IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i17p4483-d1472743.html
   My bibliography  Save this article

Detection Method of External Damage Hazards in Transmission Line Corridors Based on YOLO-LSDW

Author

Listed:
  • Hongbo Zou

    (College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
    Hubei Provincial Key Laboratory for Operation and Control of Cascaded Hydropower Station, China Three Gorges University, Yichang 443002, China)

  • Jinlong Yang

    (College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China)

  • Jialun Sun

    (Zhangjiakou Power Supply Bureau of State Grid Jibei Electric Power Co., Ltd., Zhangjiakou 075000, China)

  • Changhua Yang

    (College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China)

  • Yuhong Luo

    (College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China)

  • Jiehao Chen

    (College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China)

Abstract

To address the frequent external damage incidents to transmission line corridors caused by construction machinery such as excavators and cranes, this paper constructs a dataset of external damage hazards in transmission line corridors and proposes a detection method based on YOLO-LSDW for these hazards. Firstly, by incorporating the concept of large separable kernel attention (LSKA), the spatial pyramid pooling layer is improved to enhance the information exchange between different feature levels, effectively reducing background interference on external damage hazard targets. Secondly, in the neck network, the traditional convolution is replaced with a ghost-shuffle convolution (GSConv) method, introducing a lightweight slim-neck feature fusion structure. This improves the extraction capability for small object features by fusing deep semantic information with shallow detail features, while also reducing the model’s computational load and parameter count. Then, the original YOLOv8 head is replaced with a dynamic head, which combines scale, spatial, and task attention mechanisms to enhance the model’s detection performance. Finally, the wise intersection over union (WIoU) loss function is adopted to optimize the model’s convergence speed and detection performance. Evaluated on the self-constructed dataset of external damage hazards in transmission line corridors, the improved algorithm shows significant improvements in key metrics, with mAP@0.5 and mAP@0.5:0.95 increasing by 3.4% and 4.6%, respectively, compared to YOLOv8s. Additionally, the model’s computational load and parameter count are reduced, and it maintains a high detection speed of 96.2 frames per second, meeting real-time detection requirements.

Suggested Citation

  • Hongbo Zou & Jinlong Yang & Jialun Sun & Changhua Yang & Yuhong Luo & Jiehao Chen, 2024. "Detection Method of External Damage Hazards in Transmission Line Corridors Based on YOLO-LSDW," Energies, MDPI, vol. 17(17), pages 1-20, September.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:17:p:4483-:d:1472743
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/17/4483/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/17/4483/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Juping Gu & Junjie Hu & Ling Jiang & Zixu Wang & Xinsong Zhang & Yiming Xu & Jianhong Zhu & Lurui Fang, 2023. "Research on Object Detection of Overhead Transmission Lines Based on Optimized YOLOv5s," Energies, MDPI, vol. 16(6), pages 1-18, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhuoya Wang & Liangliang Hao & Zemin Wang, 2024. "Short-Circuit Current Calculation of Flexible Direct Current Transmission Lines Considering Line Distribution Parameters," Energies, MDPI, vol. 17(15), pages 1-15, August.
    2. Elisavet Bellou & Ioana Pisica & Konstantinos Banitsas, 2024. "Aerial Inspection of High-Voltage Power Lines Using YOLOv8 Real-Time Object Detector," Energies, MDPI, vol. 17(11), pages 1-16, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:17:p:4483-:d:1472743. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.