Method for achieving automatic detection and classification of glass defects on basis of computer software

Abstract

The invention relates to a method for achieving automatic detection and classification of glass defects on the basis of computer software. The method includes the steps that the detection software divides an image of glass according to a set partition threshold value to obtain a plurality of divided objects; the detection software processes the divided objects according to a neural network classification algorithm, and a plurality of detect feature values corresponding to the divided objects are extracted; the detection software analyzes the detect feature values corresponding to the divided objects through the neural network classification algorithm, and defect categories of the divided objects are obtained. The method for achieving automatic detection and classification of glass defects on the basis of the computer software is applicable to automatic production operation of electronic glass or other types of glass, glass check efficiency is improved, the quality is ensured when glass leaves a factory, the labor cost is lowered, the automatic progress of enterprises is accelerated, an automatic glass detection system can be popularized conveniently on a large scale, and the application range is wider.

Description

technical field [0001] The invention relates to the field of visual image detection, in particular to the field of glass quality detection, and specifically refers to a method for automatic detection and classification of glass defects based on computer software. Background technique [0002] In recent years, with the rapid growth of market demand for glass products, the production of glass products has undergone qualitative changes in terms of quality, variety, and production technology. Especially with the continuous development of production technology, high-end products have higher and higher quality requirements for glass substrates, especially electronic glass, there will be various defects such as bubbles, scratches, and pits on the glass, so the glass is fully guaranteed It is especially important to improve its level of quality. [0003] At present, the defect detection of electronic glass mainly uses manual online detection, which has low accuracy and high missed ...

AI Technical Summary

Problems solved by technology

[0003] At present, the defect detection of electronic glass mainly uses manual online detection, which has low accuracy and high missed detection rate. Manual detection is easily affected by the subjective factors of the inspectors, and it is easy to cause missed detection of glass defects, especially those with small distortion. Defects are missed, workers are prone to visual fatigue, especially on night shifts, the stability is not high, and the labor cost is high

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