Domain Adaptation in Power Line Segmentation: A New Synthetic Dataset

Power line segmentation is a critical component of UAV intelligent inspection systems to ensure the safe and reliable operation of power grids. For challenging-to-label tasks like this, simulators can efficiently generate large amounts of labeled data. In this work, a large-scale annotated synthetic power lines dataset generated utilizing the unity game engine and the unity perception package ¹. To address domain shift between real and synthetic domain, input-level adaptation performed. Additionally, a new power line segmentation loss developed to mitigate the effects of unbalanced pixel distributions among power lines and background. Experiments demonstrate that our approach achieves state-of-the-art performance on power line segmentation task.

1. INTRODUCTION

Transmission line networks have almost expanded everywhere due to rising electrical consumption. To ensure the safety of low-altitude flights, which are seriously affected by the widely spread power network, like those performed by Unmanned Aerial Vehicles (UAVs) is necessary to recognize power lines beforehand. Particularly, low-altitude flight accidents involving electrical lines might cause significant damage to those lines, resulting in widespread power outages and affecting transmission line reliability. Traditional methods for inspecting electricity networks that have been utilized for decades include visual examination by human inspectors, and helicopter-assisted inspection. The major restriction of employing the aforementioned methods is that it is relatively slow, expensive, labor-intensive, expose inspectors to hazardous working circumstances, and is also restricted by the inspectors visual observation skills.