Abstract
Mechanical scanning sonar (MSS) plays an important role in high-precision object recognition and detection in underwater environments. However, existing research on MSS has focused on large objects, such as subsea structures and sunken ships, and often relies on unreleased datasets collected in confined water tank environments, limiting the study of small underwater objects and their application to real marine environments. Therefore, in this study, a Small Underwater Objects 3D Point Cloud (SUOP) Dataset was constructed using an MSS (BV5000) in the actual underwater environments at the seafloor. The dataset contains over 1,500 high-quality 3D point clouds for five objects, corresponding initial sonar scan data files, sonar system metadata, and 2D sonar images. The practicality of the proposed dataset was verified by applying it to an object recognition model. The results demonstrate that the SUOP dataset, with its object types, materials, and scanning conditions, enables accurate and robust evaluation of underwater object detection models, hence proving to be a valuable resource for research on marine underwater object detection.
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Data availability
SUOP dataset is publicly available in zenodo repository15, with the detailed directory structure and file information in the README.md file. It includes 1,555 XYZ-format 3D point cloud data and the corresponding metadata files, as well as 27,990 2D sonar images. The data were separated into Tire, Dummy, Drum, Chair, and Net. The custom python scripts and label files for object detection is also available in separate repository archived with zenodo16.
Code availability
SUOP dataset is publicly available in zenodo repository15. Custom python scripts to generate 3D point cloud files and 2D projection images, enabling users to create training datasets with SUOP dataset, including 2D bounding boxes and object detection label files, are also available at a separate zenodo repository19. The scripts are tested on MacOS and Windows 11 in Python 3.12.2 environment.
References
Islam, M. J. et al. Semantic segmentation of Underwater Imagery: Dataset and Benchmark. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), https://doi.org/10.1109/IROS45743.2020.9340821 (2020).
Liu, C. et al. A dataset and benchmark of underwater object detection for robot picking. In 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), https://doi.org/10.1109/ICMEW53276.2021.9455997 (2021).
Chen, L. et al. Underwater object detection in the era of artificial intelligence: current, challenge, and future. Preprint at https://doi.org/10.48550/arXiv.2410.05577 (2024).
Jian, M. et al. Underwater object detection and datasets: a survey. Intell. Mar. Technol. Syst. 2(9), 1–12 (2024).
Xie, K., Yang, J. & Qiu, K. A Dataset with Multibeam Forward-Looking Sonar for Underwater Object Detection. Sci Data 9, 739 (2022).
Chen, Z., Xie, G., Deng, X., Peng, J. & Qiu, H. DA-YOLOv7: A deep learning-driven high-performance underwater sonar image target recognition model. J. Mar. Sci. Eng. 12, 1606 (2024).
Hasan, M. J. et al. Exploring the feasibility of affordable sonar technology: Object detection in underwater environments using the Ping360. Preprint at https://doi.org/10.48550/arXiv.2411.05863 (2024).
Sethuraman, A. V. et al. Machine learning for shipwreck segmentation from side scan sonar imagery: Dataset and benchmark. Preprint at https://doi.org/10.48550/arXiv.2401.14546 (2024).
Chew, J. L. & Chitre, M. Object detection with sector scanning sonar. In OCEANS 2013 – San Diego (IEEE, 2013).
Teledyne Marine, 3D Multibeam Sonar (BV 5000 Series). https://www.teledynemarine.com/brands/blueview/3d-multibeam-scanning-sonar.
Ntuli, S., Akombelwa, M., Forbes, A. & Singh, M. Classification of 3D sonar point clouds derived underwater using machine and deep learning (CANUPO and RandLA-Net) approaches. S. Afr. J. Geomatics 13, 269–287, https://doi.org/10.4314/sajg.v13i2.4 (2024).
Lai, Y.-H., Yu, J.-H. & Gu, Y. Volume estimation of harbor engineering objects using underwater three-dimensional sonar. In Proceedings of the 2022 8th International Conference on Control, Automation and Robotics (ICCAR), https://doi.org/10.1109/ICCAR55106.2022.9782650 (2022).
Tsai, C.-M., Lai, Y.-H., Sun, Y.-D., Chung, Y.-J. & Perng, J.-W. Multi-dimensional underwater point cloud detection based on deep learning. Sensors 21, 884 (2021).
DeBortoli, R., Li, F., Kapoor, A. & Hollinger, G. A. Adversarial training on point clouds for sim-to-real 3D object detection. IEEE Robot. Autom. Lett. 6, 6662–6669 (2021).
Ha, J.-W. & Choi, W.-S. Underwater 3D multibeam scan datasets. Zenodo https://doi.org/10.5281/zenodo.18475883 (2026).
Moisan, E. et al. Assessment of a static multibeam sonar scanner for 3D surveying in confined subaquatic environments. In Proceedings of the XXIII ISPRS Congress, 541–548, https://doi.org/10.5194/isprs-archives-XLI-B5-541-2016 (2016).
Teledyne Marine, ProScan software for sonar data processing. https://www.teledynemarine.com/brands/blueview/proscan.
Teledyne Marine. BlueViewer software for sonar data processing. https://www.teledynemarine.com/en-us/products/Pages/BlueViewer.aspx.
Ha, J.-W. & Choi, W.-S. SUOP object detection. https://doi.org/10.5281/zenodo.18844894 (2026).
Jocher, G. et al. YOLOv8 by Ultralytics. GitHub https://github.com/ultralytics/ultralytics (2023).
Qi, C. R., Yi, L., Su, H. & Guibas, L. J. PointNet++: Deep hierarchical feature learning on point sets in a metric space. In Adv. Neural Inf. Process. Syst. https://doi.org/10.48550/arXiv.1706.02413 (2017).
Acknowledgements
This research was supported by Korea Basic Science Institute National Research Facilities and Equipment Center grant funded by the Ministry of Science and ICT (No. RS-2024-00404564, Equipment No. NFEC-2025-02-303186). We would like to express our sincere gratitude to Professor Joo-Hyun Woo of the Korea Maritime and Ocean University for providing the data collection site, Professors Sunho Park and Professor Gi-Hoon Byun for their advice on data processing and assistance with site preparation. We would like to thank Ryang-Hun Kang, Min-Kyu Kim and Min-Seok Son for their substantial help with transporting and installing the experimental equipment. We are also deeply grateful to Professor Woen-Sug Choi for his careful guidance and helpful advice throughout the research, and to the researchers at the Intelligent Ocean Engineering Systems Laboratory for their assistance with the experimental process.
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Ji-Wan Ha constructed the SUOP dataset, conducted experiments, and conducted data analysis. Woen-Sug Choi supervised the research, provided methodology advice, and revised and reviewed the paper. Joo-Hyun Woo provided experimental sites and assisted with data collection. Gi-Hoon Byun and Sunho Park contributed data processing advice and site preparations. Ryang-Hun Kang, Min-Kyu Kim and Min-Seok Son for their substantial help with transporting and installing the experimental equipment. Hyeung-Sik Choi has provided financial support for artifacts and tools to perform the experiments.
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Ha, JW., Choi, WS., Choi, HS. et al. Small Underwater Objects 3D Point Cloud Dataset Using Mechanical Scanning Sonar. Sci Data (2026). https://doi.org/10.1038/s41597-026-07070-0
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DOI: https://doi.org/10.1038/s41597-026-07070-0
