Copper part surface defect visual inspection system and inspection method based on semi-supervised learning model

Abstract

The invention discloses a copper part surface defect visual inspection system and a copper part surface defect visual inspection method based on a semi-supervised learning model, belonging to the technical field of copper part surface defect visual inspection. According to the invention, aiming at common copper part surface defects, a conveyer belt and a guide rail are used for moving a copper part to four corresponding inspection stations, an image acquisition system is adopted to shoot pictures so as to sequentially inspect whether the upper and lower surfaces as well as two sides surfaces of the copper part have defects and classify the copper part according to the judgment result, and the system is also equipped with a camera so as to remotely monitor the running condition of the visual inspection system in real time. Operations of parameter adjustment after configuration of the system, data storage and statistics, real-time remote system monitoring and maintenance are carried out in the cloud terminal. The system and the method can realize automatic and continuous inspection on the surface defects of the copper parts, can be used for replacing manual visual inspection methods, and can improve inspection efficiency and accuracy.

Description

technical field [0001] The invention relates to the technical field of visual detection of copper surface defects, and more specifically, to a visual detection system and detection method for copper surface defects based on a semi-supervised learning model. Background technique [0002] As the key components of traction motors such as high-speed rail and subway, precision copper parts of motors have strict requirements on their surface quality. At present, the factory mainly adopts coloring flaw detection and manual visual inspection for the detection of surface defects (inclusions, cracks, pores, etc.) of copper parts. Low and high misjudgment rate, coupled with the rise in labor costs in recent years, therefore, it is very necessary to replace traditional detection methods with machine vision automated detection systems. [0003] After retrieval, relevant schemes for the detection of surface defects of copper parts have been made public. For example, the Chinese patent a...

AI Technical Summary

Problems solved by technology

[0006] Aiming at the problems in the prior art that the detection efficiency of copper parts by manual visual inspection is low and it is easy to cause subsequent pollution, the present invention proposes a visual detection system and detection method for copper parts surface defects based on a semi-supervised learning model; The invention mainly performs image acquisition, image processing, and defect determination on the local processor for the copper parts to be detected; and real-time monitoring, data storage and system maintenance through the cloud, which can realize the detection of defects such as inclusions, cracks and pits on the surface of copper parts Automatic continuous detection

Images