Mr. Kegang Wei Profile

Kegang Wei

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Proceedings Article | 18 April 2022 Paper

Operating environment sensing of transmission tower based on deep learning

Jun Ma, Zhiwei Wang, Kegang Wei, Jinyu Gao, Xuefeng Zhao

Proceedings Volume 12047, 120471D (2022) https://doi.org/10.1117/12.2613229

KEYWORDS: Environmental sensing, Detection and tracking algorithms, Inspection, Video surveillance, Video, Machine vision, Image transmission, Environmental monitoring, Data modeling, Unmanned aerial vehicles

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The large-span transmission tower line system is an important lifeline power engineering facility, because of long-term exposure to the natural environment, it needs to face a variety of different complex environment, especially some complex environment, such as traffic, vehicles influence and lush vegetation influence and so on. Operation maintenance work is an important part for the structural health assessment of transmission tower. The routine management and maintenance work mainly relies on engineers and technicians with practical experience to carry out visual inspection and fill in the questionnaire. However, human based visual inspection is an arduous and time-consuming task, and its detection results largely depend on the subjective judgment of human inspectors, as the same time the workers working at height are very dangerous. For environmental changes such as personnel, vehicles and illegal planting, some transmission towers are in remote locations, and the staff cannot find them in time. Aiming at the deficiency of artificial vision detection method, the research on the environmental perception technology of transmission tower based on deep learning is proposed. A large amount of data collected is trained, verified and tested with deep learning algorithm. In order to solve the problem of transmission towers exposed to complex environmental influence, an appropriate model was established based on deep learning algorithm, and the image was used to verify and test. The trained model was tested on some new images that were not used in the training and verification process. Experimental results show that this method can accurately identify the complex environmental objects.

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