Detection of Bearing Surface Defects Based on Improved YOLOv5
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.
NETL
NSTL
万方数据
