{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T12:06:42Z","timestamp":1777896402552,"version":"3.51.4"},"reference-count":42,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,1,28]],"date-time":"2021-01-28T00:00:00Z","timestamp":1611792000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Numerous sensors can obtain images or point cloud data on land, however, the rapid attenuation of electromagnetic signals and the lack of light in water have been observed to restrict sensing functions. This study expands the utilization of two- and three-dimensional detection technologies in underwater applications to detect abandoned tires. A three-dimensional acoustic sensor, the BV5000, is used in this study to collect underwater point cloud data. Some pre-processing steps are proposed to remove noise and the seabed from raw data. Point clouds are then processed to obtain two data types: a 2D image and a 3D point cloud. Deep learning methods with different dimensions are used to train the models. In the two-dimensional method, the point cloud is transferred into a bird\u2019s eye view image. The Faster R-CNN and YOLOv3 network architectures are used to detect tires. Meanwhile, in the three-dimensional method, the point cloud associated with a tire is cut out from the raw data and is used as training data. The PointNet and PointConv network architectures are then used for tire classification. The results show that both approaches provide good accuracy.<\/jats:p>","DOI":"10.3390\/s21030884","type":"journal-article","created":{"date-parts":[[2021,1,28]],"date-time":"2021-01-28T09:03:45Z","timestamp":1611824625000},"page":"884","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Multi-Dimensional Underwater Point Cloud Detection Based on Deep Learning"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9512-4249","authenticated-orcid":false,"given":"Chia-Ming","family":"Tsai","sequence":"first","affiliation":[{"name":"Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, Kaohsiung 804, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi-Horng","family":"Lai","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, Kaohsiung 804, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yung-Da","family":"Sun","sequence":"additional","affiliation":[{"name":"Naval Meteorological and Oceanographic Office R.O.C., Kaohsiung 804, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu-Jen","family":"Chung","sequence":"additional","affiliation":[{"name":"Naval Academy R.O.C., Kaohsiung 804, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jau-Woei","family":"Perng","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, Kaohsiung 804, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,28]]},"reference":[{"key":"ref_1","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems 28, MIT Press 55 Hayward St."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/978-3-319-46448-0_2","article-title":"SSD: Single Shot MultiBox Detector","volume":"9905","author":"Liu","year":"2016","journal-title":"Computer Vision\u2014ECCV 2016"},{"key":"ref_3","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An incremental improvement. arxiv."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Charles, R.Q., Su, H., Kaichun, M., and Guibas, L.J. (2017, January 21\u201326). PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.16"},{"key":"ref_5","unstructured":"Charles, R.Q., Yi, L., Su, H.L., and Guibas, J. (2017). PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space. Advances in Neural Information Processing Systems 30, Neural Information Processing Systems Foundation, Inc. (NIPS)."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhou, Y., and Tuzel, O. (2018, January 18\u201323). VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00472"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.culher.2018.02.017","article-title":"State of the art and applications in archaeological underwater 3D recording and mapping","volume":"33","author":"Menna","year":"2018","journal-title":"J. Cult. Herit."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"6858","DOI":"10.1109\/JSEN.2019.2912325","article-title":"A Statistically-Based Method for the Detection of Underwater Objects in Sonar Imagery","volume":"19","author":"Abu","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_9","unstructured":"Williams, D.P. (August, January 28). Transfer Learning with SAS-Image Convolutional Neural Networks for Improved Underwater Target Classification. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"160755","DOI":"10.1109\/ACCESS.2019.2951443","article-title":"Sonar Image Detection Based on Multi-Scale Multi-Column Convolution Neural Networks","volume":"7","author":"Wang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Valdenegro-Toro, M. (2016, January 12). End-to-end object detection and recognition in forward-looking sonar images with convolutional neural networks. Proceedings of the 2016 IEEE\/OES Autonomous Underwater Vehicles (AUV), Tokyo, Japan.","DOI":"10.1109\/AUV.2016.7778662"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"9929","DOI":"10.1109\/JSEN.2019.2925830","article-title":"Crosstalk Removal in Forward Scan Sonar Image Using Deep Learning for Object Detection","volume":"21","author":"Sung","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1109\/JOE.2016.2591738","article-title":"Three-Dimensional Target Reconstruction from Multiple 2-D Forward-Scan Sonar Views by Space Carving","volume":"42","author":"Aykin","year":"2017","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"856","DOI":"10.1109\/JOE.2017.2751139","article-title":"AUV-Based Underwater 3-D Point Cloud Generation Using Acoustic Lens-Based Multibeam Sonar","volume":"43","author":"Cho","year":"2018","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_15","unstructured":"Xuefeng, Z., Qingning, L., Ye, M., and Yongsheng, J. (2016, January 21\u201323). Dimensional Imaging Sonar Damage Identification Technology Research On Sea-Crossing Bridge Main Pier Pile Foundations. Proceedings of the 2016 5th International Conference on Energy and Environmental Protection, Sanya, China."