Silver paste coating specification detection method for oxide LCD module

Abstract

The invention relates to the technical field of LCD module detection, in particular to a silver paste coating specification detection method for an oxide LCD module, which comprises the following steps: image acquisition: scanning the oxide LCD module line by line on a conveyor belt on an assembly line, capturing pixel points of each line of the moving oxide LCD module one by one, and converting the pixel points into digital signals; obtaining a Mark point of the oxide LCD module; obtaining the size of the object in the image; establishing a distance reference coordinate system; measuring and analyzing the silver award specification by using the coordinate system; determining the positions and sizes of the points by measuring the distance and direction between each point on the target object and the original point of the coordinate system; distinguishing silver paste coating areas; and judging the coating specification. The silver paste specification of the oxide LCD module coated with the silver paste can be rapidly detected in real time, the production efficiency of a module production line is improved, the production cost is reduced, and the full-automatic module industry is achieved.

Description

Technical Field

[0001] The invention relates to the technical field of LCD module detection, and in particular to a method for detecting silver paste coating specifications of an oxide LCD module. Background Art

[0002] At present, LCD display modules are changing with each passing day, new technologies and new processes are constantly updated and iterated, and their applications are becoming more and more extensive. Due to the excellent performance and stability of oxide LCD, it has begun to be widely used in TCP, automotive and other fields. Therefore, the requirements for its process detection are also higher. Silver paste coating is an indispensable and important process for oxide LCD modules, which plays an important role in the display performance of oxide LCD modules. At present, the main processes of oxide LCD modules have been automated. However, the existing silver paste detection generally uses manual microscopy, which has problems such as low efficiency, missed inspection and wrong inspection of specifications and dimensions, which affects the development of intelligent manufacturing module factories and seriously limits the application and technological progress of oxide LCD modules. The silver paste detection of oxide LCD modules has the following defects:

[0003] 1. Slow detection speed: Traditional detection methods require manual operation, which is time-consuming and labor-intensive, and has a slow detection speed and cannot meet the needs of the production line.

[0004] 2. Poor reliability: Due to human factors and equipment limitations, existing detection technologies have certain defects in reliability and are prone to missed detection or false detection.

[0005] 3. High cost: Traditional detection methods require special equipment and manpower investment, and one person needs to be equipped with a dedicated microscopy device, which is costly and not conducive to the company's cost control. Summary of the invention

[0006] In view of the problems of the prior art, the present invention provides a method for detecting the silver paste coating specifications of an oxide LCD module. The method is cleverly designed and can quickly perform real-time detection on the silver paste specifications of the oxide LCD module after silver paste coating, which helps to improve the production efficiency of the module production line, reduce production costs, and realize a fully automated module industry.

[0007] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0008] The present invention provides a method for detecting the silver paste coating specifications of an oxide LCD module, which comprises the following steps:

[0009] Step S10, image acquisition: the conveyor belt on the assembly line scans the oxide LCD module line by line, and the pixels of each line of the moving oxide LCD module are captured one by one, and converted into digital signals;

[0010] Step S20, obtaining the Mark points of the oxide LCD module by identifying the Mark points based on SIFT;

[0011] Step S30, pixel size characterization: obtaining the size of the object in the image;

[0012] Step S40, establish a distance reference coordinate system: determine the center pixel point of the Mark point, take it as the origin, establish the X-axis and Y-axis, and determine the direction of the coordinate axis; after establishing the two-dimensional rectangular coordinate system, use the coordinate system to measure and analyze the silver award specifications; determine the specific position and size of these points by measuring the distance and direction of each point on the target object and the origin of the coordinate system;

[0013] Step S50, image processing: distinguishing the silver paste coating area;

[0014] Step S60: Determine the silver paste coating specification.

[0015] The image acquisition method in step S10 is:

[0016] Step S11, equipment selection and parameter adjustment: Before image acquisition, it is necessary to set the parameters of the line array camera, including exposure time, gain, and synchronization signal; adjust the position, angle, and conveyor speed of the camera so that it scans the object at a uniform speed;

[0017] Step S12, image output: After setting the parameters and completing the focus and positioning, start the line array camera and the conveyor belt. The line array camera performs real-time scanning and image acquisition, and transmits the acquired data to the computer.

