电子科技2025，Vol.38Issue(6)：82-88,7.DOI:10.16180/j.cnki.issn1007-7820.2025.06.012
基于改进YOLOv5的钢板表面缺陷检测
Steel Plate Surface Defect Detection Based on Improved YOLOv5
摘要
Abstract
To meet the requirements of steel plate surface defect detection in most industrial scenarios,a steel plate surface defect detection algorithm based on improved YOLOv5(You Only Look Once version 5)is proposed to solve the problems such as low detection accuracy of steel plate surface defects and failure to detect small target de-fects.On the basis of YOLOv5,the CBAM(Convolution Block Attention Module)is embedded into the backbone network to improve network detection accuracy.The context enhancement module is added to improve the detection performance of small targets.The NWD(Normalized Wasserstein Distance)metric is used to replace the original IoU(Intersection over Union)metric in YOLOv5,making the network more accurate in identifying crack defects.Experi-mental results show that the average detection accuracy of the proposed steel plate surface defect detection algorithm for six types of defects,including crack,inclusion,plaque,pitting,pressed iron oxide,scratch,reaches 88.9％,the frame rate reaches 110.4 frame·s-1,and the accuracy of small target crack reaches 75％.关键词
钢板表面缺陷检测/YOLOv5/注意力模块/上下文增强模块/小目标/位置偏差/NWD度量/IoU度量Key words
steel plate surface defect detection/YOLOv5/attention module/context enhancement module/small goals/position deviation/NWD measurement/IoU measurement分类
信息技术与安全科学引用本文复制引用
沈庭铅,鲁玉军,辛昊,吴涵超,汪仕男..基于改进YOLOv5的钢板表面缺陷检测[J].电子科技,2025,38(6):82-88,7.基金项目
浙江省重点研发项目(2022C01242) （2022C01242）
浙江理工大学龙港研究院项目(LGYJY2021004)Key R&D Program of Zhejiang(2022C01242) （LGYJY2021004）
Zhejiang Sci-Tech University Longgang Research Institute Project(LGYJY2021004) （LGYJY2021004）
