Enhanced SAR Ship Detection in Coastal Scenes: A Unified Framework with Denoising and Dynamic Anchors

Enhanced SAR Ship Detection in Coastal Scenes: A Unified Framework with Denoising and Dynamic Anchors | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Enhanced SAR Ship Detection in Coastal Scenes: A Unified Framework with Denoising and Dynamic Anchors Yongqi Kang, Haiping Qu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8297588/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Synthetic Aperture Radar (SAR) imagery plays a pivotal role in maritime surveillance, yet detecting ships in complex coastal scenes remains challenging due to noise interference, diverse target scales, and class imbalance. This paper introduces DN-AnchorNet, an integrated detection framework that couples a dedicated denoising branch with dynamic anchor generation to enhance feature extraction and detection accuracy. By incorporating an adaptive weighted Smooth L1 regression loss, the model effectively addresses sample imbalance, improving robustness. Compared with state-of-the-art rotated detectors—including Faster R-CNN, RoI Transformer, Gliding Vertex, YOLOv8-OBB, and H2RBOX-URC—DN-AnchorNet achieves superior overall performance on both RSDD-SAR and SSDD + datasets. It attains the highest AP (0.699 on RSDD-SAR, 0.610 on SSDD+) and F1-score (0.757, 0.689), while maintaining competitive precision and significantly lowering false positives relative to two-stage detectors. The framework effectively balances detection coverage and false-alarm suppression, demonstrating strong applicability for maritime surveillance in noisy, cluttered scenes. All codes are available at https://github.com/yongqi011210/Dn-anchornet . Artificial Intelligence and Machine Learning Synthetic Aperture Radar Denoising-detection Integration Framework Anchor Adaptation Mechanism Adaptive Weighted Smooth L1 Loss Function Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8297588","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":556377317,"identity":"e220c18d-0810-45c6-9d79-8402a8195ba4","order_by":0,"name":"Yongqi Kang","email":"","orcid":"","institution":"Ludong University","correspondingAuthor":false,"prefix":"","firstName":"Yongqi","middleName":"","lastName":"Kang","suffix":""},{"id":556377318,"identity":"993105b8-4d3c-4aec-95c1-ed28c05cf1a8","order_by":1,"name":"Haiping 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Anchor Adaptation Mechanism, Adaptive Weighted Smooth L1 Loss Function","lastPublishedDoi":"10.21203/rs.3.rs-8297588/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8297588/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSynthetic Aperture Radar (SAR) imagery plays a pivotal role in maritime surveillance, yet detecting ships in complex coastal scenes remains challenging due to noise interference, diverse target scales, and class imbalance. This paper introduces DN-AnchorNet, an integrated detection framework that couples a dedicated denoising branch with dynamic anchor generation to enhance feature extraction and detection accuracy. By incorporating an adaptive weighted Smooth L1 regression loss, the model effectively addresses sample imbalance, improving robustness. Compared with state-of-the-art rotated detectors\u0026mdash;including Faster R-CNN, RoI Transformer, Gliding Vertex, YOLOv8-OBB, and H2RBOX-URC\u0026mdash;DN-AnchorNet achieves superior overall performance on both RSDD-SAR and SSDD\u0026thinsp;+\u0026thinsp;datasets. It attains the highest AP (0.699 on RSDD-SAR, 0.610 on SSDD+) and F1-score (0.757, 0.689), while maintaining competitive precision and significantly lowering false positives relative to two-stage detectors. The framework effectively balances detection coverage and false-alarm suppression, demonstrating strong applicability for maritime surveillance in noisy, cluttered scenes. All codes are available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/yongqi011210/Dn-anchornet\u003c/span\u003e\u003cspan address=\"https://github.com/yongqi011210/Dn-anchornet\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e","manuscriptTitle":"Enhanced SAR Ship Detection in Coastal Scenes: A Unified Framework with Denoising and Dynamic Anchors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-09 04:14:54","doi":"10.21203/rs.3.rs-8297588/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3b5df42a-7bb4-4625-9e7a-caf73f6539c0","owner":[],"postedDate":"December 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":59217891,"name":"Artificial Intelligence and Machine Learning"}],"tags":[],"updatedAt":"2025-12-09T04:14:55+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-09 04:14:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8297588","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8297588","identity":"rs-8297588","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}