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

Hongbo Zou, Jinlong Yang (), Jialun Sun, Changhua Yang, Yuhong Luo and Jiehao Chen
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Hongbo Zou: College of Electrical Engineering and New Energy, 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

Energies, 2024, vol. 17, issue 17, 1-20

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.

Keywords: transmission line corridor; prevention of external damage; object detection; attention mechanism; loss function (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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