{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T18:10:36Z","timestamp":1790273436200,"version":"4.1.0"},"reference-count":49,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:00:00Z","timestamp":1790208000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100007219","name":"Natural Science Foundation of Shanghai Municipality","doi-asserted-by":"publisher","award":["25ZR1401156"],"award-info":[{"award-number":["25ZR1401156"]}],"id":[{"id":"10.13039\/100007219","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","award":["202408310293"],"award-info":[{"award-number":["202408310293"]}],"id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"\n Synthetic Aperture Radar (SAR) ship target detection holds significant application value in maritime traffic monitoring and marine environmental monitoring. However, due to challenges such as small ship targets, complex marine backgrounds, and speckle noise interference, existing methods still suffer from insufficient accuracy in small-target detection scenarios. To address this issue, this paper proposes a high-resolution detection algorithm named LHR-YOLO, based on YOLO11. In the Stem stage, a Gaussian\u2013Laplacian edge-enhancement and Gaussian-filtering mechanism is introduced to effectively improve shallow-feature perception for tiny ship targets. In the Backbone, an improved C3k2 module is designed by incorporating channel shuffling, re-parameterized convolutions, and feature aggregation strategies to enhance multi-scale feature representation. Meanwhile, in the Neck\u2013Detect stage, high-frequency perception, spatial dependency modeling, and a high-resolution detection head are integrated to further improve the localization accuracy of extremely small targets. Experimental results show that LHR-YOLO achieves\n \n \n \n \n \n \n \n m<\/mml:mi>\n A<\/mml:mi>\n P<\/mml:mi>\n <\/mml:mrow>\n \n 50<\/mml:mn>\n :<\/mml:mi>\n 95<\/mml:mn>\n <\/mml:mrow>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n values of 71.59% and 73.40% on the HRSID and SSDD datasets, respectively. Compared with YOLO11n, LHR-YOLO improves\n \n \n \n \n \n \n \n m<\/mml:mi>\n A<\/mml:mi>\n P<\/mml:mi>\n <\/mml:mrow>\n \n S<\/mml:mi>\n <\/mml:mrow>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n on HRSID from 53.95% to 60.53%, corresponding to an increase of 6.58 percentage points. These results validate the effectiveness of the proposed algorithm for detecting tiny ships in complex SAR maritime scenes and provide technical support for high-precision maritime target monitoring.The source code is available at:\n https:\/\/github.com\/LRYTH\/LHR-YOLO<\/jats:ext-link>\n .\n <\/jats:p>","DOI":"10.1371\/journal.pone.0359334","type":"journal-article","created":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T17:37:11Z","timestamp":1790271431000},"page":"e0359334","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["LHR-YOLO: A SAR small-target ship detection method based on improved YOLO11"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-5158-4039","authenticated-orcid":true,"given":"Miao","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Information Engineering, Shanghai Maritime University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dezhi","family":"Han","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Shanghai Maritime University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Shanghai Maritime University","place":["Shanghai, China"]},{"name":"School of Computer Science, The University of Sydney","place":["Sydney, New South Wales, Australia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7714-0798","authenticated-orcid":true,"given":"Yangshuyi","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Shanghai Maritime University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chongqing","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Shanghai Maritime University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,9,24]]},"reference":[{"issue":"4","key":"pone.0359334.ref001","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1109\/MGRS.2024.3456444","article-title":"The high-resolution synthetic aperture radar system and signal processing techniques: current progress and future prospects [Review Papers]","volume":"12","author":"Y Deng","year":"2024","journal-title":"IEEE Geosci Remote Sens Mag"},{"key":"pone.0359334.ref002","first-page":"398","article-title":"Based on the principle, application, and future development direction of synthetic aperture radar technology","volume":"7","author":"H Li","year":"2024","journal-title":"Transc Comp Sci Intell Syst Res"},{"key":"pone.0359334.ref003","doi-asserted-by":"crossref","DOI":"10.1016\/j.sciaf.2024.e02391","article-title":"Spacecraft formation flying orbital control for earth observation mission","volume":"26","author":"A Alzubairi","year":"2024","journal-title":"Scient African"},{"issue":"18","key":"pone.0359334.ref004","doi-asserted-by":"crossref","first-page":"7088","DOI":"10.3390\/s22187088","article-title":"A lightweight YOLOv5-MNE algorithm for SAR ship detection","volume":"22","author":"L Pang","year":"2022","journal-title":"Sensors (Basel)"},{"issue":"6","key":"pone.0359334.ref005","doi-asserted-by":"crossref","first-page":"944","DOI":"10.3390\/rs16060944","article-title":"TAG-Net: target attitude angle-guided network for ship detection and classification in SAR images","volume":"16","author":"D Pan","year":"2024","journal-title":"Remote Sensing"},{"issue":"6","key":"pone.0359334.ref006","doi-asserted-by":"crossref","first-page":"1488","DOI":"10.3390\/rs14061488","article-title":"CRTransSar: a visual transformer based on contextual joint representation learning for SAR ship detection","volume":"14","author":"R Xia","year":"2022","journal-title":"Remote Sensing"},{"key":"pone.0359334.ref007","doi-asserted-by":"crossref","first-page":"741516","DOI":"10.1016\/j.aquaculture.2024.741516","article-title":"Enhanced marine fish small sample image recognition with RVFL in Faster R-CNN model","volume":"595","author":"H Sun","year":"2025","journal-title":"Aquaculture"},{"key":"pone.0359334.ref008","unstructured":"Zhao X, Li S, Chu Z, Zhang Y, Hao P, Yan T, et al. 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