{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T11:38:12Z","timestamp":1785929892722,"version":"3.56.0"},"reference-count":24,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,11,15]],"date-time":"2022-11-15T00:00:00Z","timestamp":1668470400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["52171341"],"award-info":[{"award-number":["52171341"]}]},{"name":"National Natural Science Foundation of China","award":["61902431"],"award-info":[{"award-number":["61902431"]}]},{"name":"National Natural Science Foundation of China","award":["61972417"],"award-info":[{"award-number":["61972417"]}]},{"name":"National Natural Science Foundation of China","award":["2020-84"],"award-info":[{"award-number":["2020-84"]}]},{"name":"National Natural Science Foundation of China","award":["ZR2020MF005"],"award-info":[{"award-number":["ZR2020MF005"]}]},{"name":"the science and technology project of Qingdao west coast new area","award":["52171341"],"award-info":[{"award-number":["52171341"]}]},{"name":"the science and technology project of Qingdao west coast new area","award":["61902431"],"award-info":[{"award-number":["61902431"]}]},{"name":"the science and technology project of Qingdao west coast new area","award":["61972417"],"award-info":[{"award-number":["61972417"]}]},{"name":"the science and technology project of Qingdao west coast new area","award":["2020-84"],"award-info":[{"award-number":["2020-84"]}]},{"name":"the science and technology project of Qingdao west coast new area","award":["ZR2020MF005"],"award-info":[{"award-number":["ZR2020MF005"]}]},{"name":"the scientific foundation of Shandong province","award":["52171341"],"award-info":[{"award-number":["52171341"]}]},{"name":"the scientific foundation of Shandong province","award":["61902431"],"award-info":[{"award-number":["61902431"]}]},{"name":"the scientific foundation of Shandong province","award":["61972417"],"award-info":[{"award-number":["61972417"]}]},{"name":"the scientific foundation of Shandong province","award":["2020-84"],"award-info":[{"award-number":["2020-84"]}]},{"name":"the scientific foundation of Shandong province","award":["ZR2020MF005"],"award-info":[{"award-number":["ZR2020MF005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Underwater acoustic target recognition is very complex due to the lack of labeled data sets, the complexity of the marine environment, and the interference of background noise. In order to enhance it, we propose an attention-based residual network recognition method (AResnet). The method can be used to identify ship-radiated noise in different environments. Firstly, a residual network is used to extract the deep abstract features of three-dimensional fusion features, and then a channel attention module is used to enhance different channels. Finally, the features are classified by the joint supervision of cross-entropy and central loss functions. At the same time, for the recognition of ship-radiated noise in other environments, we use the pre-training network AResnet to extract the deep acoustic features and apply the network structure to underwater acoustic target recognition after fine-tuning. The two sets of ship radiation noise datasets are verified, the DeepShip dataset is trained and verified, and the average recognition accuracy is 99%. Then, the trained AResnet structure is fine-tuned and applied to the ShipsEar dataset. The average recognition accuracy is 98%, which is better than the comparison method.<\/jats:p>","DOI":"10.3390\/e24111657","type":"journal-article","created":{"date-parts":[[2022,11,15]],"date-time":"2022-11-15T02:28:58Z","timestamp":1668479338000},"page":"1657","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Underwater Acoustic Target Recognition Based on Attention Residual Network"],"prefix":"10.3390","volume":"24","author":[{"given":"Juan","family":"Li","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baoxiang","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2326-9518","authenticated-orcid":false,"given":"Xuerong","family":"Cui","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shibao","family":"Li","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianhang","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ke, X., Yuan, F., and Cheng, E. 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