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.

翻译：本文提出了CaveSeg——首个用于水下洞穴内自主水下航行器(AUV)导航的语义分割与场景解析的视觉学习流程。针对训练标注数据稀缺的问题，我们构建了用于水下洞穴场景语义分割的综合数据集。该数据集包含重要导航标志物（如洞穴引导绳、箭头）、障碍物（如地面平面和顶层层）、潜水员以及用于伺服控制的开放区域的像素级标注。通过对美国、墨西哥和西班牙洞穴系统的全面基准分析，我们证明基于CaveSeg可开发出稳健的深度视觉模型，用于水下洞穴环境的快速语义场景解析。特别地，我们设计了一种新型基于Transformer的模型，该模型计算量轻、可实现近实时执行，并取得了最先进的性能。最后，我们探讨了语义分割设计选择及其对水下洞穴内AUV视觉伺服控制的影响。所提出的模型与基准数据集为未来自主水下洞穴探索与测绘研究开辟了令人期待的前景。