面向热轧带钢表面缺陷检测的YOLOv5算法优化分析
The optimization of YOLOv5 algorithm for detecting surface defects on hot rolled strips
马冬梅 1朱佳浩1
作者信息
- 1. 西北师范大学物理与电子工程学院,甘肃 兰州 730070
- 折叠
摘要
单阶段目标检测网络YOLOv5 在处理热轧带钢表面缺陷的特征提取与感受特征融合时存在一定不足.文章提出一种适用于热轧带钢表面缺陷检测的优化YOLOv5 算法,该算法通过IOU-K-means++算法调整Anchor聚类锚框设定,并增加Dynamic Head目标检测头,引入通道注意力机制(C3_CA),同时结合Hard Swish激活函数与WIoU_Loss边界框回归函数,有效提高热轧带钢表面缺陷检测的综合精度.由 NEU-DET数据集测试结果表明,相较于单阶段YOLOv5 算法融合结果,优化后的YOLOv5 网络模型的均值平均精度(mAP)可提高至 75.7%,且网络约束率可有效提升 6.1%.上述优化YOLOv5 算法对热轧带钢表面缺陷位置勘定、分类指向与影响评估具有有益参考,同时也为金属表面的高精度筛检提供重要支持.
Abstract
The single-stage target detection network YOLOv5 has certain deficiencies when dealing with feature extraction and sensory feature fusion for surface defects on hot-rolled steel.This paper proposes an optimized YOLOv5 algorithm for surface defect detection of hot rolled strip steel,which adjusts the anchor clustering anchor frame setting by the IOU-K-means++algorithm,increases the dynamic head target detection head,introduces the channel attention mechanism(C3_CA),and combines the hard swish activation function with the WioU_loss bounding box regression function,effectively improving the comprehensive accuracy of hot rolled strip steel surface defect detection.Test results from the NEU-DET dataset show that compared to the single-stage YOLOv5 algorithm fusion results,the mean average precision(mAP)of the optimized YOLOv5 network model can be improved up to 75.7%,and the network constraint rate can be effectively improved by 6.1%.The above optimized YOLOv5 algorithm is a useful reference for hot rolled strip steel surface defect location surveys,classification points,and impact assessments and provides important support for high-precision screening of metal surfaces.
关键词
热轧带钢表面缺陷/YOLOv5/IOU-K-means++/Dynamic/Head/注意力机制Key words
hot rolled steel surface defects/YOLOv5/IOU-K-means++/dynamic head/attention mechanism引用本文复制引用
出版年
2024
NETL
NSTL
万方数据