Algoritmos de processamento digital de imagens para rastreamento de linhas de transmissão de energia

Abstract

High power power transmission lines are of great importance for the functioning of all sectors of society. To guarantee the reliability and availability of the energy supply, regular and occasional inspections are carried out, always manually. These inspections seek to identify anomalies in the transmission network and signs of danger to structures. Unmanned aerial vehicles, popularly known as drones, have been increasingly employed in repetitive and tedious activities, due to their low cost and increased safety, and can be applied to tracking these structures. However, the variety of environments and towers, error in location measurements, and high cost are difficult obstacles to overcome for accurate and quality inspection. The main objective of this work is to study, develop and compare real-time image processing techniques to determine movements to be performed by the drone in trajectory correction, and thus perform a tracking of transmission lines with high precision and low cost. Several solutions are found in the literature, and are located and systematically reviewed in this study. Regarding the analysis of the hardware line, controller boards and frameworks were compared, as well as ground station and simulator software, with the main focus on image processing using edge detector, deep learning and reinforcement learning. Furthermore, in relation to the training of networks, a dataset was made for identifying lines, and another for actions to be performed. Also, in order to evaluate the solution’s performance, a realistic virtual scenario based on a real route was produced. In short, a system based on a prototype equipped with two quadricopter drones equipped with only a camera and a computer, working in conjunction with a cooperative algorithm and deep learning image processing, resulted in a brief tracking in a power transmission line, simulating an inspection in a real environment