Real-time On-board Detection of Components and Faults in an Autonomous UAV System for Power Line Inspection.

The inspection of power line components is periodically conducted by specialized companies to identify possible faults and assess the state of the critical infrastructure. UAV-systems represent an emerging technological alternative in this field, with the promise of safer, more efficient, and less costly inspections. In the Drones4Energy project, we work toward a vision-based beyond-visual-line-of-sight (BVLOS) power line inspection architecture for automatically and autonomously detecting components and faults in real-time on board of the UAV. In this paper, we present the first step towards the vision system of this architecture. We train Deep Neural Networks (DNNs) and tune them for reliability under different conditions such as variations in camera used, lighting, angles, and background. For the purpose of real-time on-board implementation of the architecture, experimental evaluations and comparisons are performed on different hardware such as Raspberry Pi 4, Nvidia Jetson Nano, Nvidia Jetson TX2, and Nvidia Jetson AGX Xavier. The use of such Single Board Devices (SBDs) is an integral part of the design of the proposed power line inspection architecture. Our experimental results demonstrate that the proposed approach can be effective and efficient for fully-automatic real-time on-board visual power line inspection.