width due to uneven etching or printing.

  The first invention preferably further comprises defect cause identifying means for identifying the cause of the defect using at least one of the brightness, shape, and area of the defect image. Thereby, the cause of the defect of a mesh sheet | seat can be pinpointed correctly.

  In the first invention, it is preferable that the defect extraction unit changes the first threshold value in accordance with a change in luminance average value around a pixel to be determined in the smoothed image. As a result, the mesh inspection can be performed without being affected by the change in the thickness of the mesh width due to uneven etching or printing during the manufacture of the mesh sheet.

  The mesh frequency extraction means in the first invention sets an area excluding an area corresponding to a direct current component of the FFT image as a mesh frequency search area, and has a luminance higher than a second threshold in the mesh frequency search area. Is extracted as a mesh frequency. This eliminates the need to change the setting due to the change in mesh pitch and direction. That is, it is not necessary to adjust the threshold value for each product.

  The mesh frequency removing means in the first invention replaces the luminance of the extracted mesh frequency with 0. Thereby, the mesh image is removed from the finally obtained defect image.

  A second invention is a mesh inspection method performed by a mesh inspection apparatus that inspects a mesh sheet from an image obtained by a line sensor and illumination, an image input step for obtaining an input image of the mesh sheet, and the input image A smoothing step of smoothing to obtain a smoothed image, a defect extracting step of extracting a defect from the smoothed image by a first threshold value to obtain a center of gravity coordinate of the defect, and a center of gravity coordinate of the defect from the input image A trimming step for obtaining a trimmed image in a predetermined range centering on the FFT, an FFT step for performing FFT processing on the trimmed image to obtain an FFT image, a mesh frequency extracting step for extracting a mesh frequency from the FFT image, and the extraction A mesh frequency removing step of removing the mesh frequency from the FFT image, and an FFT image from which the mesh frequency is removed It performs IFFT processing on a mesh inspection method characterized by comprising the IFFT to obtain a defect image.

  According to the second invention, accurate defect extraction can be performed without being influenced by the change in the mesh width due to uneven etching or printing.

  The second invention preferably further comprises a defect cause identifying step of identifying the cause of the defect using at least one of the brightness, shape, and area of the defect image. Thereby, the cause of the defect of a mesh sheet | seat can be pinpointed correctly.

  In the defect extracting step according to the second aspect of the invention, it is preferable that the first threshold value is changed in accordance with a change in luminance average value around a pixel to be determined in the smoothed image. As a result, the mesh inspection can be performed without being affected by the change in the thickness of the mesh width due to uneven etching or printing during the manufacture of the mesh sheet.

  In the mesh frequency extraction step according to the second aspect of the present invention, an area excluding an area corresponding to a direct current component of the FFT image is set as a mesh frequency search area, and a luminance higher than a second threshold value in the mesh frequency search area Is extracted as a mesh frequency. This eliminates the need to change the setting due to the change in mesh pitch and direction. That is, it is not necessary to adjust the threshold value for each product.

  In the mesh frequency removing step according to the second aspect of the invention, the luminance of the extracted mesh frequency is replaced with zero. Thereby, the mesh image is removed from the finally obtained defect image.

  A third invention is a program for causing a computer to function as the mesh inspection apparatus of the first invention.

  A fourth invention is a computer-readable recording medium on which a program for causing a computer to function as the mesh inspection apparatus of the first invention is recorded.

  According to the present invention, it is possible to provide a mesh inspection apparatus and the like capable of performing accurate defect extraction without being affected by changes in the mesh width due to uneven etching or printing. Further, it is possible to provide a mesh inspection apparatus or the like that can determine the brightness, shape, and area of defects in the mesh sheet.

