  The use of Autonomous Surface Vessels (ASVs) is growing rapidly. For safe and
efficient surface auto-driving, a reliable perception system is crucial. Such
systems allow the vessels to sense their surroundings and make decisions based
on the information gathered. During the perception process, free space
segmentation is essential to distinguish the safe mission zone and segment the
operational waterways. However, ASVs face particular challenges in free space
segmentation due to nearshore reflection interference, complex water textures,
and random motion vibrations caused by the water surface conditions. To deal
with these challenges, we propose a visual temporal fusion based free space
segmentation model to utilize the previous vision information. In addition, we
also introduce a new evaluation procedure and a contour position based loss
calculation function, which are more suitable for surface free space
segmentation tasks. The proposed model and process are tested on a continuous
video segmentation dataset and achieve both high-accuracy and robust results.
The dataset is also made available along with this paper.
