Real-Time On-Board Deep Learning Fault Detection for Autonomous UAV Inspections

Inspection of high-voltage power lines using unmanned aerial vehicles is an emerging technological alternative to traditional methods. In the Drones4Energy project, we work toward building an autonomous vision-based beyond-visual-line-of-sight (BVLOS) power line inspection system. In this paper, we present a deep learning-based autonomous vision system to detect faults in power line components. We trained a YOLOv4-tiny architecture-based deep neural network, as it showed prominent results for detecting components with high accuracy. For running such deep learning models in a real-time environment, different single-board devices such as the Raspberry Pi 4, Nvidia Jetson Nano, Nvidia Jetson TX2, and Nvidia Jetson AGX Xavier were used for the experimental evaluation. Our experimental results demonstrated that the proposed approach can be effective and efficient for fully automatic real-time on-board visual power line inspection.

Automated summary

The use of deep learning-based autonomous drone vision systems shows promising results in detecting faults in power line components, providing an effective solution for real-time on-board power line inspection. Various single-board devices were utilized for experimental evaluation in running deep learning models.