The figure which shows the outline | summary of the mesh inspection apparatus 1 which concerns on this invention Side view showing details of imaging unit of mesh inspection apparatus 1 The figure which shows an example of the mesh sheet | seat 10 The flowchart which shows the flow of the process of the mesh inspection which the mesh inspection apparatus 1 performs The flowchart which shows the flow of the detailed process of the mesh frequency removal which the mesh inspection apparatus 1 performs Diagram showing image preprocessing The figure which shows the image which smoothed the image which pre-processed Diagram showing defect trimming from image The figure which shows the image which FFT-processed the trimmed image The figure which shows extraction of the mesh frequency from the image which performed the FFT process The figure which shows the image which carried out the IFFT process of the image which removed the mesh frequency

Hereinafter, embodiments of a mesh inspection apparatus according to the present invention will be described in detail with reference to the drawings.
First, the mesh inspection apparatus 1 according to the present invention will be described with reference to FIGS.
FIG. 1 is a diagram illustrating an outline of the mesh inspection device 1, FIG. 2 is a side view illustrating details of an imaging unit of the mesh inspection device 1, and FIG. 3 is a diagram illustrating an example of a mesh sheet 10.

  As shown in FIG. 1, the mesh inspection apparatus 1 is an apparatus for inspecting the adhesion of foreign matter and mesh omission on the mesh sheet 10. Have.

The processing unit 3 includes a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM (Random Access Memory), a storage device, and the like.
The CPU calls and executes a program stored in a storage device, a ROM, a recording medium or the like to a work memory area on the RAM, and processes the image data captured by the line sensor 5.
The ROM is a non-volatile memory, and permanently stores a boot program for the mesh inspection apparatus 1, a program such as BIOS, data, and the like.
The RAM is a volatile memory, and temporarily holds a program, data, and the like loaded from a storage device, a ROM, a recording medium, and the like, and includes a work area used by the processing unit 3 to perform various processes.

The storage device is an HDD (hard disk drive), and stores a program executed by the processing unit 3, data necessary for program execution, an OS (operating system), and the like. As for the program, a control program corresponding to an OS (operating system) and an application program corresponding to processing described later are stored.
Each of these program codes is read by the processing unit 3 as necessary, transferred to the RAM, read by the CPU, and executed as various means.
The processing unit 3 may include a display device such as a display device, a keyboard for inputting data, an input device such as a mouse and a numeric keypad, and a peripheral device I / F (interface) device.

The line sensor 5 includes a CCD (Charge Coupled Device) image sensor element, a lens, a driver, a control circuit, and the like.
As shown in FIG. 2, the line sensor 5 forms an image of an object such as a mesh sheet 10 on the element surface with a lens, and irradiates the white LED line illumination 7 and transmits the amount of light passing through the mesh sheet 10. It is converted into an electrical signal and output as an image. The electric signal is taken out as a time-series pulse, and an integrated value averaged at every constant pitch is output to an object such as the mesh sheet 10 that moves in the direction of the arrow by the roller 11. The image obtained by the line sensor 5 is input with a resolution at which the mesh of the mesh sheet 10 is reflected in the image. For example, it is assumed that one line is 4000 pixels, one pixel is about 0.05 mm, and the defect of the mesh sheet 10 detected by the mesh inspection apparatus 1 is about 0.1 mm.

  As shown in FIG. 3, the mesh sheet 10 is a lattice-like periodic structure, and is an electromagnetic wave in which a mesh made of a metal foil such as a copper foil or a silver foil is laminated on one surface of a transparent film substrate with an adhesive. This is a shielding member. The mesh width of the mesh sheet 10 is, for example, about 10 μm and the pitch is 250 to 300 μm.

Next, details of the mesh inspection apparatus 1 according to the present invention will be described with reference to FIGS. 4 to 11.
4 is a flowchart showing a flow of mesh inspection processing performed by the mesh inspection apparatus 1, FIG. 5 is a flowchart showing a detailed processing flow of mesh frequency removal performed by the mesh inspection apparatus 1, and FIG. FIG. 7 is a diagram illustrating an image obtained by smoothing a preprocessed image, FIG. 8 is a diagram illustrating trimming of a defect from the image, and FIG. 9 is an FFT process performed on the trimmed image. FIG. 10 is a diagram illustrating extraction of a mesh frequency from an image subjected to FFT processing, and FIG. 11 is a diagram illustrating an image obtained by performing IFFT processing on an image from which the mesh frequency has been removed.

