Copper plate surface defect detection and automatic classification method based on machine vision and deep learning

Abstract

The invention discloses a copper plate surface defect detection and automatic classification method based on machine vision and deep learning. The method comprises the steps: 1, conveying a copper plate to a sensor fixing position through a conveying device; 2, controlling the conveying device to stop moving by a sensor, and triggering an industrial camera to collect images; 3, preprocessing the collected images; 4, inputting the preprocessed defect images into a pre-trained defect detection model to carry out intelligent identification on the surface of a copper part; 5, judging whether the surface of the copper plate has defects or not by the defect detection model; and 6, driving a mechanical arm to grab the defective copper plate into a corresponding defective product groove by a PC. The system comprises the industrial camera, a light source, the sensor, the conveying device, the defective product groove, the PC and the mechanical arm. The problems of low manual detection efficiency, low accuracy, high omission ratio and the like can be solved, meanwhile, the mechanical arm is controlled to autonomously complete the sorting task, and the method has the characteristics of high robustness and high automation level.

Description

technical field [0001] The invention relates to the fields of machine vision and deep learning, in particular to a method for detecting and automatically classifying defects on the surface of copper plates based on machine vision and deep learning. Background technique [0002] With the rapid development of industry, China has become the largest copper consumer in the world. In high-tech fields such as automobiles and aviation, the quality and stability of high-end copper plates are high. A small defect may have a huge impact on product quality. In the production and processing of copper plate workpieces, various types of defects will occur, such as bubbles, scratches, scratches, peeling and other defects, so defect detection is an indispensable part of the process. [0003] Traditional copper plate defect detection methods mainly include manual visual inspection and infrared detection technology, but manual detection technology is inefficient and cannot distinguish subtle d...

AI Technical Summary

Problems solved by technology

[0004] In order to solve the above problems, the purpose of the present invention is to propose a copper plate surface defect detection and automatic classification method based on machine vision and deep learning, aiming to solve the problem of low efficiency and accuracy of existing manual visual inspection, as well as the degree of automation of manual sorting low problem

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