Computer vision based tilt detection of transmission line towers under high wind speed impact

Shiyao Zhang; Lu Zhang; Bo Zhang; Yige Zhang

doi:10.1117/12.3029322

22 May 2024 Computer vision based tilt detection of transmission line towers under high wind speed impact

Shiyao Zhang, Lu Zhang, Bo Zhang, Yige Zhang

Proceedings Volume 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023); 1317620 (2024) https://doi.org/10.1117/12.3029322
Event: Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 2023, Hangzhou, China

Abstract

Transmission lines are an important component of the power system, and their stable operation is crucial for ensuring power supply safety. In a strong wind environment, it may cause the tower to tilt, causing transmission line faults, and thus affecting the normal electricity consumption of users. Therefore, a computer vision based method for detecting the inclination of transmission line towers under high wind speed impact is proposed. Preprocess the transmission line tower image and use the random forest algorithm to extract the features of the transmission line tower image. On this basis, based on the Region Proposal Network (RPN) in computer vision, the tilt detection of transmission line towers under high wind speed impact is achieved. The experimental results show that the method proposed in this paper has a good detection effect on the tilt of transmission line towers, and can accurately detect the tilt angle of transmission line towers. When the maximum wind speed is reached, the tilt angle of the tower is the maximum.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shiyao Zhang, Lu Zhang, Bo Zhang, and Yige Zhang "Computer vision based tilt detection of transmission line towers under high wind speed impact", Proc. SPIE 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 1317620 (22 May 2024); https://doi.org/10.1117/12.3029322

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