The processing unit 3 of the mesh inspection apparatus 1 inputs an image from the line sensor 5 with a resolution that allows the mesh of the mesh sheet 10 to appear in the image, and performs shading correction on the white LED line illumination 7 that is a light source as preprocessing (step S101). ). Here, the shading correction is a process for removing unevenness from an image having density unevenness (caused by the white LED line illumination 7).
As shown in FIG. 6, the luminance distribution in the width direction of the input image from the line sensor 5 is not uniform, and in particular, the luminance at the end portion is low. Therefore, shading correction is performed on the input image so that the luminance distribution in the width direction is uniform.

The processing unit 3 of the mesh inspection apparatus 1 smoothes the preprocessed image and blurs the image to the extent that no mesh is erroneously detected (step S102).
For the smoothing process, for example, a 9 × 9 Gaussian filter is used. A moving average filter, a weighted average filter, or the like that is simply averaged may be used.
As shown in FIG. 1 of FIG. 7, in the image before smoothing, meshes and defects are mixed, and it is difficult for the computer to extract the defects in this state. Therefore, by smoothing the image, the mesh is blurred as shown in FIG. At this time, the edge of the defect is slightly blurred, but not enough to overlook the defect.

The processing unit 3 of the mesh inspection apparatus 1 performs threshold processing on the smoothed image and extracts defects using a predetermined threshold (step S103).
As shown in FIG. 3 of FIG. 7, an area where pixels determined to be defective by the process of step S <b> 103 are connected can be extracted as a defective area.
When the dark defect determination threshold value of a dark defect such as adhesion of foreign matter is α and the bright defect threshold value of a bright defect such as missing mesh is β, for example, the surrounding 255 × The luminance average value of 255 pixels is calculated, and when the luminance of the pixel to be determined is smaller than the luminance average value -α, it is determined as a dark defect, and when the luminance of the pixel to be determined is higher than the luminance average value + β, it is determined as a bright defect. To do.
The brightness of the mesh image is affected by changes in the thickness of the mesh width due to uneven etching or printing (changes in the thickness of the mesh width are not defects) and are not necessarily constant. This is because when the thickness of the mesh width changes, the amount of light transmitted through the mesh sheet as a whole also changes. Therefore, the threshold value for determining the defect is not always fixed for all pixels, but the threshold value for the dark defect and the bright defect is set in accordance with the brightness of the peripheral pixels of the pixel to be determined. By making the determination while changing, it is possible to determine the defect more accurately without being affected by the change in the thickness of the mesh width due to unevenness of etching or printing.
As the dark defect, for example, a foreign substance such as rubber can be detected. Further, as a bright defect, for example, a mesh omission can be detected.

As illustrated in FIG. 8, the processing unit 3 of the mesh inspection apparatus 1 calculates the barycentric coordinates of the defect extracted in the image after smoothing, and, for example, 128 around the calculated barycentric coordinates in the image before smoothing. Trimming × 128 pixels is performed (step S104). As shown in the lower diagram of FIG. 8, the trimmed defect image includes both the mesh and the defect.
As shown in the upper left diagram of FIG. 8, there may be a plurality of defects for one image. In consideration of such a case, the processing unit 3 calculates a barycentric coordinate for each defect area, with a range where pixels determined to be defective in step S103 are connected as one defect area. Then, as shown in the upper right diagram in FIG. 8, the processing unit 3 trims each predetermined range around the center of gravity coordinates for each defect area. It is desirable that the predetermined range always includes all the defective areas.
Further, as the number of pixels to be trimmed, it is desirable to use a numerical value that is a power of 2 on the principle of performing FFT processing thereafter.
In step S103, the defect can be accurately detected by extracting the defect using the smoothed image. Then, in step S104, a part of the image before smoothing is trimmed and used for later-described processing, and accurate brightness information that has not been smoothed is used to determine the brightness, shape, and area of the defect. It can be carried out.

