PCB Bare Board Defect Detection Based on Lightweight Improved YOLOv5s
Improving the detection ability of bare board defects in printed circuit board is of great significance for intelligent manufacturing production.This article proposes a lightweight improved YOLOv5s PCB bare board defect detection algorithm.This method uses a lightweight Mobilenetv3 feature extraction module to replace the backbone structure of YOLOv5s,greatly reduced the size of the model and the number of floating-point operations,fully saved hardware resources.In response to the complex distribution of PCB bare board defect features,a coordinate attention module and additional 160 X 160 size feature detection head has been introduced to improve the accuracy of PCB bare board defect detection.Through experiment,it was found that compared with the original YOLOv5s model,the size of improved network model is only 30.5%of the original network model,and floating-point operation is only 29.8%of the original network model.Moreover,its accuracy in detecting PCB bare board defects reaches 98.15%,and its recall rate reaches 98.95%.Both mAP0.5 and mAP0.5∶0.95have reached 98.35%and 65.79%.The improved network model helps to provide technical support for the development of PCB bare board defect detection technology in embedded and other hardware resource limited devices.