Infrared Technology, Volume. 47, Issue 1, 72(2025)

IR Image Classification and Detection of Power Equipment Based on CBAM Improvement

Jia CHEN1...2, Chengbo YU1,2,*, Shibing WANG2,3, Qichao JIANG1,2, Xin HE1,2, and Wei ZHANG12 |Show fewer author(s)
Author Affiliations
  • 1School of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing 400054, China
  • 2Chongqing Energy Internet Engineering Technology Research Center, Chongqing 400054, China
  • 3State Grid Chongqing Electric Power Company Shinan Power Supply Branch, Chongqing 401336, China
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    References(15)

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    [7] [7] CONG S, PU H, WANG X, et al. Application of improved YOLOv5 in infrared image recognition of electrical equipment[C]//2023 8th Asia Conference on Power and Electrical Engineering (ACPEE), 2023: 1836-1843.

    [8] [8] LI J, XU Y, NIE K, et al. PEDNet: a lightweight detection network of power equipment in infrared image based on YOLOv4-Tiny[J]. IEEE Transactions on Instrumentation and Measurement, 2023, 72: 1-12.

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    [11] [11] JIANG P, Ergu D, LIU F, et al. A review of Yolo algorithm developments[J]. Procedia Computer Science, 2022, 199: 1066-1073.

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    [16] [16] Bochkovskiy A, WANG C Y, LIAO H Y M. YOLOv4: Optimal speed and accuracy of object detection[J]. arXiv preprint arXiv: 2004.10934, 2020.

    [17] [17] Olorunshola O E, Irhebhude M E, Evwiekpaefe A E. A comparative study of YOLOv5 and YOLOv7 object detection algorithms[J]. Journal of Computing and Social Informatics, 2023, 2(1): 1-12.

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    CHEN Jia, YU Chengbo, WANG Shibing, JIANG Qichao, HE Xin, ZHANG Wei. IR Image Classification and Detection of Power Equipment Based on CBAM Improvement[J]. Infrared Technology, 2025, 47(1): 72

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    Paper Information

    Category:

    Received: Sep. 6, 2023

    Accepted: Feb. 18, 2025

    Published Online: Feb. 18, 2025

    The Author Email: YU Chengbo (yuchengbo@cqut.edu.cn)

    DOI:

    CSTR:32186.14.

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