# Weakly Supervised Caveline Detection For AUV Navigation Inside Underwater Caves

arXiv: https://arxiv.org/abs/2303.03670 · HTML: https://mlsanity.com/paper/2303.03670/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2023.

## Built on by (most cited first, 3 of 3)

- [CaveSeg: Deep Semantic Segmentation and Scene Parsing for Autonomous Underwater Cave Exploration](https://mlsanity.com/paper/2309.11038/) (2023)
- [Semantics from Space: Satellite-Guided Thermal Semantic Segmentation Annotation for Aerial Field Robots](https://mlsanity.com/paper/2403.14056/) (2024)
- [AquaBEV: Monocular Underwater BEV Occupancy with 3D Sonar Supervision](https://mlsanity.com/paper/2609.04411/) (2026)

## Abstract (arXiv)

Underwater caves are challenging environments that are crucial for water resource management, and for our understanding of hydro-geology and history. Mapping underwater caves is a time-consuming, labor-intensive, and hazardous operation. For autonomous cave mapping by underwater robots, the major challenge lies in vision-based estimation in the complete absence of ambient light, which results in constantly moving shadows due to the motion of the camera-light setup. Thus, detecting and following the caveline as navigation guidance is paramount for robots in autonomous cave mapping missions. In this paper, we present a computationally light caveline detection model based on a novel Vision Transformer (ViT)-based learning pipeline. We address the problem of scarce annotated training data by a weakly supervised formulation where the learning is reinforced through a series of noisy predictions from intermediate sub-optimal models. We validate the utility and effectiveness of such weak supervision for caveline detection and tracking in three different cave locations: USA, Mexico, and Spain. Experimental results demonstrate that our proposed model, CL-ViT, balances the robustness-efficiency trade-off, ensuring good generalization performance while offering 10+ FPS on single-board (Jetson TX2) devices.