{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T15:27:55Z","timestamp":1768404475351,"version":"3.49.0"},"reference-count":50,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T00:00:00Z","timestamp":1663200000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Laboratory of Ocean Geomatics, Ministry of Natural Resources, China","award":["2021A01"],"award-info":[{"award-number":["2021A01"]}]},{"name":"Key Laboratory of Ocean Geomatics, Ministry of Natural Resources, China","award":["2042021kf1030"],"award-info":[{"award-number":["2042021kf1030"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["2021A01"],"award-info":[{"award-number":["2021A01"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["2042021kf1030"],"award-info":[{"award-number":["2042021kf1030"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Artificial reef detection in multibeam sonar images is an important measure for the monitoring and assessment of biological resources in marine ranching. With respect to how to accurately detect artificial reefs in multibeam sonar images, this paper proposes an artificial reef detection framework for multibeam sonar images based on convolutional neural networks (CNN). First, a large-scale multibeam sonar image artificial reef detection dataset, FIO-AR, was established and made public to promote the development of artificial multibeam sonar image artificial reef detection. Then, an artificial reef detection framework based on CNN was designed to detect the various artificial reefs in multibeam sonar images. Using the FIO-AR dataset, the proposed method is compared with some state-of-the-art artificial reef detection methods. The experimental results show that the proposed method can achieve an 86.86% F1-score and a 76.74% intersection-over-union (IOU) and outperform some state-of-the-art artificial reef detection methods.<\/jats:p>","DOI":"10.3390\/rs14184610","type":"journal-article","created":{"date-parts":[[2022,9,16]],"date-time":"2022-09-16T01:35:10Z","timestamp":1663292110000},"page":"4610","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Artificial Reef Detection Method for Multibeam Sonar Imagery Based on Convolutional Neural Networks"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3665-1350","authenticated-orcid":false,"given":"Zhipeng","family":"Dong","sequence":"first","affiliation":[{"name":"The First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4746-6479","authenticated-orcid":false,"given":"Yanxiong","family":"Liu","sequence":"additional","affiliation":[{"name":"The First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China"},{"name":"The Key Laboratory of Ocean Geomatics, Ministry of Natural Resources, Qingdao 266590, China"}]},{"given":"Long","family":"Yang","sequence":"additional","affiliation":[{"name":"The First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China"},{"name":"The Key Laboratory of Ocean Geomatics, Ministry of Natural Resources, Qingdao 266590, China"}]},{"given":"Yikai","family":"Feng","sequence":"additional","affiliation":[{"name":"The First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China"},{"name":"The Key Laboratory of Ocean Geomatics, Ministry of Natural Resources, Qingdao 266590, China"}]},{"given":"Jisheng","family":"Ding","sequence":"additional","affiliation":[{"name":"The First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China"},{"name":"The Key Laboratory of Ocean Geomatics, Ministry of Natural Resources, Qingdao 266590, China"}]},{"given":"Fengbiao","family":"Jiang","sequence":"additional","affiliation":[{"name":"The First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,15]]},"reference":[{"key":"ref_1","first-page":"1133","article-title":"Construction of marine ranching in China: Reviews and prospects","volume":"40","author":"Yang","year":"2016","journal-title":"J. Fish. China"},{"key":"ref_2","first-page":"1255","article-title":"Strategic thinking on the construction of modern marine ranching in China","volume":"43","author":"Yang","year":"2019","journal-title":"J. Fish. China"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhou, X., Zhao, X., Zhang, S., and Lin, J. (2019). Marine ranching construction and management in east china sea: Programs for sustainable fishery and aquaculture. Water, 11.","DOI":"10.3390\/w11061237"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"139782","DOI":"10.1016\/j.scitotenv.2020.139782","article-title":"Evolution of marine ranching policies in China: Review, performance and prospects","volume":"737","author":"Yu","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"105407","DOI":"10.1016\/j.ocecoaman.2020.105407","article-title":"Influencing factors of spatial variation of national marine ranching in China","volume":"199","author":"Qin","year":"2021","journal-title":"Ocean Coast. Manag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2210","DOI":"10.1093\/icesjms\/fsr141","article-title":"A methodology for acoustic and geospatial analysis of diverse artificial-reef datasets","volume":"68","author":"Kang","year":"2011","journal-title":"ICES J. Mar. