Laser & Optoelectronics Progress, Volume. 62, Issue 6, 0615007(2025)

Lightweight Model for Irregular Wear Detection in Power Operations

Guangle Wang*, Yatong Zhou, and Zhao Wang
Author Affiliations
  • School of Electronic and Information Engineering, Hebei University of Technology, Tianjin 300401, China
  • show less
    References(20)

    [3] Wu Q E, Wan G L, Zhou L Tet al. Construction of the lightweight YOLOv3 helmet detection network model[J]. Computer Simulation, 40, 293-299(2023).

    [4] Xie G B, Xie J H, Lin Z Yet al. Detection algorithm for reflective clothing and safety helmets based on CT-YOLOX[J]. Foreign Electronic Measurement Technology, 42, 51-58(2023).

    [8] Cai Y M, Yang W. LKA_Unet: applying spatial attention mechanism based on large convolutional kernel to Unet[J]. Computer Era, 81-84(2023).

    [15] Jing F K, Ren H G, Li S. Small-target traffic sign detection based on multiscale feature fusion[J]. Laser & Optoelectronics Progress, 61, 1237002(2024).

    [16] Dai S, Sun X M, Zhang J Met al. Multiscale convolutional neural network-based lithology classification method for multisource data fusion[J]. Laser & Optoelectronics Progress, 61, 1437005(2024).

    Tools

    Get Citation

    Copy Citation Text

    Guangle Wang, Yatong Zhou, Zhao Wang. Lightweight Model for Irregular Wear Detection in Power Operations[J]. Laser & Optoelectronics Progress, 2025, 62(6): 0615007

    Download Citation

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

    Category: Machine Vision

    Received: Aug. 1, 2024

    Accepted: Aug. 28, 2024

    Published Online: Mar. 13, 2025

    The Author Email:

    DOI:10.3788/LOP241782

    CSTR:32186.14.LOP241782

    Topics