Machine vision based light-emitting panel detection method

Abstract

The invention relates to a machine vision based light-emitting panel detection method. The machine vision based light-emitting panel detection method is characterized in that an image of a product to be detected is obtained by establishing a background model so as to obtain a preprocessing image; then, the image of the product is independently processed through a topographic contour model and a radiation energy model; finally, a defect region is obtained by adopting synchronously summarized processing data. Therefore, the detection requirements of various common light-emitting panels can be met. In addition, by relying on mutual matching of various models, various defects of the light-emitting panels can be effectively overcome through machine vision, and the problem of a poor MURA defect convergence effect is solved. Furthermore, the machine vision based light-emitting panel detection method can be achieved by being matched with a conventional image acquisition system, detection software and relevant mechanical mechanisms and is easy to popularize.

Description

technical field [0001] The invention relates to a detection method, in particular to a machine vision-based light-emitting panel detection method. Background technique [0002] As far as the existing technology is concerned, machine vision is to use machines instead of human eyes for measurement and judgment. Specifically, the machine vision system refers to converting the captured target into an image signal through a machine vision product (that is, an image capture device, which is divided into CMOS and CCD), and transmitting it to a dedicated image processing system to obtain the shape information of the captured target. According to information such as pixel distribution, brightness, and color, it is converted into digital signals; the image system performs various operations on these signals to extract the characteristics of the target, and then controls the on-site equipment actions according to the results of the discrimination. [0003] Specifically, taking TFT-LCD...

AI Technical Summary

Problems solved by technology

The main problem of using this method for MURA defect detection is: when the MURA defect area is large and the contrast is low, when the "least squares B-spline fitting" method is used for processing, the defect features will be "fitted" into the background , but cannot achieve the purpose of "fitting"

In other words, it cannot distinguish between background and real defects

On the other hand, when the resolution of the module changes, the fitting effect changes until the background and features cannot be effectively distinguished

[0011] It can be seen that the common problems shown by the above methods (including similar methods) are: the various "methods" involved are based on the processing of some specific data, are only effective for several specific data sets, and show detection performance. That is, the robustness is poor, the model adaptability is poor, and it cannot meet the actual engineering needs

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