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.
关键词
深度学习/Yolov5/目标检测/印刷电路板/缺陷检测
Key words
deep learning/YOLOv5/object detection/printed circuit board(PCB)/defect detection
维普
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