arXiv 2503.228802025-03-28cs.CVcs.LG

海洋碎片前视声纳数据集

The Marine Debris Forward-Looking Sonar Datasets

University of Groningen（格罗宁根大学）；Bonn-Rhein-Sieg University of Applied Sciences（波恩-莱茵-锡格应用科学大学）；Worcester Polytechnic Institute（伍斯特理工学院）；Heriot-Watt University（赫瑞瓦特大学）；German Research Center for Artificial Intelligence（德国人工智能研究中心）

Matias Valdenegro-Toro, Deepan Chakravarthi Padmanabhan, Deepak Singh, Bilal Wehbe, Yvan Petillot

备注：: 10 pages, 12 figures, Oceans Brest 2025 camera readyu

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AI总结：

本文针对水下机器人声纳感知公开数据缺乏的问题，提出了Marine Debris Forward-Looking Sonar数据集，包含三种环境设置及多种计算机视觉任务，为研究界提供完整数据描述与初步结果。

摘要：

声纳感知是水下机器人的基础，但受限于AI系统，需要大型训练数据集。声纳模态的公开数据缺乏。本文提出Marine Debris Forward-Looking Sonar数据集，包含三种不同设置（水池、转台、被淹没的采石场）以增加数据集多样性，以及多种计算机视觉任务：目标分类、目标检测、语义分割、补丁匹配和无监督学习。我们提供了完整的数据集描述、基本分析和部分任务的初步结果。我们期望研究界能从该公开数据集中获益。

英文摘要：

Sonar sensing is fundamental for underwater robotics, but limited by capabilities of AI systems, which need large training datasets. Public data in sonar modalities is lacking. This paper presents the Marine Debris Forward-Looking Sonar datasets, with three different settings (watertank, turntable, flooded quarry) increasing dataset diversity and multiple computer vision tasks: object classification, object detection, semantic segmentation, patch matching, and unsupervised learning. We provide full dataset description, basic analysis and initial results for some tasks. We expect the research community will benefit from this dataset, which is publicly available at https://doi.org/10.5281/zenodo.15101686

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。