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.

翻译：我们提出了一种新方法，通过利用纹理和运动线索作为监督信号，对已有RGB训练的水域分割网络进行在线自监督适应，使其能够处理目标域航空热红外图像。这一热红外能力使当前运行于近岸环境中的自主空中机器人能够在夜间执行视觉导航、水深测量及水流追踪等任务。传统监督和无监督方法因近岸热红外数据稀缺且难以获取而受限，本方法有效克服了这一问题。工作中，我们首次构建了航空近岸热红外数据集，证明所提方法优于在有限目标域热红外数据上训练的完全监督分割模型，并在Nvidia Jetson嵌入式计算平台上展示了实时处理能力。相关代码与数据集将开源至：https://github.com/connorlee77/uav-thermal-water-segmentation。