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OpenSonarDatasets|声纳技术数据集|水下研究数据集
收藏github2024-11-19 更新2024-11-22 收录
下载链接：
https://github.com/remaro-network/OpenSonarDatasets
下载链接
链接失效反馈资源简介：
OpenSonarDatasets是一个致力于整合开放源代码声纳数据集的仓库，旨在为水下研究和开发提供便利。该仓库鼓励研究人员扩展当前的数据集集合，以增加开放源代码声纳数据集的可见性，并提供一个更容易查找和比较数据集的方式。
创建时间：
2024-11-14
原始信息汇总
OpenSonarDatasets 🌊
数据集概述
OpenSonarDatasets 是一个致力于整合开源声呐数据集的仓库，旨在为水下研究和开发提供便利。该仓库鼓励研究人员扩展当前的数据集集合，以增加开源声呐数据集的可见性，并提供一个更便捷的方式来查找和比较数据集。
数据集来源
该数据集比较起源于提交给 IEEE Journal of Oceanic Engineering 的期刊论文 "Sonar-based DL in Underwater Robotics: Overview, Robustness, and Challenges"。初始数据集包含在该论文中，但请注意，未来由社区贡献的数据集将不会包含在原始论文的比较中。
数据集比较表
以下表格比较了当前最先进的声呐水下数据集，分析了声呐类型、数据类型、数据样本数量、数据中的对象标签、数据是否标注、深度学习任务、数据集是否描述了声呐频率、高度等设置，以及数据集的发布年份。
| 数据集名称 | 声呐类型 | 数据类型 | 数据样本数量 | 对象标签 | 标注类型 | 设置描述 | 发布年份 | 相关论文 |
|---|---|---|---|---|---|---|---|---|
| Northern Adriatic Reefs | SSS | GeoTIFF | 7 | Reefs | ✗ | ✓ | 2010 | ✗ |
| Lago Grey | SSS | Raw | ✗ | Glacier, Walls | ✗ | ✓ | 2019 | Paper |
| UCI ML | ✗ | Raw | 211 | Mines, Rocks | Classification | ✗ | ✗ | ✗ |
| SeabedObjects-KLSG | SSS | Images | 1190 | Wrecks, Humans, Mines | Classification | ✗ | 2020 | Paper |
| Marine_PULSE | SSS | Images | 627 | Pipes, Mounds, Platforms | Classification | ✗ | 2023 | Paper |
| NKSID | FLS | Images | 2617 | Infrastructures, Propellers, Tires | Classification | ✓ | 2024 | Paper |
| UATD | FLS | Images | 9200 | Tires, Mannequins, Boxes | Object Detection | ✓ | 2022 | Paper |
| SSS for Mine Detection | SSS | Images | 1170 | Mines | Object Detection | ✗ | 2024 | Paper |
| SWDD | SSS | Images | 7904 | Walls | Object Detection | ✓ | 2024 | Paper |
| SubPipe | SSS * | Images | 10030 | Pipelines | Object Detection | ✓ | 2024 | Paper |
| UXO | FLS | Images/Raw | 74437 | Unexploded Ordnances | Object Detection | ✓ | 2024 | Paper |
| MDT | FLS | Images | 2471 | Infrastructures, Debris | Segmentation | ✓ | 2021 | Paper |
| SASSED | SAS | Images | 129 | Muds, Sea Grass, Rocks, Sands | Segmentation | ✗ | 2023 | ✗ |
| Seafloor Sediments | SSS | Images | 434164 | Rocks, Marine life | Segmentation | ✓ | 2023 | Paper |
| DIDSON | FLS | Images | 1000 | Fishes Species | Segmentation | ✓ | 2022 | Paper |
| AI4Shipwreck | SSS | Images | 286 | Shipwrecks | Segmentation | ✓ | 2024 | Paper |
| Cave Sonar | MSIS * | Rosbag | 500 meters | Cave Seabed | SLAM | ✓ | 2017 | Paper |
