Review of autonomous inspection technology for power lines using UAVs

Abstract

With the continuous expansion of the scale of the power grid and the gradual maturity of the application of drones, drones with the advantages of flexibility and convenience can overcome terrain constraints while reducing manpower and safety risks and can be well qualified for the power inspection work under the large-scale development of the power grid and improve the inspection efficiency. However, the massive amount of data collected by UAV power line inspection relies on the human judgment through the naked eyes of staff, and the recognition efficiency is very low and the accuracy rate is subjectively influenced. In recent years, the gradual introduction of traditional machine learning and deep learning has led to significant improvements in inspection efficiency and accuracy. The autonomous obstacle avoidance technology based on the inherent electromagnetic field characteristics of power lines makes it possible for UAVs to cruise autonomously. Based on a systematic summary of the achieved results, this paper briefly reviews the overall development of both power component detection and autonomous cruise in UAV power line inspection. At the same time, the current problems and future research trends are described, expecting to provide references for further research in this field.