The Design of Cathode Copper Quality Inspection System Based on Machine Vision

Abstract

in order to meet the market requirements for the surface quality of cathode copper, a copper plate surface quality detection system based on machine vision is designed. The surface quality of cathode copper is judged by image acquisition and processing. According to the actual production conditions of a cathode copper production workshop in Guangxi and the characteristics of the cathode copper itself, the hardware equipment related to the Jetson nano and camera are selected to ensure that the images that can meet the processing requirements are collected. The Yolo algorithm is used to train the target detection, feature extraction and target recognition on the Linux system to obtain a data set. The data set trained by the computer is run on the Jeston nano micro controller, After the CSI camera collects the image data, the data is compared and displayed on the LCD1602. The detection system can increase the efficiency to automatically judge whether the product quality is qualified, solve the problems of low manual detection accuracy and large amount of labor, and realize the full-automatic production of cathode copper.