[0018] The method of Mark point recognition based on SIFT in step S20 is:

[0019] Step S21, feature extraction of Mark points: In Mark point recognition, key points and descriptors are first extracted from the image of the target object and the reference image, and then the position of the target object in the image is determined by matching the descriptors;

[0020] Step S22, key point matching: The key point matching process uses the nearest neighbor matching algorithm, that is, for each key point, the closest key point is found in the reference image, and the matching degree is evaluated by comparing the distances between their descriptors; the matching method is a ratio test based on the nearest neighbor matching, that is, for each key point, its nearest neighbor and the next nearest neighbor are found, and if the distance ratio between the nearest neighbor and the next nearest neighbor is less than a threshold, the match is considered successful, otherwise the match is considered failed; the distance ratio calculation method is as follows:

[0021]

[0022] Among them, d1 is the distance between the nearest neighbor descriptor and the current key point descriptor, and d2 is the distance between the next nearest neighbor descriptor and the current key point descriptor. If the distance ratio is less than a set threshold, the match is considered successful.

[0023] The pixel size characterization method in step S30 is: by calculating the number of pixels in the image, the size of the object in the image can be obtained. The calculation method is as follows:

[0024] S = L × N;

[0025] Among them, S is the size of the object, L is the actual size represented by each pixel, and N is the number of pixels occupied by the object.

[0026] The image processing method in step S50 is:

[0027] Step S51: The method for presetting the image ROI area is as follows: according to the position of the Mark point of the oxide LCD module and the position of the silver paste coating, the retention thresholds y of the upper, lower, left and right of the image are respectively preset manually. 上 ,y 下 、x 左 、x 右 , Wherein, x, y are the pixel coordinates of the coordinate system established in step S40;

[0028] According to the thresholds set above, only those that meet the formula are retained: Pixels of

[0029] Step S52, image ROI region binary segmentation: convert the ROI image into a grayscale image, and then use the Otsu method to segment it into the silver paste area and other areas; by finding a threshold value, the inter-class variance between the foreground and the background is maximized, thereby achieving image binary segmentation; the specific steps are as follows:

[0030] First, calculate the grayscale histogram of the image and normalize it;

[0031] Then, a threshold T is selected from the grayscale level 0 to 255 to divide the image into two parts: pixels less than or equal to T are one category, and pixels greater than T are another category;

[0032] Calculate the number of pixels, average gray value and inter-class variance of the two classes;

[0033] According to the calculation formula of between-class variance σ 2 =ω1(μ1-μ T ) 2 +ω2(μ2-μ T ) 2 , find the threshold T that maximizes the inter-class variance;

[0034] Among them, ω1 and ω2 are the pixel proportions of the two classes, μ1 and μ2 are the average grayscale values ​​of the two classes, and μ T is the average gray value of the entire image;

[0035] By calculating the inter-class variance under different thresholds, the threshold that maximizes the inter-class variance is selected as the final binarization threshold.

[0036] The method for determining the silver paste coating specification in step S60 is as follows:

[0037] S61. Calculate the number of silver paste coating areas:

[0038] The fine noise in the image is filtered out through opening and closing operations. Then, the number of silver paste areas n is calculated through the contour search algorithm in Opencv. Finally, the number of silver paste areas n is compared with the set coating area number n. set Compare, if n≠n set , then this oxide LCD module has a breakpoint or missed coating when the silver paste is coated, so it is directly judged as not meeting the specifications; otherwise, after passing the judgment of this step, it enters step S62;

[0039] S62: Calculate the width and height of the silver paste coating:

[0040] First, the extreme values ​​x of the x-axis and y-axis of each silver paste coating area are obtained one by one through the traversal algorithm and the sorting algorithm. min , x max ,y min ,y max ; The calculation method is as follows:

[0041]

[0042] Among them, MAX and MIN are functions for finding maximum and minimum values ​​respectively;

[0043] Then, the number of pixels of the height and width of the silver paste coating area is obtained by the extreme values ​​of the two coordinate axes, and finally the distance dimension S represented by each pixel is obtained. pixel Calculate the height h and width w of the silver paste as follows:

[0044]

[0045] Finally, the height h and width w of the silver paste are compared with the set thresholds of the height and width of the silver paste coating. If h and w meet the formula: If the condition is met, that is, the height and width of the silver paste coating specifications are within the threshold range, it can be determined that the silver paste coating specifications meet the requirements, otherwise, they do not meet the requirements.

