Defect Detection of Lightweight Injection Molding Gear Based on VSD-YOLOv5s
An improved YOLOv5s network model based on ShuffleNetV2 backbone network was proposed after analyzing the problems of slow identification speed and low accuracy of online defect detection in in-jection molding gear processing.On the basis of YOLOv5s network model,the original C3 backbone net-work structure is replaced by lightweight ShuffleNetV2 structure,reducing the number and size of model parameters and improving the recognition speed.The SE attention mechanism is introduced,and the DIOU-NMS method is used to remove redundant boxes,reduce error suppression,and improve recognition accura-cy.The experimental results show that compared with the original model,the recognition accuracy of the improved model is increased by 0.9%,the average recognition accuracy is increased by 1.7%,and the rec-ognition speed is increased by 5 fps,which can meet the speed and accuracy requirements of online recogni-tion of injection gear surface defects.
NETL
NSTL
万方数据
维普
