Laser & Optoelectronics Progress, Volume. 62, Issue 2, 0237013(2025)

Detection and Classification of Surface Defects on Solar Cell Panels Based on Deep Learning

Junbo Tu*, Jialin Zeng, Yuexin Tang, Chenxi Wu, and Xiaoyu Liu
Author Affiliations
  • School of Mechanical Engineering, Sichuan University, Chengdu 610065, Sichuan , China
  • show less
    References(24)

    [2] Bai Y. Photovoltaic industry has become a banner of high end manufacturing in China[N]. China Electric Power News.

    [3] Zhou D Y. Design of defect detection system based on electroluminescent solar panel[D](2022).

    [14] Li T, Sun Y. Defect detection of solar panels based on improved lightweight YOLOv5[J]. Modular Machine Tool & Automatic Manufacturing Technique, 95-99, 106(2023).

    [15] Wang X Y, Jiang S X. Overview of chip defect detection[J]. Modern Manufacturing Technology and Equipment, 58, 94-98(2022).

    [20] Liu S Y. Solar cell defect detection a lgorithm based on improved YOLOv5s[D](2023).

    [24] Xu Y H, Tang H Q, Xiao S P. Improved YOLOv5s-based detection of foreign objects in transmission lines[J]. Electric Engineering, 54-57, 62(2023).

    Tools

    Get Citation

    Copy Citation Text

    Junbo Tu, Jialin Zeng, Yuexin Tang, Chenxi Wu, Xiaoyu Liu. Detection and Classification of Surface Defects on Solar Cell Panels Based on Deep Learning[J]. Laser & Optoelectronics Progress, 2025, 62(2): 0237013

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: Digital Image Processing

    Received: Apr. 15, 2024

    Accepted: Jun. 6, 2024

    Published Online: Dec. 17, 2024

    The Author Email:

    DOI:10.3788/LOP241100

    CSTR:32186.14.LOP241100

    Topics