[0046] Beneficial effects of the present invention:

[0047] The present invention is cleverly designed, using the Mark points of the oxide LCD as a reference datum to establish a reference coordinate system, and using image processing algorithms and mathematical principles to determine the silver paste coating specifications; the present invention realizes rapid and real-time determination of the silver paste specifications while ensuring the accuracy of the determination, greatly improving the production efficiency of the oxide LCD module, and can quickly perform real-time detection of the silver paste specifications of the oxide LCD module after silver paste coating, which helps to improve the production efficiency of the module production line, reduce production costs, and realize a fully automated module industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 The present invention is a hardware equipment layout diagram of a method for detecting the silver paste coating specifications of an oxide LCD module.

[0049] Figure 2 The present invention is a flow chart of a method for detecting the silver paste coating specifications of an oxide LCD module.

[0050] Figure 3 A schematic diagram of the principle of establishing a distance reference coordinate system for determining the silver paste specifications of the present invention.

[0051] Figure 4 Schematic diagram of the steps and process for determining the silver paste specifications of the present invention. DETAILED DESCRIPTION

[0052] In order to facilitate the understanding of those skilled in the art, the present invention is further described below in conjunction with the embodiments and drawings, and the contents mentioned in the embodiments are not intended to limit the present invention. The present invention is described in detail below in conjunction with the drawings.

[0053] The embodiment of the present application provides a method for detecting the silver paste coating specifications of an oxide LCD module, which comprises the following steps:

[0054] Step S10, image acquisition: the conveyor belt on the assembly line scans the oxide LCD module line by line, and the pixels of each line of the moving oxide LCD module are captured one by one, and converted into digital signals;

[0055] Step S20, obtaining the Mark points of the oxide LCD module by identifying the Mark points based on SIFT;

[0056] Step S30, pixel size characterization: obtaining the size of the object in the image;

[0057] Step S40, establish a distance reference coordinate system: determine the center pixel point of the Mark point, take it as the origin, establish the X-axis and Y-axis, and determine the direction of the coordinate axis; after establishing the two-dimensional rectangular coordinate system, use the coordinate system to measure and analyze the silver award specifications; determine the specific position and size of these points by measuring the distance and direction of each point on the target object and the origin of the coordinate system;

[0058] Step S50, image processing: distinguishing the silver paste coating area;

[0059] Step S60: Determine coating specifications.

[0060] Furthermore, the hardware device layout design of the embodiment of the present application is as follows: Figure 1 As shown, the following equipment is included:

[0061] Drive shafts and conveyor belts: As the power source for the flow of oxide LCD modules, their barrier-free operation not only provides continuous and stable power support for the production line, but also effectively promotes the smooth flow, thereby ensuring efficient production and high-quality output of LCD modules. In addition, this also provides the basis for the imaging of industrial line scan cameras, providing important support for the monitoring and quality control of the production process.

[0062] Bar-shaped industrial linear array light source: Bar-shaped industrial linear array light source plays a vital role in the imaging of industrial line scan cameras. By providing a stable and uniform light source, the bar-shaped industrial linear array light source can effectively eliminate shadows and reflections, and improve the clarity and accuracy of imaging. In addition, the bar-shaped industrial linear array light source can also help reduce noise and improve the contrast of the image, thereby better showing the details and features of the inspected object. Through reasonable light source design and layout, the imaging quality of industrial linear array cameras can be effectively improved, providing reliable support for monitoring and quality control of the production process.

[0063] Industrial line array camera: Industrial line array camera can quickly and accurately obtain image information of the object to be inspected by receiving light reflected by different materials on the surface of oxide LCD, thus realizing inspection tasks on automated production lines.

[0064] Specifically, under the above settings, the embodiment of the present application can quickly perform real-time detection on the silver paste specifications of the oxide LCD module after silver paste coating, which is helpful to improve the production efficiency of the module production line, reduce production costs, and provide high-quality solutions for the fully automated module industry; wherein, the embodiment of the present application uses the Mark points of the oxide LCD as a reference benchmark, establishes a reference coordinate system, and uses image processing algorithms and mathematical principles to determine the silver paste coating specifications; the embodiment of the present application can flexibly adjust hardware equipment such as the camera light source according to the different precision requirements of actual production, thereby achieving fast and real-time silver paste specification determination, while ensuring the accuracy of the determination, greatly improving the production efficiency of the oxide LCD module.