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Moisan, E., Charbonniera, P., Fouchera, P., Grussenmeyerb, P., Guilleminb, S., and Koehlb, M. (2015, January 16\u201317). Building a 3D reference model for canal tunnel surveying using sonar and laser scanning. Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Piano di Sorrento, Italy.","DOI":"10.5194\/isprsarchives-XL-5-W5-153-2015"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Moisan, E., Charbonnier, P., Foucher, P., Grussenmeyer, P., and Guillemin, S. (2018). Evaluating a Static Multibeam Sonar Scanner for 3D Surveys in Confined Underwater Environments. Remote Sens., 10.","DOI":"10.3390\/rs10091395"},{"key":"ref_18","unstructured":"Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., and Xiao, J. (2015, January 7\u201312). 3D ShapeNets: A deep representation for volumetric shapes. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wu, W., Qi, Z., and Fuxin, L. (2019, January 15\u201320). PointConv: Deep Convolutional Networks on 3D Point Clouds. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00985"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2738","DOI":"10.1109\/TGRS.2014.2364333","article-title":"Graph-Based Supervised Automatic Target Detection","volume":"53","author":"Mishne","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Sinai, A., Amar, A., and Gilboa, G. (2016, January 19\u201323). Mine-Like Objects detection in Side-Scan Sonar images using a shadows-highlights geometrical features space. Proceedings of the OCEANS 2016 MTS\/IEEE Monterey, Monterey, CA, USA.","DOI":"10.1109\/OCEANS.2016.7760991"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.ecss.2017.04.026","article-title":"Applying multibeam sonar and mathematical modeling for mapping seabed substrate and biota of offshore shallows","volume":"192","author":"Peterson","year":"2017","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1109\/JOE.2018.2791878","article-title":"Performance of Multibeam Echosounder Backscatter-Based Classification for Monitoring Sediment Distributions Using Multitemporal Large-Scale Ocean Data Sets","volume":"44","author":"Snellen","year":"2019","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"724","DOI":"10.1109\/JOE.2013.2281133","article-title":"Bayesian Seabed Classification Using Angle-Dependent Backscatter Data from Multibeam Echo Sounders","volume":"39","author":"Landmark","year":"2014","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"S\u00f6hnlein, G., Rush, S., and Thompson, L. (2011, January 19\u201322). Using manned submersibles to create 3D sonar scans of shipwrecks. Proceedings of the OCEANS\u201911 MTS\/IEEE, Waikoloa, HI, USA.","DOI":"10.23919\/OCEANS.2011.6107130"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1145\/358669.358692","article-title":"Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography","volume":"24","author":"Fischler","year":"1981","journal-title":"Commun. ACM"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Tang, J., Mao, Y., Wang, J., and Wang, L. (2019, January 5\u20137). Multi-task Enhanced Dam Crack Image Detection Based on Faster R-CNN. Proceedings of the IEEE 4th International Conference on Image, Vision and Computing, Xiamen, China.","DOI":"10.1109\/ICIVC47709.2019.8981093"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Kafedziski, V., Pecov, S., and Tanevski, D. (2018, January 20\u201321). Detection and Classification of Land Mines from Ground Penetrating Radar Data Using Faster R-CNN. Proceedings of the 26th Telecommunications Forum, Belgrade, Serbia.","DOI":"10.1109\/TELFOR.2018.8612117"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"You, W., Chen, L., and Mo, Z. (2019, January 3\u20135). Soldered Dots Detection of Automobile Door Panels based on Faster R-CNN Model. Proceedings of the Chinese Control and Decision Conference, Nanchang, China.","DOI":"10.1109\/CCDC.2019.8833343"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","article-title":"Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition","volume":"37","author":"He","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhang, X., and Zhu, X. (2019, January 19\u201321). Vehicle Detection in the Aerial Infrared Images via an Improved Yolov3 Network. Proceedings of the IEEE 4th International Conference on Signal and Image Processing, Wuxi, China.","DOI":"10.1109\/SIPROCESS.2019.8868430"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Miao, F., Tian, Y., and Jin, L. (2019, January 24\u201315). Vehicle Direction Detection Based on YOLOv3. Proceedings of the 2019 11th International Conference on Intelligent Human-Machine Systems and Cybernetics, Hangzhou, China.","DOI":"10.1109\/IHMSC.2019.10157"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1049\/hve.2019.0091","article-title":"Research on automatic location and recognition of insulators in substation based on YOLOv3","volume":"5","author":"Liu","year":"2020","journal-title":"High. Volt."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_39","unstructured":"Maturana, D., and Scherer, S. (October, January 28). VoxNet: A 3D Convolutional Neural Network for real-time object recognition. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems, Hamburg, Germany."},{"key":"ref_40","unstructured":"Li, Y., Bu, R., Sun, M., Wu, W., Di, X., and Chen, B. (2018). Pointcnn: Convolution on x-transformed points. Advances in Neural Information Processing Systems 31, Curran Associates Inc."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Li, J., Chen, B.M., and Lee, G.H. (2018, January 18\u201323). SO-Net: Self-Organizing Network for Point Cloud Analysis. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00979"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Simonovsky, M., and Komodakis, N. (2017, January 21\u201326). Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.11"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/3\/884\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:16:40Z","timestamp":1760159800000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/3\/884"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,28]]},"references-count":42,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["s21030884"],"URL":"https:\/\/doi.org\/10.3390\/s21030884","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,28]]}}}