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30 November 2022 A real-time detection method of safety hazards in transmission lines based on YOLOv5s
Hui Li, Fan Jiang, Fei Guo, Wei Meng
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Proceedings Volume 12456, International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022); 124561C (2022) https://doi.org/10.1117/12.2659987
Event: International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022), 2022, Qingdao, China
Abstract
Transmission line inspection in most parts of the country is mainly manual inspection. This kind of inspection method is not only inefficient, but also poses a threat to the safety of inspectors. With the development of UAV and artificial intelligence technology, the power industries have begun to patrol transmission lines on a large scale using UAV running object detection algorithms. However, the existing algorithms are complex, which are not suitable for deployment on embedded devices because of low detection speed and high power consumption. In this paper, we propose a lightweight model to detect cranes, tower cranes, construction machinery, smoke, flame, and foreign matters based on You Only Look Once (YOLO) v5s, which can realize real-time high-precision object detection. Experimental results show that the mean Average Precision (mAP) of our proposed model is 72.23%, and the detection speed is also very fast. It can be concluded that the model is very suitable to run on embedded devices to detect potential safety hazards of transmission lines.
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Hui Li, Fan Jiang, Fei Guo, and Wei Meng "A real-time detection method of safety hazards in transmission lines based on YOLOv5s", Proc. SPIE 12456, International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022), 124561C (30 November 2022); https://doi.org/10.1117/12.2659987
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KEYWORDS
Safety
Detection and tracking algorithms
Data modeling
Inspection
Internet
Artificial intelligence
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