Research on PCB defect detection based on YOLOv3 network

Yongchun Wang; Tingting Shi; Yilong Zhang; Minhua Ren

doi:10.1117/12.2640745

13 October 2022 Research on PCB defect detection based on YOLOv3 network

Yongchun Wang, Tingting Shi, Yilong Zhang, Minhua Ren

Proceedings Volume 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022); 1228706 (2022) https://doi.org/10.1117/12.2640745
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 2022, Wuhan, China

Abstract

As the carrier of electronic components, PCB plays a role in supporting interconnection in electronic equipment. With the continuous in-depth development of the PCB market, PCB defects have become more and more subtle, complex and diverse. The traditional quality inspection operations are complex, lack for intelligence and have low precision, which greatly reduces the inspection efficiency. In this paper, a target detection method based on YOLOV3 network is designed for six defects of PCB board: open circuit, short circuit, excess copper, and etching gap. Through the design of the convolution block and the extraction and design of the feature layer, the backbone model of the darknet53 unique to YOLOV3 is generated and trained in three batches. And use the non-maximum suppression method to obtain the position, score and type of the defect frame, so as to achieve defect detection, and on the basis of only 2000 training data, the overall detection success rate is 79.03%.

Citation Download Citation

Yongchun Wang, Tingting Shi, Yilong Zhang, and Minhua Ren "Research on PCB defect detection based on YOLOv3 network", Proc. SPIE 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 1228706 (13 October 2022); https://doi.org/10.1117/12.2640745

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KEYWORDS

Defect detection

Target detection

Detection and tracking algorithms

Inspection

Data modeling

Convolution

Computing systems

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