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"134768","DOI":"10.1016\/j.scitotenv.2019.134768","article-title":"Microplastic pollution in water, sediment, and fish from artificial reefs around the Ma\u2019an Archipelago, Shengsi, China","volume":"703","author":"Zhang","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"104255","DOI":"10.1016\/j.marpol.2020.104255","article-title":"Exploring the goals and objectives of policies for marine ranching management: Performance and prospects for China","volume":"122","author":"Yu","year":"2020","journal-title":"Mar. Pol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"113333","DOI":"10.1016\/j.jenvman.2021.113333","article-title":"Early detection of marine invasive species following the deployment of an artificial reef: Integrating tools to assist the decision-making process","volume":"297","author":"Castro","year":"2021","journal-title":"J. Environ. Manag."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1093\/icesjms\/fsaa174","article-title":"No detrimental effects of desalination waste on temperate fish assemblages","volume":"78","author":"Whitmarsh","year":"2021","journal-title":"ICES J. Mar. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1093\/icesjms\/fsw133","article-title":"Monitoring of reef associated and pelagic fish communities on Australia\u2019s first purpose built offshore artificial reef","volume":"74","author":"Becker","year":"2016","journal-title":"ICES J. Mar. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.jembe.2012.01.013","article-title":"Comparison of baited remote underwater video (BRUV) and underwater visual census (UVC) for assessment of artificial reefs in estuaries","volume":"416","author":"Lowry","year":"2012","journal-title":"J. Exp. Mar. Biol. Ecol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"105589","DOI":"10.1016\/j.fishres.2020.105589","article-title":"Application of a long-range camera to monitor fishing effort on an offshore artificial reef","volume":"228","author":"Becker","year":"2020","journal-title":"Fish. Res."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Trzcinska, K., Tegowski, J., Pocwiardowski, P., Janowski, L., Zdroik, J., Kruss, A., Rucinska, M., Lubniewski, Z., and Schneider von Deimling, J. (2021). Measurement of seafloor acoustic backscatter angular dependence at 150 kHz using a multibeam echosounder. Remote Sens., 13.","DOI":"10.3390\/rs13234771"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Tassetti, A.N., Malaspina, S., and Fabi, G. (2015, January 16\u201317). Using a multibeam echosounder to monitor an artificial reef. Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Piano di Sorrento, Italy.","DOI":"10.5194\/isprsarchives-XL-5-W5-207-2015"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wan, J., Qin, Z., Cui, X., Yang, F., Yasir, M., Ma, B., and Liu, X. (2022). MBES seabed sediment classification based on a decision fusion method using deep learning model. Remote Sens., 14.","DOI":"10.3390\/rs14153708"},{"key":"ref_17","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":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 8\u201312). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision (CVPR), Boston, MA, USA.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1109\/TPAMI.2015.2437384","article-title":"Region-based convolutional networks for accurate object detection and segmentation","volume":"38","author":"Girshick","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_20","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (July, January 26). You only look once: Unified, real-time object detection. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA."},{"key":"ref_21","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (July, January 26). SSD: Single shot multibox detector. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA."},{"key":"ref_22","unstructured":"Dai, J., Li, Y., He, K., and Sun, J. (2016, January 4\u20139). R-FCN: Object detection via region based fully convolutional networks. Proceedings of the Neural Information Processing Systems (NIPS), Barcelona, Spain."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, faster, stronger. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_25","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An incremental improvement. