  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 plane 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.