The processing unit 3 of the mesh inspection apparatus 1 removes the mesh frequency of the mesh sheet 10 from the trimmed image (step S105). The mesh frequency is a spatial frequency corresponding to a mesh and appears in a specific region in the FFT image.
FIG. 5 shows the details of the mesh frequency removal process.
The processing unit 3 of the mesh inspection apparatus 1 performs an FFT process on the trimmed image to obtain an FFT image (spectral image) as shown in FIG. 9 (step S201).
In the FFT image shown in FIG. 9, the center portion of the image is a region corresponding to the spatial frequency of the defect to be analyzed (region corresponding to the DC component of the image). Other bright areas excluding the central part of the image are areas corresponding to the mesh frequency. In FIG. 9, when two virtual straight lines orthogonal to each other at the center of the image are considered, areas corresponding to mesh frequencies exist at predetermined intervals on the two virtual straight lines. The position of the region corresponding to the mesh frequency varies depending on the shape, interval, and mesh width of the mesh.

The processing unit 3 of the mesh inspection apparatus 1 calculates the power spectrum of the FFT image (step S202), sets a search region for searching for the mesh frequency (step S203), and extracts the mesh frequency from the search region. (Step S204).
As shown in FIG. 10, the outside of a circle with a constant radius r1 centered on the image center of the FFT image is set as a search region for searching for the mesh frequency. In the search area, a pixel having a luminance value larger (brighter) than a predetermined threshold is searched. A region inside a circle having a constant radius r2 with the searched pixel as a center point is extracted as a region corresponding to the mesh frequency.

The processing unit 3 of the mesh inspection apparatus 1 removes the mesh frequency by setting the luminance value of the pixel included in the region corresponding to the mesh frequency extracted in the FFT image to 0 as shown in FIG. 11 (step S205). Then, IFFT processing is performed on the FFT image from which the mesh frequency has been removed, and an image from which the mesh frequency has been removed from the trimmed image is obtained (step S206).
In addition, instead of setting the luminance value of a pixel included in a region inside a circle having a constant radius r2 with the searched pixel as a center point to 0, a pixel having a luminance value greater than a predetermined threshold value in the search region However, a process for setting the luminance value to 0 may be performed.
In any method, it is possible to accurately determine a region corresponding to a mesh frequency without being affected by changes in the shape, interval, and mesh width of the mesh due to uneven etching and printing.

After removing the mesh frequency from the trimmed image, the processing unit 3 of the mesh inspection apparatus 1 determines the brightness, shape, and area of the defect (step S106), and outputs the result (step S107).
In the defect determination, if the brightness is low, that is, if the defect is black, it is determined that the foreign substance is attached, and further, the cause of the defect is specified by determining whether the defect area, the defect shape is a point or a line, etc. Is possible. When the brightness of the defect is high, that is, when the defect is white, it can be determined that the defect is a missing mesh.
For example, the post-IFFT image on the left side of FIG. In addition, it can be seen that the post-IFFT image on the right side of FIG. 11 has a larger ellipse-shaped region surrounding a black region having an elliptical shape. This is considered to be a serious defect in which bubbles are generated in the manufacturing process.

  As described above, in the embodiment of the present invention, since the mesh is erased without blurring the defect, it is possible to provide a mesh inspection apparatus or the like that can determine the luminance, shape, and area of the defect of the mesh sheet. In particular, by detecting a defect without losing the luminance information of the defect, bubbles generated in the manufacturing process can be detected.

  When extracting defects by smoothing the input image, by changing the threshold value for extracting defects according to the average brightness around the pixel to be judged, due to uneven etching or printing during mesh sheet manufacturing The mesh inspection can be performed without being affected by the change in the thickness of the mesh width.

  Also, if the mesh pitch or direction of the mesh sheet changes, the area corresponding to the spatial frequency of the mesh changes in the image obtained by performing FFT processing on the image trimmed from the input image, but the power spectrum is calculated. By automatically extracting the mesh frequency using the threshold value in the mesh frequency search region, it is not necessary to change the setting by changing the mesh pitch or direction.

