Real-time and high-precision situational awareness technology is critical for autonomous navigation of unmanned surface vehicles (USVs). In particular, robust and fast obstacle semantic segmentation methods are essential. However, distinguishing between the sea and the sky is challenging due to the differences between port and maritime environments. In this study, we built a dataset that captured perspectives from USVs and unmanned aerial vehicles in a maritime port environment and analysed the data features. Statistical analysis revealed a high correlation between the distribution of the sea and sky and row positional information. Based on this finding, a three-branch semantic segmentation network with a row position encoding module (RPEM) was proposed to improve the prediction accuracy between the sea and the sky. The proposed RPEM highlights the effect of row coordinates on feature extraction. Compared to the baseline, the three-branch network with RPEM significantly improved the ability to distinguish between the sea and the sky without significantly reducing the computational speed.

实时和高精度情景感知技术对无人水面载具的自主导航至关重要，特别是鲁棒且快速的障碍物语义分割方法是必不可少的。本研究建立了一个海港环境中捕获无人水面载具和无人航空器的角度的数据集，并分析了数据特征。统计分析揭示了海洋和天空的分布与行坐标信息之间的高相关性。基于这一发现，我们提出了一种具有行位置编码模块（RPEM）的三分支语义分割网络，以提高区分海洋和天空的预测准确性。所提出的RPEM突出了行坐标对特征提取的影响。与基准线相比，带有RPEM的三分支网络在不显著降低计算速度的情况下，显著提高了区分海洋和天空的能力。

自主导航中的高效多分支分割网络用于情境感知