DIFFERENT COLOR SPACES IN DEEP LEARNING-BASED WATER SEGMENTATION FOR AUTONOMOUS MARINE OPERATIONS

For autonomous unmanned surface vehicles (USV) operations, it is important to be able to observe the surroundings using visual information. Water segmentation is a task where the water surface is recognized and separated from everything else. The algorithm performing the segmentation must be robust, because safety is the most important feature of autonomous USVs. This is especially challenging in many USV applications, where the rapidly changing weather and lighting conditions can cause significant distribution shifts. In this study, we analyze the robustness of different color spaces (e.g., RGB and HSV) for water segmentation and consider how to use different color channels in training and testing to maximize the robustness. We evaluate the segmentation performance on a challenging completely unseen test dataset, recorded in vastly different conditions and with different equipment.