冷轧铜带表面缺陷智能识别方法  

Intelligent recognition method for surface defects of cold rolling copper strip

作　　者：徐扬欢 王东城 刘宏民[1,2] 于华鑫 XU Yang-huan;WANG Dong-cheng;LIU Hong-min;YU Hua-xin(National Engineering Research Center for Equipment and Technology of Cold Rolling Strip,Yanshan University,Qinhuangdao 066004,China;State Key Laboratory of Metastable Materials Science and Technology,Yanshan University,Qinhuangdao 066004,China)

机构地区：[1]燕山大学国家冷轧板带装备及工艺工程技术研究中心,秦皇岛066004 [2]燕山大学亚稳材料制备技术与科学国家重点实验室,秦皇岛066004

出　　处：《中国有色金属学报》2022年第10期2950-2964,共15页The Chinese Journal of Nonferrous Metals

基　　金：国家自然科学基金资助项目(52074242);河北省高端人才和“巨人计划”创新团队资助项目(2019)。

摘　　要：表面质量是冷轧铜带重要质量指标之一。为实现铜带表面缺陷的精准自动检测,首先对常见表面缺陷进行分类,并制作了铜带表面缺陷图像数据集(YSU_CSC);然后,以卷积神经网络EfficientNet为核心,基于迁移学习策略,通过训练实验建立了冷轧铜带表面缺陷智能识别模型,同时与其他三种常用的卷积神经网络缺陷识别结果进行对比。结果表明:该模型的精度较高,准确率达到93.05%,单张缺陷图像平均识别时间为197 ms,综合性能较好,可以满足工程要求;最后,将该模型在测试集上的缺陷识别结果进行可视化,分析了该模型对部分图像识别错误的原因,给出了进一步优化的方向。Surface quality is one of the significant indicators of cold rolling copper strip product quality.In order to realize the accurate and automatic detection of copper strip surface defects,this article first classifies common surface defects,and creates a copper strip surface defect image dataset(YSU_CSC).Then,with the EfficientNet convolutional neural network as the core,based on the transfer learning strategy,the optimal cold rolling copper strip surface defect recognition model was established through training experiments.At the same time,it was compared with the defect recognition model established by the other three convolution neural network algorithms.The results show that the accuracy of this model is the highest,and the accuracy reaches 93.05%,the average recognition time of single defect image is 197 ms,and the comprehensive performance is the best,which basically meets the engineering requirements.Finally,the defect recognition results of this model on the testing set were visualized,the causes of the error of defect recognition was analyzed,and the direction of further optimization is given.

关 键 词：冷轧铜带 表面缺陷 卷积神经网络 迁移学习 识别模型 

分 类 号：TF35[冶金工程—冶金机械及自动化]