{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T20:19:11Z","timestamp":1788812351899,"version":"build-2803163510"},"reference-count":17,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2019,12,23]],"date-time":"2019-12-23T00:00:00Z","timestamp":1577059200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002510","name":"Kongju National University","doi-asserted-by":"publisher","award":["2019-0161-01"],"award-info":[{"award-number":["2019-0161-01"]}],"id":[{"id":"10.13039\/501100002510","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Auto-detecting a submerged human body underwater is very challenging with the absolute necessity to a diver or a submersible. For the vision sensor, the water turbidity and limited light condition make it difficult to take clear images. For this reason, sonar sensors are mainly utilized in water. However, even though a sonar sensor can give a plausible underwater image within this limitation, the sonar image\u2019s quality varies greatly depending on the background of the target. The readability of the sonar image is very different according to the target distance from the underwater floor or the incidence angle of the sonar sensor to the floor. The target background must be very considerable because it causes scattered and polarization noise in the sonar image. To successfully classify the sonar image with these noises, we adopted a Convolutional Neural Network (CNN) such as AlexNet and GoogleNet. In preparing the training data for this model, the data augmentation on scattering and polarization were implemented to improve the classification accuracy from the original sonar image. It could be practical to classify sonar images undersea even by training sonar images only from the simple testbed experiments. Experimental validation was performed using three different datasets of underwater sonar images from a submerged body of a dummy, resulting in a final average classification accuracy of 91.6% using GoogleNet.<\/jats:p>","DOI":"10.3390\/s20010094","type":"journal-article","created":{"date-parts":[[2019,12,23]],"date-time":"2019-12-23T03:15:01Z","timestamp":1577070901000},"page":"94","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":69,"title":["Study on the Classification Performance of Underwater Sonar Image Classification Based on Convolutional Neural Networks for Detecting a Submerged Human Body"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6139-2632","authenticated-orcid":false,"given":"Huu-Thu","family":"Nguyen","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, Kongju National University, Cheonan 31080, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6441-6636","authenticated-orcid":false,"given":"Eon-Ho","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Kongju National University, Cheonan 31080, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2834-1109","authenticated-orcid":false,"given":"Sejin","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Automotive Engineering, Kongju National University, Cheonan 31080, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Francisco, F., and Jan, S. (2019). Detection of Visual Signatures of Marine Mammals and Fish within Marine Renewable Energy Farms using Multibeam Imaging Sonar. J. Mar. Sci. Eng., 7.","DOI":"10.3390\/jmse7020022"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Lee, S. (2017, January 21\u201324). Deep Learning of Submerged Body Images from 2D Sonar Sensor based on Convolutional Neural Network. Proceedings of the 2017 IEEE Underwater Technology (UT), Busan, Korea.","DOI":"10.1109\/UT.2017.7890309"},{"key":"ref_3","first-page":"91","article-title":"Identification of Underwater Objects using Sonar Image","volume":"53","author":"Kang","year":"2016","journal-title":"J. Inst. Electron. Inf. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1007\/s00773-014-0294-x","article-title":"Acoustic beam profile-based rapid underwater object detection for an imaging sonar","volume":"20","author":"Cho","year":"2015","journal-title":"J. Mar. Sci. 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Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/1\/94\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:44:46Z","timestamp":1760190286000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/1\/94"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,23]]},"references-count":17,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2020,1]]}},"alternative-id":["s20010094"],"URL":"https:\/\/doi.org\/10.3390\/s20010094","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,23]]}}}