Year: 2025
Type: article
Abstract: Purpose of research. Overhead communication lines (OCL) are an important element of the communication infrastructure, but their technical condition requires regular monitoring and inspection. Traditional inspection methods, including visual inspection by specialists, do not always allow for the efficient collection and recording of all necessary data. In order to improve the quality of OCL inspection, a method was developed for determining the tilt of a communication line pole based on images from an unmanned aerial vehicle (UAV). Methods. A combination of mathematical transformations and machine learning methods was used to solve the problem. Data processing included the use of camera parameters, object coordinates in the image, flight altitude, and UAV coordinates. Based on these data, an algorithm was developed for detecting key support points and calculating the tilt angle of the poles. Results. As a result of the experiments conducted based on the data obtained from the UAV, the accuracy of detecting key support points according to the mAP50 metric was 0.71. Within the correctly predicted support, the accuracy of detecting its top and base was 0.88 according to the F1-score metric. To determine the tilt of the VLS pillars, a formula was derived that made it possible to calculate the maximum tilt of the pillar is 24.5°, and the minimum is 0.6°. The average tilt angle of the pillars for the entire set of images is approximately 6.1°. Conclusion. The developed method allows automating the technical inspection of VLS, ensuring high accuracy in determining their key parameters. The use of UAVs and machine learning reduces time and cost, and improves the quality of data collection and analysis. The use of UAVs in combination with machine learning methods can significantly reduce time and cost, improve the quality of data collection and analysis, and reduce the risk of human error.
Authors: M. I. Zaikin, M. A. Astapova, D. M. Volkov
Institution: Russian Academy of Sciences
Language: English
Topic: Power Line Inspection Robots
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4411243746