  The preferred embodiments of the mesh inspection apparatus and the like according to the present invention have been described above with reference to the accompanying drawings, but the present invention is not limited to such examples. It will be apparent to those skilled in the art that various changes or modifications can be conceived within the scope of the technical idea disclosed in the present application, and these naturally belong to the technical scope of the present invention. Understood.

DESCRIPTION OF SYMBOLS 1 ......... Mesh inspection apparatus 3 ......... Processing part 5 ......... Line sensor 7 ......... White LED line illumination 9 ......... Signal lamp 10 ......... Mesh sheet 11 ......... Roller

Claims (12)

A mesh inspection apparatus that inspects a mesh sheet from an image obtained by a line sensor and illumination,
An image input means for obtaining an input image of the mesh sheet;
Smoothing means for smoothing the input image to obtain a smoothed image;
A defect extracting means for extracting a defect from the smoothed image by a first threshold value and obtaining a barycentric coordinate of the defect;
Trimming means for obtaining a trimmed image of a predetermined range centered on the center of gravity coordinates of the defect from the input image;
FFT means for performing FFT processing on the trimmed image to obtain an FFT image;
Mesh frequency extraction means for extracting a mesh frequency from the FFT image;
Mesh frequency removing means for removing the extracted mesh frequency from the FFT image;
IFFT means for obtaining a defect image by performing IFFT processing on the FFT image from which the mesh frequency has been removed;
A mesh inspection apparatus comprising: A defect cause identifying means for identifying the cause of the defect using at least one of the brightness, shape, and area of the defect image;
The mesh inspection apparatus according to claim 1, further comprising:   2. The mesh according to claim 1, wherein the defect extraction unit changes the first threshold value in accordance with a change in luminance average value around a pixel to be determined in the smoothed image. Inspection device.   The mesh frequency extraction means sets an area excluding an area corresponding to a direct current component of the FFT image as a mesh frequency search area, and meshes an area having a luminance higher than a second threshold in the mesh frequency search area. The mesh inspection apparatus according to claim 1, wherein the mesh inspection apparatus is extracted as a frequency.   The mesh inspection apparatus according to claim 1, wherein the mesh frequency removing means replaces the luminance of the extracted mesh frequency with zero. From an image obtained by line sensor and illumination, a mesh inspection method performed by a mesh inspection apparatus that inspects a mesh sheet,
An image input process for obtaining an input image of the mesh sheet;
A smoothing step of smoothing the input image to obtain a smoothed image;
A defect extraction step of extracting a defect from the smoothed image with a first threshold value to obtain a center of gravity coordinate of the defect;
A trimming step of obtaining a trimmed image of a predetermined range centered on the center of gravity coordinates of the defect from the input image;
An FFT process for performing an FFT process on the trimmed image to obtain an FFT image;
A mesh frequency extraction step of extracting a mesh frequency from the FFT image;
A mesh frequency removing step of removing the extracted mesh frequency from the FFT image;
An IFFT process for obtaining a defect image by performing IFFT processing on the FFT image from which the mesh frequency has been removed;
A mesh inspection method comprising: A defect cause identifying step for identifying the cause of the defect using at least one of the brightness, shape, and area of the defect image;
The mesh inspection method according to claim 6, further comprising:   7. The mesh according to claim 6, wherein the defect extraction step changes the first threshold value in accordance with a change in luminance average value around a pixel to be determined in the smoothed image. Inspection method.   In the mesh frequency extraction step, an area excluding an area corresponding to a direct current component of the FFT image is set as a mesh frequency search area, and an area having a luminance higher than a second threshold is meshed in the mesh frequency search area. The mesh inspection method according to claim 6, wherein the mesh inspection method is extracted as a frequency.   The mesh inspection method according to claim 6, wherein the mesh frequency removing step replaces the luminance of the extracted mesh frequency with zero.   A program for causing a computer to function as the mesh inspection apparatus according to any one of claims 1 to 5.   A computer-readable recording medium having recorded thereon a program that causes the computer to function as the mesh inspection apparatus according to any one of claims 1 to 5.