Infrared Technology, Volume. 46, Issue 6, 654(2024)

PCB Defect Detection Based on PA-YOLO v5

Jinni CHEN*, Xiaohua BAI, Yunhong LI, and Gufeng TIAN
Author Affiliations
  • [in Chinese]
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    References(8)

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    [11] [11] DING R W, DAI L H, LI G P, et al. TDD-net: a tiny defect detection network for printed circuit boards[J]. CAAI Transactions on Intelligence Technology, 2019, 4(2): 110-116.

    [13] [13] XIA B, CAO J, WANG C. SSIM-NET: Real-time PCB defect detection based on SSIM and MobileNet-V3[C]//2019 2nd World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM) of IEEE, 2019: 756-759.

    [16] [16] CHEN R, ZHAN Z, HU X, et al. Printed circuit board defect detection based on the multi-attentive faster RCNN under noise interference[J].Chin. J. Sci. Instrum, 2021, 42(12): 167-174.

    [17] [17] KANG L, GE Y, HUANG H, et al. Research on PCB defect detection based on SSD[C]//4th International Conference on Civil Aviation Safety and Information Technology (ICCASIT) of IEEE, 2022: 1315-1319.

    [18] [18] WANG Z, CHEN W, LI T, et al. Improved YOLOv3 detection method for PCB plug-in solder joint defects based on ordered probability density weighting and attention mechanism[J]. AI Communications, 2022, 35(3):171-186.

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    CHEN Jinni, BAI Xiaohua, LI Yunhong, TIAN Gufeng. PCB Defect Detection Based on PA-YOLO v5[J]. Infrared Technology, 2024, 46(6): 654

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    Paper Information

    Category:

    Received: Sep. 13, 2023

    Accepted: --

    Published Online: Sep. 20, 2024

    The Author Email: Jinni CHEN (396161675@qq.com)

    DOI:

    CSTR:32186.14.

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