Abstract
In the production process of printed circuit boards (PCBs), defect detection is a very important part of the process. Due to the complexity of printed circuit board structure, traditional detection methods have problems such as poor detection accuracy and low detection speed. In this paper, a deep learning-based target detection method is proposed for PCB defect detection. The baseline model of the method is PP-YOLOv2, and Resnet50 is used as the backbone network for feature extraction and mish activation function with better smoothing. We trained the model on the COCO pre-trained model. Finally, tests were performed on publicly available PCB datasets, and the experimental results show that the method has high detection accuracy and fast detection speed, which is more suitable for production use compared with other PCB defect detection methods.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
