Monocular vision navigation for aerial surveillance of power lines based on Deep Neural Networks and Hough transform

Abstract

Surveillance of overhead power line installations can be conveniently addressed using unmanned aerial vehicles (UAV). UAV are robotic platforms able to perform sophisticated tasks such as autonomous flight based on visual information. In this paper, we propose a novel solution to the problem of following a power line autonomously based on monocular vision. The method uses Deep Neural Networks (DNN) and the Hough transform to successfully discern power line images from environmental information, which is an essential result to accomplish fully autonomous vision-based navigation. A simulated navigation test demonstrates the efficiency of the proposed method, in the special condition of following right-angled changes of direction, which is a known restriction in many navigation methods reported in literature. The design of the proposed method is modular and can be incorporated in navigation strategies for automatic surveillance applications.