| Aurora | MBES, SSS * | Raw | MBES: 81km, SSS: 15h | Seabed, Marine habitats | SLAM | ✓ | 2020 | Paper |
| MBES-Slam | MBES | Rosbag | 4 missions | Seabed | SLAM | ✓ | 2022 | Paper |
贡献指南
欢迎社区贡献！如果您有开源声呐数据集并希望添加到此仓库，请创建一个包含数据集表格描述和数据集链接（以及相关论文）的拉取请求。该仓库不存储数据集，而是一个集中目录，用于查找大多数可用数据集的链接。通过贡献，您可以帮助创建一个中心位置，使研究人员能够轻松访问和比较声呐数据集，最终促进水下机器人领域的发展。
AI搜集汇总
数据集介绍
构建方式
OpenSonarDatasets数据集的构建基于对现有开源声呐数据集的系统性整合。这一过程始于对IEEE Journal of Oceanic Engineering期刊论文《Sonar-based DL in Underwater Robotics: Overview, Robustness, and Challenges》的研究，该论文提供了初始数据集的来源。随后，通过社区的广泛参与，不断纳入新的数据集，确保了数据集的多样性和时效性。每个数据集的收录均经过严格的筛选，包括声呐类型、数据类型、样本数量、对象标签、数据标注、数据收集设置描述以及发布年份等关键信息，以确保数据集的质量和适用性。
特点
OpenSonarDatasets数据集的显著特点在于其全面性和开放性。该数据集不仅涵盖了多种声呐类型，如侧扫声呐（SSS）、前视声呐（FLS）、机械扫描成像声呐（MSIS）和多波束回声测深仪（MBES），还包含了丰富的数据类型和大量的数据样本。此外，数据集中的对象标签和标注信息为深度学习任务提供了坚实的基础，而详细的数据收集设置描述则有助于研究者更好地理解和应用这些数据。
使用方法
使用OpenSonarDatasets数据集时，研究者可以通过访问GitHub页面获取数据集的详细信息和下载链接。每个数据集的条目都包含了声呐类型、数据类型、样本数量、对象标签、数据标注、数据收集设置描述以及发布年份等关键信息，便于研究者进行筛选和比较。此外，数据集的开放性和社区参与机制使得研究者可以轻松地贡献新的数据集，进一步丰富和扩展数据集的内容，从而推动水下机器人和声呐技术的发展。
背景与挑战
背景概述
OpenSonarDatasets是一个专注于整合开源声呐数据集的存储库，旨在为水下研究和开发提供支持。该数据集的创建源于对水下机器人研究中缺乏统一和可访问声呐数据集的认知。其核心研究问题是如何通过提供一个组织良好的开源声呐数据集集合，来促进研究人员在开源声呐数据集上的项目启动。该数据集的初始版本包含在即将发表于IEEE Journal of Oceanic Engineering的论文《Sonar-based DL in Underwater Robotics: Overview, Robustness, and Challenges》中。OpenSonarDatasets不仅提供了数据集的链接，还通过一个详细的比较表，帮助研究人员理解和选择适合其研究需求的数据集。
当前挑战
OpenSonarDatasets面临的挑战主要集中在数据集的多样性和质量上。首先，由于声呐技术的多样性，数据集涵盖了多种类型的声呐（如侧扫声呐、前视声呐等），这增加了数据集的标准化和比较的复杂性。其次，数据集的标注和注释情况不一，部分数据集缺乏详细的标注信息，这影响了数据集在深度学习任务中的应用。此外，数据集的收集环境和设置描述不一致，导致在实际应用中的可重复性和可靠性存在差异。最后，尽管该存储库旨在不断更新，但原始论文的内容是固定的，这意味着社区贡献的数据集不会被纳入原始的论文比较中，这可能会影响数据集的长期维护和更新。
常用场景
经典使用场景
OpenSonarDatasets 数据集的经典使用场景主要集中在水下机器人和海洋研究领域。该数据集通过整合多种类型的声纳数据，如侧扫声纳（SSS）、前视声纳（FLS）、机械扫描成像声纳（MSIS）和多波束回声测深仪（MBES），为研究人员提供了丰富的数据资源。这些数据广泛应用于水下目标检测、分类、分割以及同时定位与地图构建（SLAM）等任务，极大地促进了水下机器人技术的进步和海洋环境的监测与保护。
衍生相关工作
OpenSonarDatasets 数据集的发布催生了大量相关研究工作。例如，基于该数据集的研究成果发表在 IEEE Journal of Oceanic Engineering 等权威期刊上，涉及声纳图像的深度学习应用、水下目标检测的鲁棒性研究以及多传感器融合技术等。此外，该数据集还激发了多个开源项目的开发，如水下机器人导航系统的优化和海洋环境监测平台的构建，进一步推动了水下机器人技术的发展和应用。
数据集最近研究
最新研究方向
在海洋科学与机器人技术的交叉领域，OpenSonarDatasets数据集的最新研究方向主要集中在利用深度学习技术提升水下声呐图像的解析与识别能力。随着水下机器人技术的快速发展，研究人员正致力于通过整合多源声呐数据，如侧扫声呐（SSS）、前视声呐（FLS）和多波束回声测深仪（MBES），来增强对复杂水下环境的感知和理解。这些研究不仅推动了水下目标检测、分类和分割等任务的性能提升，还为水下机器人导航与地图构建（SLAM）提供了更为精确的数据支持。此外，随着数据集的不断扩展和更新，研究者们也在探索如何通过预训练模型和对抗训练等方法，提高模型在不同水下环境中的鲁棒性和泛化能力。
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