Online Self-Supervised Thermal Water Segmentation for Aerial Vehicles
Connor Lee Jonathan Gustafsson Frennert Lu Gan Matthew Anderson Soon-Jo Chung
We present a new method to adapt an RGB-trained water segmentation network to
target-domain aerial thermal imagery using online self-supervision by
leveraging texture and motion cues as supervisory signals. This new thermal
capability enables current autonomous aerial robots operating in near-shore
environments to perform tasks such as visual navigation, bathymetry, and flow
tracking at night. Our method overcomes the problem of scarce and
difficult-to-obtain near-shore thermal data that prevents the application of
conventional supervised and unsupervised methods. In this work, we curate the
first aerial thermal near-shore dataset, show that our approach outperforms
fully-supervised segmentation models trained on limited target-domain thermal
data, and demonstrate real-time capabilities onboard an Nvidia Jetson embedded
computing platform. Code and datasets used in this work will be available at:
https://github.com/connorlee77/uav-thermal-water-segmentation.