In this paper, we present CaveSeg - the first visual learning pipeline for semantic segmentation and scene parsing for AUV navigation inside underwater caves. We address the problem of scarce annotated training data by preparing a comprehensive dataset for semantic segmentation of underwater cave scenes. It contains pixel annotations for important navigation markers (e.g. caveline, arrows), obstacles (e.g. ground plain and overhead layers), scuba divers, and open areas for servoing. Through comprehensive benchmark analyses on cave systems in USA, Mexico, and Spain locations, we demonstrate that robust deep visual models can be developed based on CaveSeg for fast semantic scene parsing of underwater cave environments. In particular, we formulate a novel transformer-based model that is computationally light and offers near real-time execution in addition to achieving state-of-the-art performance. Finally, we explore the design choices and implications of semantic segmentation for visual servoing by AUVs inside underwater caves. The proposed model and benchmark dataset open up promising opportunities for future research in autonomous underwater cave exploration and mapping.

本研究介绍了CaveSeg，这是一种针对水下洞穴中AUV导航的语义分割和场景解析的视觉学习方法。通过准备全面的水下洞穴场景语义分割数据集，解决了标注有限的训练数据的问题。通过在美国、墨西哥和西班牙的洞穴系统上进行全面的基准分析，展示了CaveSeg可以基于深度视觉模型实现快速的水下洞穴场景解析，并开辟了自主水下洞穴探索和绘制地图的未来研究机会。

CaveSeg：自主水下洞穴探索的深度语义分割和场景分析