有色金属科学与工程2025，Vol.16Issue(4)：544-551,8.DOI:10.13264/j.cnki.ysjskx.2025.04.006
基于改进DeeplabV3+的阴极铜板结瘤缺陷识别方法
Identification method of copper cathode plate nodulation defects based on improved DeeplabV3+
靖青秀 1常琪琪 1杨雪晴 1张志聪 1黄晓东2
作者信息
- 1. 江西理工大学,材料冶金化学学部,江西 赣州 341000
- 2. 江西理工大学,经济管理学院,江西 赣州 341000
- 折叠
摘要
Abstract
Surface nodulation is a major quality defect in electrolytic copper cathode products.In production practices,the problems that occur during the electrolytic process are often diagnosed according to the analysis of different types of nodules on the cathode copper plates.The traditional manual observation method for determining nodule types on copper cathode plates has the disadvantages of low accuracy,time lag,etc.An improved DeeplabV3+semantic segmentation model was proposed,which can be deployed on-site to achieve real-time identification of nodule types on the surfaces of copper cathode plates.MobileNetV2 was the backbone network to achieve lightweighting,with a model size of 11.15%of its original size.A spatial and channel attention mechanism was introduced to capture multi-scale information to improve the accuracy of nodule edge region segmentation,resulting in a 1.06%increase in the accuracy of defect category classification.The experimental results showed the algorithm's excellent segmentation and classification effects on electrolytic copper cathode plates'point-like,clustered and edge nodule defects.The segmentation accuracy on the test set reached 91.58%,which could meet the actual production demands and provide a practical reference for further intelligent control of surface quality online detection of cathode copper plates in electrolytic copper production.关键词
阴极铜板结瘤/图像分割/DeeplabV3+/注意力机制Key words
copper cathode plate nodulation/image segmentation/DeeplabV3+/attention mechanism分类
矿业与冶金引用本文复制引用
靖青秀,常琪琪,杨雪晴,张志聪,黄晓东..基于改进DeeplabV3+的阴极铜板结瘤缺陷识别方法[J].有色金属科学与工程,2025,16(4):544-551,8.
