Mobile phone glass cover plate window area defect detection method based on machine vision

Abstract

The invention discloses a mobile phone glass cover plate window area defect detection method based on machine vision. The method comprises the following steps of 1, acquiring a mobile phone screen image; 2, carrying out rough detection on a mobile phone screen area; 3, extracting defects of the mobile phone screen image through a threshold segmentation algorithm; 4, connecting the scattered pointsin the dense point cluster area by using a clustering algorithm; 5, performing defect classification by using a neural network classifier; 6, extracting the area, the length and the radius of the defect area, and comparing according to a detection standard; 7, re-classifying the defects by using a deep learning classifier; and 8, counting information and quantity of various defects. According tothe detection algorithm, the principle of rough detection and fine detection in sequence is followed, the defects of pits, scratches, dirt, broken filaments and the like in the screen areas of the mobile phone glass cover plates of various different types can be rapidly and accurately extracted, and meanwhile the detection precision can be adjusted according to the detection standards of differentproduct window areas.

Description

technical field [0001] The invention relates to the field of visual inspection, in particular to a method for detecting defects in the window area of ​​a glass cover plate of a mobile phone based on machine vision. Background technique [0002] In recent years, driven by technologies such as 5G and wireless charging, non-metallic mobile phone covers have become mainstream. Among them, the glass cover has better mechanical properties and optical properties, and the cost is lower than ceramic materials, so it is favored by 3C product companies. However, defects such as pitting, scratches, dirt, and edge collapse will inevitably occur during the actual manufacturing and transportation processes. Timely detection of defects in the production process can avoid process waste and monitor product quality, thereby ensuring high-quality product production and saving production costs. [0003] At present, many domestic cover glass manufacturers still use a large number of manual visu...

AI Technical Summary

Problems solved by technology

At present, many domestic inspection equipment manufacturers have successively carried out research and development of glass inspection algorithms, but there are still problems such as unstable inspection process, high missed inspection or high pass inspection rate.

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