[0065] In the embodiment of the present application, image acquisition is performed using a linear array camera, and the conveyor belt on the upper assembly line is used to scan the oxide LCD module line by line, and the pixels of each row of the oxide LCD module are captured one by one through the moving oxide LCD module, and converted into digital signals; the method of image acquisition in step S10 is:

[0066] Step S11, equipment selection and parameter adjustment: Before image acquisition, it is necessary to set the parameters of the line array camera, including exposure time, gain, and synchronization signal; adjust the position, angle, and conveyor speed of the camera so that it scans the object at a uniform speed;

[0067] Step S12, image output: After setting the parameters and completing the focus and positioning, start the line array camera and the conveyor belt. The line array camera performs real-time scanning and image acquisition, and transmits the acquired data to the computer.

[0068] Among them, the Mark point is a special mark in the LCD module; it is usually in the shape of a "cross" and is located at the lower left and lower right corners of the module terminal side. It is the basis of the automation equipment of the entire module and is used for positioning, calibration and detection of the module during production and assembly. Therefore, silver paste specification detection also needs to use the Mark point as the basis of the entire method. The principle of the SIFT-based Mark point recognition method is to use the SIFT algorithm to extract key points and descriptors in the image, and then identify the Mark points of the target object by matching these descriptors. The SIFT-based Mark point recognition method in step S20 is:

[0069] Step S21, feature extraction of Mark points: In Mark point recognition, key points and descriptors are first extracted from the image of the target object and the reference image, and then the position of the target object in the image is determined by matching the descriptors;

[0070] Step S22, key point matching: The key point matching process uses the nearest neighbor matching algorithm, that is, for each key point, the closest key point is found in the reference image, and the matching degree is evaluated by comparing the distances between their descriptors; the matching method is a ratio test based on the nearest neighbor matching, that is, for each key point, its nearest neighbor and the next nearest neighbor are found, and if the distance ratio between the nearest neighbor and the next nearest neighbor is less than a threshold, the match is considered successful, otherwise the match is considered failed; the distance ratio calculation method is as follows:

[0071]

[0072] Among them, d1 is the distance between the nearest neighbor descriptor and the current key point descriptor, and d2 is the distance between the next nearest neighbor descriptor and the current key point descriptor. If the distance ratio is less than a set threshold, the match is considered successful.

[0073] The pixel size characterization method in step S30 is: by calculating the number of pixels in the image, the size of the object in the image can be obtained. The calculation method is as follows:

[0074] S = L × N;

[0075] Among them, S is the size of the object, L is the actual size represented by each pixel, and N is the number of pixels occupied by the object.

[0076] In the embodiment of the present application, since the surface of the glass of the oxide LCD module will produce specular reflection under the illumination of the strip array light source, while the surface of the silver paste will produce diffuse reflection; therefore, there will be obvious differences between the surface of the silver paste and other materials; the brightness of the surface of the glass of the oxide LCD module will be significantly higher than that of the silver paste surface; therefore, an image processing algorithm can be used to distinguish the silver paste coating area; the image processing method in step S50 is:

[0077] Step S51, the image captured by the linear array camera has high resolution, which contains a large number of redundant pixels; in order to improve the processing speed of the algorithm, the redundant pixels in the image can be significantly reduced by presetting the region of interest (ROI); by setting the ROI, the number of unnecessary pixels in the image can be effectively reduced, thereby reducing the amount of data and improving the algorithm processing efficiency; the method for presetting the image ROI area is: according to the position of the Mark point of the oxide LCD module and the position of the silver paste coating, the retention thresholds y of the upper, lower, left and right of the image are manually preset 上 ,y 下 、x 左 、x 右 , Wherein, x, y are the pixel coordinates of the coordinate system established in step S40;

[0078] According to the thresholds set above, only those that meet the formula are retained: Pixels of

[0079] Step S52, image ROI region binary segmentation: convert the ROI image into a grayscale image, and then use the Otsu method to segment it into the silver paste area and other areas; by finding a threshold value, the inter-class variance between the foreground and the background is maximized, thereby achieving image binary segmentation; the specific steps are as follows:

[0080] First, calculate the grayscale histogram of the image and normalize it;

[0081] Then, a threshold T is selected from the grayscale level 0 to 255 to divide the image into two parts: pixels less than or equal to T are one category, and pixels greater than T are another category;

[0082] Calculate the number of pixels, average gray value and inter-class variance of the two classes;

[0083] According to the calculation formula of between-class variance σ 2 =ω1(μ1-μ T ) 2 +ω2(μ2-μ T ) 2 , find the threshold T that maximizes the inter-class variance;

[0084] Among them, ω1 and ω2 are the pixel proportions of the two classes, μ1 and μ2 are the average grayscale values ​​of the two classes, and μ T is the average gray value of the entire image;

[0085] By calculating the inter-class variance under different thresholds and selecting the threshold that maximizes the inter-class variance as the final binarization threshold, Otsu's binarization can be achieved; this method can effectively and adaptively select the optimal threshold according to the characteristics of the image, and is suitable for image segmentation of silver paste and glass of oxide LCD.

