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

Naeem Ayoub, Peter Schneider-Kamp

2020

Abstract

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.

Paper Citation

in Harvard Style

Ayoub N. and Schneider-Kamp P. (2020). Real-time On-board Detection of Components and Faults in an Autonomous UAV System for Power Line Inspection.In Proceedings of the 1st International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA, ISBN 978-989-758-441-1, pages 68-75. DOI: 10.5220/0009826700680075

in Bibtex Style

@conference{delta20,
author={Naeem Ayoub and Peter Schneider-Kamp},
title={Real-time On-board Detection of Components and Faults in an Autonomous UAV System for Power Line Inspection},
booktitle={Proceedings of the 1st International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA,},
year={2020},
pages={68-75},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009826700680075},
isbn={978-989-758-441-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA,
TI - Real-time On-board Detection of Components and Faults in an Autonomous UAV System for Power Line Inspection
SN - 978-989-758-441-1
AU - Ayoub N.
AU - Schneider-Kamp P.
PY - 2020
SP - 68
EP - 75
DO - 10.5220/0009826700680075