Robust Sea-Sky Line Detection in Complex Maritime Environments via Semantic Segmentation

Abstract

Sea-sky line detection (SSLD) is pivotal for applications such as unmanned surface vehicles (USVs) navigation and maritime target detection. However, existing algorithms are susceptible to interference from adverse weather, illumination change, and the presence of waves, leading to poor detection accuracy and robustness. To address these challenges, we propose a novel SSLD algorithm based on a deep semantic segmentation network. First, we integrate the strengths of convolutional neural networks (CNNs) and Transformers in a lightweight block named efficient vision transformer (E-ViT). This block enables efficient interaction and aggregation of local and global features with lower computational overhead. Building upon E-ViT, we develop an encoder module that significantly improves the accuracy of semantic segmentation while maintaining the network’s lightweight. Then, we design a robust postprocessing module, which leverages semantic information to effectively remove interferences and filter out candidate points for the sea-sky line, thereby achieving high-precision SSLD. Finally, we construct a well-labeled maritime scene dataset with diverse complex attributes to validate the proposed algorithm. Experimental results demonstrate that our method outperforms several state-of-the-art algorithms in terms of both accuracy and robustness in complex maritime scenarios.