Analysis method for LCD appearance defects

Abstract

The invention provides an LCD (liquid crystal display) appearance defect analysis method, belongs to the technical field of LCD grinding defect detection, and aims to solve the problem that a visual defect area cannot be accurately detected due to the fact that a screen grinding edge area is variable. The theoretical edge area is compared with the actual grinding area, the defect generation area is accurately obtained, no matter how the grinding edge area changes, the grinding edge area can be automatically recognized, the algorithm can be suitable for different LCD products, and the applicability and accuracy of the detection algorithm are improved.

Description

Technical Field

[0001] The invention relates to the technical field of LCD grinding defect detection, and in particular to a method for analyzing appearance defects in a LCD grinding area. Background Art

[0002] During the production process of LCD displays, due to environmental influences, some process and quality problems often occur, resulting in defects and flaws in the product appearance. At present, in order to distinguish specific flaws and defects, defect analysis is carried out through machine vision. Machine vision detection uses images as the means or carrier of the detected target and signal transmission to extract useful information from the image.

[0003] Screen defects occur due to the production process. For example, after the grinding process, some factors in the grinding process cause defects to occur near the grinding edge, such as shells and breakages. The characteristics of the defects are: a. They occur near the grinding edge; b. The size of the grinding boundary area is variable; especially feature b, which leads to the lack of a clear positioning reference in the visual analysis, resulting in the variability of the analysis area. In the actual detection process, features that are not defects are often judged as defects, which requires more people to be arranged for manual determination; defects may also be judged as normal products, resulting in product risks. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method for detecting appearance defects in a grinding area of ​​an LCD, which solves the problem that the visual defect area cannot be accurately detected due to the variable grinding edge area of ​​the screen.

[0005] A method for analyzing LCD appearance defects comprises the following steps:

[0006] Step S1, obtaining an LCD image to be detected;

[0007] Step S2, sampling the grinding area: starting from a corner of the LCD image, evenly draw n rays, the rays intersect with the outer contour and inner contour of the LCD grinding area to obtain 2N intersection points, the intersection points of the rays and the outer contour form a set to obtain the starting point set {S(x t ,y t )} t＝1 n = {S(x1,y1),S(x2,y2),...,S(x t ,y t ),...,S(x n ,y n )}, the intersection points of the ray and the inner contour form a set of termination points {E(x1,y1),E(x2,y2),...,E(x n,y n )}, where S(x n ,y n ) is the coordinate of the intersection of the ray and the outer contour, E(x n ,y n ) is the coordinate of the intersection of the ray and the inner contour;

[0008] Step S3: filter and reconstruct the sampled data. The specific steps are as follows:

[0009] S31: Calculate the distance DIST between the starting point and the end point in sequence t , if the distance is less than the limit distance DIST max , then it is recorded as a passing sample, otherwise it is counted as a deviation sample;

[0010] S32: Calculate the average distance between the starting point and the ending point of the sample:

[0011]

[0012] S33: traverse the deviation samples in sequence, taking the starting point of the deviation sample as the base point and taking the average distance DIST avg is the length, and the end point that deviates from the sample is taken as the direction to calculate the corrected end point;

[0013] S33: adding the corrected deviation samples to the passing samples to obtain a corrected start point set and an end point set to form edge sample data;

[0014] Step S4, calculating the edge sample data as an edge region: performing smoothing processing on the starting point set and the ending point set respectively to obtain a starting contour line and an ending contour line, and then performing closed-loop processing on the starting contour line and the ending contour line to obtain a theoretical edge region;

[0015] Step S5, bracketing the theoretical edge area in the direction of the starting point to obtain the defect occurrence area;

[0016] Step S6, extracting the low brightness value area on the LCD image, calculating the area located in the low brightness value area and intersecting with the theoretical edge area, and obtaining the actual grinding area;

[0017] Step S7, performing intersection operation on the actual grinding area and the defect occurrence area to obtain the defect area. If the defect area does not exist, it means that there is no grinding defect. If the defect area exists, the area and position information of the defect are calculated based on the defect area.

