基于改进YOLOv5的轴承表面缺陷检测
Detection of Bearing Surface Defects Based on Improved YOLOv5
吴迪 1于正林 2徐式达 1周斌 2邵长顺2
作者信息
- 1. 长春理工大学机电工程学院,长春 130022
- 2. 长春理工大学机电工程学院,长春 130022;长春理工大学重庆研究院,重庆 401135
- 折叠
摘要
传统的轴承表面缺陷检测由于缺陷目标较小,错检漏检率高,检测效率低等问题,为此提出一种基于YOLOv5 网络改进的算法模型.首先,在主干网络中添加高效通道注意力机制(efficient channel attention,ECA),增强网络的特征提取能力,集中关注各种影响轴承质量的重点信息;其次,在YOLOv5 网络基础上添加小目标检测层,通过补充融合特征层和引入额外检测头,提高网络对小目标缺陷检测的精度;最后,在特征融合网络中,融入简化后的加权双向特征金字塔网络(bidirec-tional feature pyramid network,BiFPN),在不增加较多计算成本的基础上,更好地实现多尺度特征融合.在构建的深沟球轴承表面缺陷数据集上的实验结果表明,相比于原YOLOv5s模型,精确率、召回率、平均精度分别提高了5.8%、2.4%、5.3%,检测速度为71 f/s,满足工业大批量检测的要求.
Abstract
Traditional bearing surface defect detection has problems such as small defect targets,high false or missed detection rates,and low detection efficiency,therefore,an improved algorithm model based on YOLOv5 network is proposed.Firstly,add an efficient channel attention(ECA)mechanism to the backbone network to enhance the network's feature extraction ability and focus on various key information that affects bearing quality;Secondly,a small object detection layer is added to the YOLOv5 network,and the accuracy of small object de-fect detection is improved by supplementing the fusion feature layer and introducing additional detection heads;Finally,in the feature fusion network,a simplified bidirectional feature pyramid network(BiFPN)is incorporated to better achieve multi-scale feature fusion without increasing computational costs.The experimental results on the constructed deep groove ball bearing surface defect dataset show that compared to the original YOLOv5s model,the accuracy,recall,and average accuracy have been improved by 5.8%,2.4%,and 5.3%,respec-tively,with a detection speed of 71 f/s,meeting the requirements of industrial mass inspection.
关键词
YOLOv5/缺陷检测/注意力机制/小目标检测层/简化BiFPNKey words
YOLOv5/defect detection/attention mechanism/small target detection layer/simplify BiFPN引用本文复制引用
基金项目
吉林省科技发展计划(20190302069GX)
吉林省科技厅基础研究项目(202002044JC)
出版年
2024
NETL
NSTL
万方数据