Laser Journal, Volume. 46, Issue 3, 65(2025)

A DRM-YOLOv8n algorithm for PCB defect detection

ZHOU Jing1, HUANG Liwen1、*, TANG Xin2, and WANG Bosi3
Author Affiliations
  • 1School of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing 400000, China
  • 2KingZon (Chongqing) Package Technology Co., Ltd., Chongqing 400000, China
  • 3China Merchants Zhixing (Chongqing) Technology Co., Ltd., Chongqing 400000, China
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    References(7)

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    [10] [10] Bochkovskiy A, Wang C Y, Liao H Y M. YOLOv4: optimal speed and accuracy of object detection[J]. arXiv: 2004.10934, 2020.

    [12] [12] Shao G F, Zheng Y Y, Wu J Y, et al. An Improved YOLOv3 Network for PCB Defect Detection[C]//2021 China Automation Congress (CAC), Beijing, China, 2021: 1819-1823.

    [16] [16] Zhang T W, Zhang X L, Ke X. Quad- CPN: a novel quad feature pyramid network for SAR ship detection[J]. Remote Sensing, 2021, 13(14): 2771.

    [17] [17] Zheng Z, Wang P, Liu W, et al. Distance-IoU loss: faster and better le-arning for bounding box regression[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(7): 12993-13000.

    [18] [18] Dai J F, Qi H Z, Xiong Y W, et al. Deformable convolutional networks[C]//2017 IEEE International Confere-nce on Computer Vision. 2017: 764-77.

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    ZHOU Jing, HUANG Liwen, TANG Xin, WANG Bosi. A DRM-YOLOv8n algorithm for PCB defect detection[J]. Laser Journal, 2025, 46(3): 65

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    Paper Information

    Category:

    Received: Sep. 18, 2024

    Accepted: Jun. 12, 2025

    Published Online: Jun. 12, 2025

    The Author Email: HUANG Liwen (cqhlw@cqut.edu.cn)

    DOI:10.14016/j.cnki.jgzz.2025.03.065

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