Abstract
Printed circuit board is an important part of electronic industry. The detection of PCB defects could improve the reliability of PCB and electronic devices. Comparing to the traditional inspection methods, the methods based on Convolutional Neural Network have higher accuracy, faster speed, and more robustness. In this paper, a PCB defects inspection method based on EfficientDet-D1 are proposed, which uses EfficientNet-B1 as the feature extraction backbone. Besides, we use K-means clustering to predict more accurate anchor ratios. The results of our work show that the method achieves a similar mAP with other PCB defect detection methods while costing less time in detection.
Sign-in/Register to access full text options 
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.
