Defect detection method of wire harness terminal based on improved YOLOv3
A deep-learning based automotive wiring harnesses defect detection system is developed.The Pr-YOLOv3 algorithm based on improved YOLOv3 is used to detect defects in wiring harness terminal connectors,and the backbone extraction network is replaced with ResNet50,which improves the feature extraction capability and reduces the number of parameters.Drawing on the advantages in multi-scale prediction methods and feature fusion,the backbone extraction network is interfaced with the FPN feature pyramid,which enriches the feature expression ability.Trained with the improved YOLOv3 model,the accuracy can reach 98.61%and the Recall index can reach 98.6%.
NETL
NSTL
万方数据
维普
