Research on PCB solder joint defect detection method based on machine vision

Yuanpei Chang; Ying Xue; Yu Zhang; Jiajun Ma; Guangjie Li; Dandan Wu; Qiang Zhan; Jiancun Zuo

doi:10.1117/12.2680409

27 June 2023 Research on PCB solder joint defect detection method based on machine vision

Yuanpei Chang, Ying Xue, Yu Zhang, Jiajun Ma, Guangjie Li, Dandan Wu, Qiang Zhan, Jiancun Zuo

Proceedings Volume 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022); 127050M (2023) https://doi.org/10.1117/12.2680409
Event: Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 2022, Nanjing, China

Abstract

Printed circuit boards (PCBs) are an essential component of electronic products, and detecting solder joint defects is critical in the PCB production process. Machine vision technology allows detection with high efficiency and cost-effectiveness. Therefore, this paper summarizes the basic principles of image processing-based and machine learning-based methods for defect detection and compares the advantages and disadvantages of both methods with relevant performance evaluation indicators. Finally, this paper contains a summary and an outlook.

Citation Download Citation

Yuanpei Chang, Ying Xue, Yu Zhang, Jiajun Ma, Guangjie Li, Dandan Wu, Qiang Zhan, and Jiancun Zuo "Research on PCB solder joint defect detection method based on machine vision", Proc. SPIE 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 127050M (27 June 2023); https://doi.org/10.1117/12.2680409

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