Research on surface defect detection of SMD inductors based on improved YOLOv5

CHEN Jianchun; QIAO Jian; ZHU Ziwei; WANG Gongwei

Abstract：In order to achieve fast and accurate detection of surface defects of SMD inductors and break through the current technical problems of slow speed and low accuracy of surface defect detection of SMD inductors,this paper introduced SE attention module and bidirectional feature pyramid network(BiFPN)model based on YOLOv5 algorithm,proposed a feature extraction network structure based on attention mechanism,and assigned corresponding weight information to different feature channels,so that they can be quickly transferred in feature fusion.The detection accuracy of the surface defect model of SMD inductor is further improved.Considering that the defect type of the SMD inductors cannot be detected efficiently when extracting the network,a bidirectional feature pyramid structure is designed to enhance the ability of the model to express the feature information of different scales.The ablation experiment of SE attention mechanism and BiFPN network,as well as the contrast experiment of target detection algorithm,are completed by using the SMD inductors surface defect detection data set.The results show that the mean average precision(mAP)of the improved model proposed in this paper reached 97.12%,which is 5.87%higher than the original YOLOv5 algorithm,and the detection speed reached 40.47 FPS,which can meet the real-time and accuracy requirements of surface defect detection of SMD inductors.