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——首个面向水下洞穴内AUV导航的语义分割与场景解析视觉学习流水线。针对标注训练数据稀缺的问题，我们构建了包含水下洞穴场景语义分割的综合数据集。该数据集为重要导航标识（如洞穴线、箭头）、障碍物（如地面层与顶部覆盖层）、潜水员以及用于伺服控制的开放区域提供了像素级标注。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可开发出鲁棒的深度视觉模型，实现对水下洞穴环境的快速语义场景解析。特别地，我们设计了一种新颖的基于Transformer的计算轻量化模型，在实现接近实时执行的同时达到了最先进的性能。最后，我们探讨了语义分割设计对水下洞穴内AUV视觉伺服控制的启示。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前景广阔的发展方向。