[0086] In the embodiment of the present application, according to the above process, the area where the silver paste is applied has been successfully determined, and the conditions for determining the coating specifications have been met; now it is only necessary to calculate the relevant parameters using the existing conditions and compare them with the set silver paste specification parameters to determine the specifications of the silver paste coating in real time. The method for determining the coating specifications in step S60 is:

[0087] S61. Calculate the number of silver paste coating areas:

[0088] First, the fine noise in the image is filtered out through opening and closing operations. Then, the number of silver paste areas n is calculated through the contour search algorithm in Opencv. Finally, the number of silver paste areas n is compared with the set coating area number n. set Compare, if n≠n set, then this oxide LCD module has a breakpoint or missed coating when the silver paste is coated, so it is directly judged as not meeting the specifications; otherwise, after passing the judgment of this step, it enters step S62;

[0089] S62: Calculate the width and height of the silver paste coating:

[0090] First, the extreme values ​​x of the x-axis and y-axis of each silver paste coating area are obtained one by one through the traversal algorithm and the sorting algorithm. min , x max ,y min ,y max ; The calculation method is as follows:

[0091]

[0092] Among them, MAX and MIN are functions for finding maximum and minimum values ​​respectively;

[0093] Then, the number of pixels of the height and width of the silver paste coating area is obtained by the extreme values ​​of the two coordinate axes, and finally the distance dimension S represented by each pixel is obtained. pixel Calculate the height h and width w of the silver paste as follows:

[0094]

[0095] Finally, the height h and width w of the silver paste are compared with the set thresholds of the height and width of the silver paste coating. If h and w meet the formula: If the condition is met, that is, the height and width of the silver paste coating specifications are within the threshold range, it can be determined that the silver paste coating specifications meet the requirements, otherwise, they do not meet the requirements.

[0096] Key point 1 of the embodiment of the present application: a pipeline oxide LCD module acquisition system of a linear array camera is built; Key point 2: the Mark points of the oxide LCD are identified using the SIFT algorithm, and a two-axis coordinate system is established based on the Mark points as a specification determination benchmark; Key point 3 uses: pixels represent the actual size, so that the present invention can flexibly adjust hardware equipment such as cameras, lenses, and light sources according to actual accuracy requirements, which is beneficial to the production cost of oxide LCD modules.

[0097] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention is disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technician familiar with the profession can make some changes or modifications to equivalent embodiments of equivalent changes by using the technical contents disclosed above without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent changes and modifications made to the above embodiments according to the technology of the present invention are within the scope of the technical solution of the present invention without departing from the content of the technical solution of the present invention.

Claims

1. A method for detecting the silver paste coating specification of an oxide LCD module, characterized in that: The following steps are involved: Step S10, image acquisition: the conveyor belt on the assembly line scans the oxide LCD module line by line, and the pixels of each line of the moving oxide LCD module are captured one by one, and converted into digital signals; Step S20, obtaining the Mark points of the oxide LCD module by identifying the Mark points based on SIFT; Step S30, pixel size characterization: obtaining the size of the object in the image; Step S40, establish a distance reference coordinate system: determine the center pixel point of the Mark point, take it as the origin, establish the X-axis and Y-axis, and determine the direction of the coordinate axis; after establishing the two-dimensional rectangular coordinate system, use the coordinate system to measure and analyze the silver award specifications; determine the specific position and size of these points by measuring the distance and direction of each point on the target object and the origin of the coordinate system; Step S50, image processing: distinguishing the silver paste coating area; Step S60: Determine the silver paste coating specification.

2. The method for detecting the silver paste coating specification of an oxide LCD module according to claim 1, characterized in that: The method of image acquisition in step S10 is: Step S11, equipment selection and parameter adjustment: Before image acquisition, it is necessary to set the parameters of the line array camera, including exposure time, gain, and synchronization signal; adjust the position, angle, and conveyor speed of the camera so that it scans the object at a uniform speed; Step S12, image output: After setting the parameters and completing the focus and positioning, start the line array camera and the conveyor belt. The line array camera performs real-time scanning and image acquisition, and transmits the acquired data to the computer.

