Revolutionizing power line inspection: automated data acquisition through autonomous UAVs in simulated environment

Abstract

Due to the growing need for electricity, the effective inspection of the power lines is becoming an important matter. In this paper, the author presents the inspection of power or transmission line with autonomous automatic UAVs (Unmanned Aerial Vehicles). For the comprehensive inspection of power lines and its different components, such as (cross arms, cracks in poles, rot damage, and insulator burn), It is needed to inspect from every side of the elements and the masts. So, the angle and speed of the drone are much more important to take images while moving around the poles. The simulator used for the experiments, including deep learning models, acts as a vital source of data analysis. At the same time, pictures are used as the primary data source. Through the Deep learning method, a suggestion of action generated for the movement around the masts. The use of a simulator is a quick, accurate, and inexpensive solution, with less real/world factors affecting the inspection process, such as weather, time, and cost of using many different resources. This study presents experiments with lightweight deep learning models through developing the prototype of vision based unmanned aerial vehicle to inspect the power line in a simulated environment. It focuses on the large demand of power companies to inspect the power line autonomously with the influence of deep learning. Finally, several deep learning models are compared when inspection along the power lines. The model shows satisfactory results in the testing path. The model trained by MobileNetV2 performs best among all other models.