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.

翻译：自主水面船舶的应用正在迅速增长。为了实现安全高效的水面自主驾驶，可靠的感知系统至关重要。这类系统使船舶能够感知周围环境并根据收集到的信息做出决策。在感知过程中，自由空间分割对于区分安全任务区域和划分可航行水道至关重要。然而，由于近岸反射干扰、复杂的水面纹理以及水面条件引起的随机运动振动，自主水面船舶在自由空间分割方面面临特殊挑战。为应对这些挑战，我们提出了一种基于视觉时间融合的自由空间分割模型，利用先前的视觉信息。此外，我们还引入了一种新的评估流程和基于轮廓位置的损失计算函数，这些方法更适用于水面自由空间分割任务。所提出的模型和流程在连续视频分割数据集上进行了测试，取得了高精度且鲁棒的结果。该数据集也随本文一同发布。