LSNetv2: Improving weakly supervised power line detection with bipartite matching

Abstract

This paper addresses the crucial task of power line detection and localization in electrical infrastructure inspection using Unmanned Aerial Vehicles (UAVs) from weak supervision, polyline annotations. We first identify several limitations in the state-of-the-art approach LSNet. In particular, the inability of LSNet to detect line-crossings and lines in close proximity. To overcome these limitations, we propose LSNetv2, which enhances LSNet with multi-line segment detection capability facilitated via a bipartite matching loss. Additionally, we update LSNet’s regression loss in order to stabilize training by reducing the interdependence between predicted coordinates. Finally, LSNetv2 makes use of an increased receptive field to extract global information, improving overall detection performance. Through extensive evaluations on various power line detection datasets, LSNetv2 demonstrates superior performance and robustness. On the public datasets PLDU, PLDM and TTPLA, it achieved Fβ scores of 0.857, 0.875, and 0.671, respectively, while using only modified weak polyline annotation, establishing itself as an effective and efficient solution for power line detection in UAV-based electrical infrastructure inspections.