To reduce the pressure of traditional manual inspection and improve detection efficiency,a method based on deep learning was used to solve the defect detection task in printed circuit board(PCB)manufacturing.The YOLOv5s object detection algorithm was adopted to quickly and efficiently detect defects in PCB.The sample data of PCB was collected with various defects,and the data augmentation was used to expand the dataset and label them separately;a Python deep learning environment was configured in Pycharm and the PCB defect detection tasks were completed by using the official YOLOv5s weight as a pre trained model.The results show that the average detection accuracy of the optimal model after training is 96.3％;after testing,it can achieve a real-time detection speed of about 11 frames per second on the Intel i5-8265U CPU platform.The defect detection of PCB based on YOLOv5s has good detection performance and can replace traditional manual detection,thereby reducing the pressure of manpower and financial resources,which has high practical value.
deep learningYOLOv5object detectionprinted circuit board(PCB)defect detection
万方数据
维普
