Abstract
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.
Highlights
Unmanned aerial vehicles (UAVs), commonly known as drones, are aircraft platforms that fly without direct human pilot interaction
Based on recent advances in drone technology, in this paper, we addressed a number of challenges regarding traditional power line inspection methods and develop autonomous algorithms by utilizing deep learning (DL) technology
We proposed and evaluated the use of autonomous DL algorithms running in real time on-board UAVs with the purpose of visual power line inspection
Summary
Unmanned aerial vehicles (UAVs), commonly known as drones, are aircraft platforms that fly without direct human pilot interaction. These aircraft are operated indirectly by a pilot from the ground control station or by using autonomous operating systems and sensors mounted on it [1]. These types of inspection are rather expensive and slow, with some of them even being outright dangerous To overcome these issues, research projects at some companies, as well as in the academic world are focusing on the development of artificial intelligence (AI)-based autonomous power line inspection and fault detection methods. Based on recent advances in drone technology, in this paper, we addressed a number of challenges regarding traditional power line inspection methods and develop autonomous algorithms by utilizing deep learning (DL) technology.
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