Abstract
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.
Abstract (translated)
在本文中,我们介绍了水下洞穴中的洞穴SEG(深视觉模型),它是第一个用于AUV在水下洞穴中进行语义分割和场景解析的视觉学习管道。我们为了解决缺乏标注训练数据的问题,准备了一组全面的数据集,用于水下洞穴场景的语义分割。该数据集包含对重要导航标记(例如洞穴线、箭头)像素annotations、障碍物(例如地面平原和上方层)、潜水员、以及用于控制运动的开放区域。通过对美国、墨西哥和西班牙等地的水下洞穴系统的全面基准分析,我们证明了基于洞穴SEG的强有力深度视觉模型可以用于快速水下洞穴环境中语义场景解析。特别是,我们制定了一种新型Transformer-based模型,其计算量较轻,并提供了近乎实时的执行,除了实现最先进的性能外。最后,我们探索了语义分割对AUV水下洞穴内视觉控制的设计和影响。我们提出的模型和基准数据集为未来自主水下洞穴探索和 mapping的研究打开了 promising 机会。
URL
https://arxiv.org/abs/2309.11038
