CaveSeg introduces a visual learning pipeline for semantic segmentation in underwater caves, addressing the scarcity of annotated training data. The dataset includes pixel annotations for navigation markers, obstacles, scuba divers, and open areas. Benchmark analyses across cave systems in the USA, Mexico, and Spain demonstrate the effectiveness of robust deep visual models developed based on CaveSeg. A novel transformer-based model is formulated to achieve near real-time execution with state-of-the-art performance. The design choices and implications of semantic segmentation for visual servoing by AUVs inside underwater caves are explored. The proposed model and benchmark dataset pave the way for future research in autonomous underwater cave exploration and mapping.
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by A. Abdullah,... alle arxiv.org 03-04-2024
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