[0018] Preferably, the smoothing process in step S4 is quadratic B-spline interpolation.

[0019] Preferably, the number of rays n in step S2 is greater than or equal to 16.

[0020] The beneficial effects of the scheme of the present invention are as follows: when detecting appearance defects in the grinding area of ​​LCD, the present invention first calculates and analyzes the theoretical edge area, compares the theoretical edge area with the actual grinding area, and accurately obtains the defect occurrence area. No matter how the grinding edge area changes, the grinding edge area can be automatically identified, so that the algorithm can be applied to different LCD products, thereby improving the applicability and accuracy of the detection algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0022] Figure 1 It is a schematic diagram of the flow of the LCD appearance defect analysis algorithm based on multi-line scan image acquisition in the present invention;

[0023] Figure 2 It is a schematic diagram of sampling normal samples and defective samples;

[0024] Figure 3 It is a schematic diagram of the simulated contours of normal samples and defective samples;

[0025] Figure 4 It is a schematic diagram of the simulation area of ​​normal samples and defective samples;

[0026] Figure 5 It is a schematic diagram of the area bracketed by normal samples and defective samples;

[0027] Figure 6 It is a schematic diagram of the actual contours of normal samples and defective samples;

[0028] Figure 7 It is a schematic diagram of the defect area of ​​normal samples and defective samples. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the solutions in the embodiments of the present invention are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0030] It should be noted that the embodiments of the present invention and the features therein may be combined with each other as long as there is no conflict between the combinations.

[0031] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments, but they are not intended to be limiting of the present invention. Figure 1 As shown, a method for analyzing appearance defects of a grinding area of ​​an LCD includes the following steps:

[0032] Step S1, obtaining an LCD image to be detected;

[0033] Step S2, sampling the grinding area: Figure 2 As shown, taking a corner of the LCD image as the starting point, at least 16 rays are evenly drawn in the range of 8° to 82°. The rays intersect with the outer contour and inner contour of the LCD grinding area to obtain 2N intersection points. The intersection points of the rays and the outer contour form a set to obtain the starting point set {S(x t ,y t )} t＝1 n = {S(x1,y1),S(x2,y2),...,S(x t ,y t ),...,S(x n ,y n )}, the intersection points of the ray and the inner contour form a set of termination points {E(x1,y1),E(x2,y2),...,E(x n ,y n )}, where S(x n ,y n ) is the coordinate of the intersection of the ray and the outer contour, E(x n ,y n ) is the coordinate of the intersection of the ray and the inner contour;

[0034] Step S3: filter and reconstruct the sampled data. The specific steps are as follows:

[0035] S31: Calculate the starting point S(x t ,y t ) and the end point E(x n ,y n ) t ,

[0036] If the distance is less than the limit distance DIST max , then it is recorded as a passing sample, otherwise it is counted as a deviation sample;

[0037] S32: Calculate the average distance between the starting point and the ending point of the sample:

[0038]

[0039] S33: Traverse the deviation samples in sequence, taking the starting point of the deviation sample as the base point S(x t ,y t ), with the average distance DIST avg The length is the end point of the deviation sample, and the corrected end point E'(x n ,y n );

[0040] S33: adding the corrected deviation samples to the passing samples to obtain a corrected start point set and an end point set to form edge sample data;

[0041] Step S4: Calculate the edge sample data as the edge area: Figure 3 , Figure 4 As shown, the starting point set and the ending point set are respectively subjected to continuous quadratic B-spline interpolation smoothing processing to obtain the starting contour line and the ending contour line, and then the starting contour line and the ending contour line are subjected to closed-loop processing to obtain the theoretical edge area;

[0042] The smoothing process of quadratic B-spline interpolation avoids the analysis of the final result due to the variability of the contour, and approximates and creates the theoretical edge area. The essence of interpolation is to use the values ​​of the surrounding points to calculate the value of the interpolation point. As shown in the figure below, the value of the red point is known, and the value of the black point is unknown. Then, through a certain algorithm, the value of the black point is calculated using the values ​​of the red points around the black point, which is interpolation.

