Defect detection method based on visual identification and system

Abstract

The invention relates to the defect detection technology field and discloses a defect detection method based on visual identification and a system. The method comprises steps that an image of a to-be-detected panel is acquired; edge detection on the image is carried out, and a target contour is extracted; the target contour is a contour on which defect detection is carried out; a first tangential gray scale gradient is calculated; the first tangential gray scale gradient is a tangential gray scale gradient of a first pixel point on the target contour; the first tangential gray scale gradient is equal to a difference value of a gray scale of a first pixel and a gray scale of a second pixel; the second pixel is adjacent to the first pixel and is before the first pixel on the target contour; according to the first tangential gray scale gradient, whether defect is detected is determined; if the tangential gray scale gradient satisfies a preset defect determination condition, that defect is detected is determined. Through the method, automatic detection on fine defects in a panel printing process is realized, a detection result is objective, and dependence on artificial experience can be avoided.

Description

technical field [0001] The invention relates to the technical field of defect detection, in particular to a defect detection method and system based on visual recognition. Background technique [0002] The production and processing process of glass cover plates for electronic equipment generally includes: cutting, grooving, chamfering, fine carving, flat grinding, cleaning, electroplating / screen printing, lens cleaning, and packaging. In the process of realizing the present invention, the inventor of the present invention finds that there are following problems in the prior art: [0003] At present, most commonly used printing defect inspection methods are based on the edge extraction of the glass cover image, and then analyze the flatness and concave-convex area of ​​the edge contour. For details, please refer to figure 1 , taking a mobile phone panel as an example, figure 1 The image in is based on the real image of the phone panel, figure 2 yes figure 1 An enlarged s...

AI Technical Summary

Problems solved by technology

In the prior art, the Figure 5 ~ Figure 8 Among them, it is impossible to detect the small defects caused by the damage of the printing screen

[0004] The existing process is to use manual visual inspection during the final inspection. This method cannot prevent the batch failure caused by the equipment and production process. A cover plate usually needs to go through as many as 20 printing and printing processes from the transparent substrate to the finished product. Baking, cumulatively takes hours

At present, the single-pass printing capacity of conventional automated screen printing machines is 600-1400 pieces per hour. From the first occurrence of defective products to the final inspection, thousands of defective products may have occurred, resulting in increased production costs and reduced effective production capacity.

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