Author(s)

Yao Xiao ¹, Jingnuo Wu ¹

Affiliation(s)

¹ State Grid Tong Hua Power Supply Company, State Grid Jilin Electric Power Company Limited, Tonghua, Jilin, China

Corresponding Author

Yao Xiao

ABSTRACT

In the era of smart grids, the maintenance and safety inspection of overhead transmission lines increasingly rely on efficient inspection methods. Visual inspection is one of the key links, which combines modern information technology, drones, machine vision, and artificial intelligence (AI) algorithms to improve inspection efficiency and accuracy. Although existing technologies have significantly improved the efficiency and safety of inspections, there are still significant issues in dealing with extreme environments such as storms and high-altitude areas, ensuring stable drone flight, image processing speed, and accuracy. This article believes that continuous technological progress can make visual inspection of transmission lines more intelligent and reliable. Therefore, it intends to conduct an in-depth analysis of the visualization techniques and algorithms for line inspection. This article mainly applies experimental comparison method and analytic hierarchy process to analyze the visual inspection of overhead transmission lines. The experimental results show that the Mask R-CNN (Region-based Convolutional Neural Network) algorithm has the highest accuracy (97.89%) and safety (99.123%) in line recognition. 

KEYWORDS

Transmission Lines, Visualization Technology, Inspection Images, Algorithm Analysis, Target Detection

CITE THIS PAPER

Yao Xiao, Jingnuo Wu, Exploration of Visual Inspection and Algorithm Technology for Aerial Transmission Lines. Automation and Machine Learning (2024) Vol. 5: 145-153. DOI: http://dx.doi.org/10.23977/autml.2024.050119.

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