[0043] Step S5: Figure 5 As shown, the theoretical edge area is bracketed toward the starting point to obtain the defect occurrence area;

[0044] Step S6: Figure 6 As shown, the low brightness value area on the LCD image is extracted, and the area located in the low brightness value area and intersecting with the theoretical edge area is calculated to obtain the actual grinding area;

[0045] Step S7: Figure 7 As shown, the actual grinding area and the defect occurrence area are intersected to obtain the defect area. If the defect area does not exist, it means that there is no grinding defect. If the defect area exists, the area and position information of the defect are calculated based on the defect area.

[0046] In order to solve the problem that the visual defect area cannot be accurately detected due to the variable grinding edge area of ​​the screen, the present invention first calculates and analyzes the theoretical edge area when detecting the appearance defects of the LCD grinding area, and compares the theoretical edge area with the actual grinding area to accurately obtain the defect occurrence area. No matter how the grinding edge area changes, the grinding edge area can be automatically identified, so that the algorithm can be applied to different LCD products, thereby improving the applicability and accuracy of the detection algorithm.

[0047] The main features and advantages of the present invention are shown and described above. It is obvious to those skilled in the art that the present invention is limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential features of the present invention. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes that fall within the meaning and scope of the equivalent elements of the claims are included in the present invention.

Claims

1. A method for analyzing LCD appearance defects, characterized in that: The steps include: Step S1, obtaining an LCD image to be detected; Step S2, sampling the grinding area: starting from a corner of the LCD image, evenly draw n rays, the rays intersect with the outer contour and inner contour of the LCD grinding area to obtain 2N intersection points, the intersection points of the rays and the outer contour form a set to obtain the starting point set {S(x t ,y t )} t＝1 n = {S(x1,y1),S(x2,y2),...,S(x t ,y t ),...,S(x n ,y n )}, the intersection points of the ray and the inner contour form a set of termination points {E(x1,y1),E(x2,y2),...,E(x n ,y n )}, where S(x n ,y n ) is the coordinate of the intersection of the ray and the outer contour, E(x n ,y n ) is the coordinate of the intersection of the ray and the inner contour; Step S3: filter and reconstruct the sampled data. The specific steps are as follows: S31: Calculate the distance DIST between the starting point and the end point in sequence t , if the distance is less than the limit distance DIST max , then it is recorded as a passing sample, otherwise it is counted as a deviation sample; S32: Calculate the average distance between the starting point and the ending point of the sample: S33: traverse the deviation samples in sequence, taking the starting point of the deviation sample as the base point and taking the average distance DIST avg is the length, and the end point that deviates from the sample is taken as the direction to calculate the corrected end point; S33: adding the corrected deviation samples to the passing samples to obtain a corrected start point set and an end point set to form edge sample data; Step S4, calculating the edge sample data as an edge region: performing smoothing processing on the starting point set and the ending point set respectively to obtain a starting contour line and an ending contour line, and then performing closed-loop processing on the starting contour line and the ending contour line to obtain a theoretical edge region; Step S5, bracketing the theoretical edge area in the direction of the starting point to obtain the defect occurrence area; Step S6, extracting the low brightness value area on the LCD image, calculating the area located in the low brightness value area and intersecting with the theoretical edge area, and obtaining the actual grinding area; Step S7, performing intersection operation on the actual grinding area and the defect occurrence area to obtain the defect area. If the defect area does not exist, it means that there is no grinding defect. If the defect area exists, the area and position information of the defect are calculated based on the defect area.

2. The method for analyzing LCD appearance defects according to claim 1, characterized in that: The smoothing process in step S4 is quadratic B-spline interpolation.

3. The method for analyzing LCD appearance defects according to claim 1, characterized in that: The number of rays n in step S2 is greater than or equal to 16.

Images