Intelligent identification of external damage factors around transmission lines based on satellite remote sensing images

Long Li; Ping Lou; Liangliang Cheng; Haoyan Li; Jun Lai; Keliang Gan

doi:10.1117/12.2679618

16 June 2023 Intelligent identification of external damage factors around transmission lines based on satellite remote sensing images

Long Li, Ping Lou, Liangliang Cheng, Haoyan Li, Jun Lai, Keliang Gan

Author Affiliations +

Proceedings Volume 12702, International Conference on Intelligent Systems, Communications, and Computer Networks (ISCCN 2023); 127021X (2023) https://doi.org/10.1117/12.2679618
Event: International Conference on Intelligent Systems, Communications, and Computer Networks (ISCCN 2023), 2023, Changsha, China

Abstract

As an important part of the power grid, the safe and stable operation of the transmission line is related to the entire national economic construction and people's life. In order to identify the external damage factors around the transmission line in a timely and reasonable manner, this paper uses the existing satellite remote sensing image technology to detect and analyze. In this paper, the bulldozers and excavators of large construction machinery in the influence factors of transmission lines are analyzed and identified by means of 50% cross validation and experimental data comparison. The experimental data shows that the missed alarm rate is less than 2%. Therefore, the improved target detection algorithm proposed in this paper can effectively identify multiple targets in complex background, and then apply it to the detection of external damage factors of transmission lines.

Citation Download Citation

Long Li, Ping Lou, Liangliang Cheng, Haoyan Li, Jun Lai, and Keliang Gan "Intelligent identification of external damage factors around transmission lines based on satellite remote sensing images", Proc. SPIE 12702, International Conference on Intelligent Systems, Communications, and Computer Networks (ISCCN 2023), 127021X (16 June 2023); https://doi.org/10.1117/12.2679618

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