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Dong, Z., Wang, M., Wang, Y., Liu, Y., Feng, Y., and Xu, W. (2022). Multi-oriented object detection in high-resolution remote sensing imagery based on convolutional neural networks with adaptive object orientation features. Remote Sens., 14.","DOI":"10.3390\/rs14040950"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2104","DOI":"10.1109\/TGRS.2019.2953119","article-title":"Object detection in high resolution remote sensing imagery based on convolutional neural networks with suitable object scale features","volume":"58","author":"Dong","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Xiong, H., Liu, L., and Lu, Y. (2021, January 17\u201319). Artificial reef detection and recognition based on Faster-RCNN. Proceedings of the IEEE 2nd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA), Chongqing, China.","DOI":"10.1109\/ICIBA52610.2021.9687986"},{"key":"ref_29","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). YOLOv4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_30","unstructured":"Feldens, P., Westfeld, P., Valerius, J., Feldens, A., and Papenmeier, S. (2021). Automatic detection of boulders by neural networks. Hydrographische Nachrichten 119, Deutsche Hydrographische Gesellschaft E.V."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_34","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A.L. (2014). Semantic image segmentation with deep convolutional nets and fully connected CRFs. Comput. Sci., 357\u2013361."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs","volume":"40","author":"Chen","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_36","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"48890","DOI":"10.1109\/ACCESS.2019.2910572","article-title":"Convolutional edge constraint-based U-Net for salient object detection","volume":"7","author":"Han","year":"2019","journal-title":"IEEE Access"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"106210","DOI":"10.1016\/j.cmpb.2021.106210","article-title":"Medical image recognition and segmentation of pathological slices of gastric cancer based on Deeplab v3+ neural network","volume":"207","author":"Wang","year":"2021","journal-title":"Comput. Met. Prog. Biomed."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1007\/s00371-018-1519-5","article-title":"Dual-path adversarial learning for fully convolutional network (FCN)-based medical image segmentation","volume":"34","author":"Bi","year":"2018","journal-title":"Vis. Comput."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., and Liang, J. (2018, January 20). UNet++: A nested U-Net architecture for medical image segmentation. Proceedings of the 4th Deep Learning in Medical Image Analysis (DLMIA) Workshop, Granada, Spain.","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Guo, M., Liu, H., Xu, Y., and Huang, Y. (2020). Building extraction based on U-Net with an attention block and multiple losses. Remote Sens., 12.","DOI":"10.3390\/rs12091400"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1109\/LGRS.2018.2802944","article-title":"Road extraction by deep residual U-Net","volume":"15","author":"Zhang","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.isprsjprs.2020.01.013","article-title":"ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data","volume":"162","author":"Diakogiannis","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Lin, Y., Xu, D., Wang, N., Shi, Z., and Chen, Q. (2020). Road extraction from very-high-resolution remote sensing images via a nested SE-Deeplab model. Remote Sens., 12.","DOI":"10.3390\/rs12182985"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Jiao, L., Huo, L., Hu, C., and Tang, P. (2020). Refined unet: Unet-based refinement network for cloud and shadow precise segmentation. Remote Sens., 12.","DOI":"10.3390\/rs12122001"},{"key":"ref_47","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","article-title":"Backpropagation applied to kandwritten zip code recognition","volume":"1","author":"LeCun","year":"1989","journal-title":"Neural Comput."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"6315","DOI":"10.1080\/01431161.2021.1938736","article-title":"A cloud detection method for GaoFen-6 wide field of view imagery based on the spectrum and variance of superpixels","volume":"42","author":"Dong","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"He, S., and Jiang, W. (2021). Boundary-assisted learning for building extraction from optical remote sensing imagery. Remote Sens., 13.","DOI":"10.3390\/rs13040760"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/18\/4610\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:32:12Z","timestamp":1760142732000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/18\/4610"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,15]]},"references-count":50,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["rs14184610"],"URL":"https:\/\/doi.org\/10.3390\/rs14184610","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,15]]}}}