3. The method for detecting the silver paste coating specification of an oxide LCD module according to claim 1, characterized in that: The method for identifying Mark points based on SIFT in step S20 is: Step S21, feature extraction of Mark points: In Mark point recognition, key points and descriptors are first extracted from the image of the target object and the reference image, and then the position of the target object in the image is determined by matching the descriptors; Step S22, key point matching: The key point matching process uses the nearest neighbor matching algorithm, that is, for each key point, the closest key point is found in the reference image, and the matching degree is evaluated by comparing the distances between their descriptors; the matching method is a ratio test based on the nearest neighbor matching, that is, for each key point, its nearest neighbor and the next nearest neighbor are found, and if the distance ratio between the nearest neighbor and the next nearest neighbor is less than a threshold, the match is considered successful, otherwise the match is considered failed; the distance ratio calculation method is as follows: Among them, d1 is the distance between the nearest neighbor descriptor and the current key point descriptor, and d2 is the distance between the next nearest neighbor descriptor and the current key point descriptor. If the distance ratio is less than a set threshold, the match is considered successful.

4. The method for detecting the silver paste coating specification of an oxide LCD module according to claim 1, characterized in that: The pixel size characterization method in step S30 is: by calculating the number of pixels in the image, the size of the object in the image can be obtained. The calculation method is as follows: S = L × N; Among them, S is the size of the object, L is the actual size represented by each pixel, and N is the number of pixels occupied by the object.

5. The method for detecting the silver paste coating specification of an oxide LCD module according to claim 1, characterized in that: The image processing method in step S50 is: Step S51: The method for presetting the image ROI area is as follows: according to the position of the Mark point of the oxide LCD module and the position of the silver paste coating, the retention thresholds y of the upper, lower, left and right of the image are respectively preset manually. 上 ,y 下 、x 左 、x 右 , Wherein, x, y are the pixel coordinates of the coordinate system established in step S40; According to the thresholds set above, only those that meet the formula are retained: Pixels of Step S52, image ROI region binary segmentation: convert the ROI image into a grayscale image, and then use the Otsu method to segment it into the silver paste area and other areas; by finding a threshold value, the inter-class variance between the foreground and the background is maximized, thereby achieving image binary segmentation; the specific steps are as follows: First, calculate the grayscale histogram of the image and normalize it; Then, a threshold T is selected from the grayscale level 0 to 255 to divide the image into two parts: pixels less than or equal to T are one category, and pixels greater than T are another category; Calculate the number of pixels, average gray value and inter-class variance of the two classes; According to the calculation formula of between-class variance σ 2 =ω1(μ1-μ T ) 2 +ω2(μ2-μ T ) 2 , find the threshold T that maximizes the inter-class variance; Among them, ω1 and ω2 are the pixel proportions of the two classes, μ1 and μ2 are the average grayscale values ​​of the two classes, and μ T is the average gray value of the entire image; By calculating the inter-class variance under different thresholds, the threshold that maximizes the inter-class variance is selected as the final binarization threshold.

6. The method for detecting the silver paste coating specification of an oxide LCD module according to claim 1, characterized in that: The method for determining the silver paste coating specification in step S60 is: S61. Calculate the number of silver paste coating areas: The fine noise in the image is filtered out through opening and closing operations. Then, the number of silver paste areas n is calculated through the contour search algorithm in Opencv. Finally, the number of silver paste areas n is compared with the set coating area number n. set Compare, if n≠n set , then this oxide LCD module has a breakpoint or missed coating when the silver paste is coated, so it is directly judged as not meeting the specifications; otherwise, after passing the judgment of this step, it enters step S62; S62: Calculate the width and height of the silver paste coating: First, the extreme values ​​x of the x-axis and y-axis of each silver paste coating area are obtained one by one through the traversal algorithm and the sorting algorithm. min , x max ,y min ,y max ; The calculation method is as follows: Among them, MAX and MIN are functions for finding maximum and minimum values ​​respectively; Then, the number of pixels of the height and width of the silver paste coating area is obtained by the extreme values ​​of the two coordinate axes, and finally the distance dimension S represented by each pixel is obtained. pixel Calculate the height h and width w of the silver paste as follows: Finally, the height h and width w of the silver paste are compared with the set thresholds of the height and width of the silver paste coating. If h and w meet the formula: If the condition is met, that is, the height and width of the silver paste coating specifications are within the threshold range, it can be determined that the silver paste coating specifications meet the requirements, otherwise, they do not meet the requirements.

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