Open AccessAn Effective Method for Defect Detection of Copper Coated Iron Wire Based on Machine Vision
Author(s) -
Yuqing Ma,
Xueren Ge
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/631/2/022077
Subject(s) - automation , machine vision , production line , artificial intelligence , computer vision , pixel , segmentation , computer science , copper , copper wire , interference (communication) , division (mathematics) , electroplating , line (geometry) , materials science , engineering , metallurgy , mechanical engineering , nanotechnology , telecommunications , mathematics , channel (broadcasting) , arithmetic , geometry , layer (electronics)
In this study, a defect detection system based on machine vision is established to distinguish the defective copper coated iron wires during electroplating. By collecting the real-time images in the production line using a CCD industrial color camera, this system could effectively identify different colors and connect the same pixel region based on Halcon. Also, image segmentation is applied to eliminating the interference of the bearings placed around the wires. Then, the division procedure is completed and the defective area is classified. This defect detection system could efficiently improve the level of automation during industrial production.