Advancing Deep Learning for Automatic Autonomous Vision-based Power Line Inspection

Abstract

Electricity is fundamental to the ability to function of almost all modern-day societies. To maintain the reliability, availability, and sustainability of electricity supply, electric utilities are usually required to perform visual inspections on their electrical grids regularly. These inspections have been typically carried out using a combination of airborne surveys via low-flying helicopters and field surveys via foot patrol and tower climb. The primary purpose of these visual inspections is to plan for necessary repair or replacement works before any major damage that may lead to a power outage. These traditional inspection methods are not only slow and expensive but also potentially dangerous. In the past few years, numerous efforts have been made to automate these visual inspections. However, due to the high accuracy requirements of the task and its unique challenges, automatic vision-based inspection has not yet been widely adopted in this field.

In this dissertation, we exploit recent advances in Deep Learning (DL), especially deep Convolutional Neural Networks (CNNs), and Unmanned Aerial Vehicle (UAV) technologies for facilitating automatic autonomous vision-based power line inspection. Specifically, we propose a novel automatic autonomous vision-based power line inspection concept that uses UAV inspection as the main inspection method, optical images as the primary data source, and deep learning as the backbone of data analysis. To facilitate the implementation of the proposed concept, we first conduct an extensive literature review on automatic vision-based power line inspection. Based on that, we identify the possibilities and challenges of DL vision-based UAV inspection. We then propose approaches for addressing the identified challenges, for advancing deep learning, and for paving the way for realizing fully automatic autonomous vision-based power line inspection.