Work

Domain Adaptation in Power Line Segmentation: A New Synthetic Dataset

Year: 2023

Type: conference-paper

Abstract: 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 package1. 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.

Source: Proceedings - International Conference on Image Processing

DOI: 10.1109/icip49359.2023.10222078

Authors: Georgios Kalitsios, Vasileios Mygdalis, Ioannis Pitas

Institution: Aristotle University of Thessaloniki

Language: English

Topic: Power Line Inspection Robots

Subfield: Mechanical Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

Funder: Horizon 2020 Framework Programme