DGP-DETR : A real-time detection algorithm for underwater target detection

DGP-DETR : A real-time detection algorithm for underwater target detection | 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 DGP-DETR : A real-time detection algorithm for underwater target detection Hui Lv, Ke Si, Wen Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7164495/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 The exploration of contemporary underwater visual information collection faces issues of poor image fidelity and the complexity of heterogeneous backgrounds. To address these challenges, this paper proposes the DGP-DETR target detection architecture. By introducing Partical Reparameter Convolution (RPConv) of the backbone, we reducing parameter complexity. The Deformable attention mechanism significantly enhances the model's detection capability by dynamically and adaptively selecting crucial area features. We also designed a Global-Local Spatial Attention Bidirectional Feature Pyramid Network (G-Bifpn), which strengthens feature interaction through multi-scale feature fusion, thereby improving target detection accuracy while optimizing computational efficiency. Experimental results indicate that Precision and Recall improvements of 5.3% and 9.2%, respectively. Moreover, the mAP50 and mAP50-95 metrics show enhancements of 4.2% and 8.6%, respectively. The empirical findings substantiate that the proposed architecture constitutes an effective solution to the inherent challenges encountered in underwater target detection. Underwater target detection RT-DETR Attention mechanism Feature fusion Full Text Additional Declarations No competing interests reported. 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-7164495","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":490501030,"identity":"af9f35b9-5905-43b7-ab2b-9a489e536904","order_by":0,"name":"Hui Lv","email":"","orcid":"","institution":"Henan Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Lv","suffix":""},{"id":490501031,"identity":"6d8b3aaf-ac53-4f63-abf9-bb341b927770","order_by":1,"name":"Ke Si","email":"","orcid":"","institution":"Henan Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Ke","middleName":"","lastName":"Si","suffix":""},{"id":490501032,"identity":"852b6a53-e54b-4ef4-9fd3-cf0cf29be081","order_by":2,"name":"Wen Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYBACfobjBw4k/mGzY2xvPkCcFsnGM4kHHjbwJTP3HEsgTovB4QPGBx82yDG2z8gxINJlxw4kHEjcYcbM23Pm4403DHZyug0EdDD2HAT65Uwan2R772bLOQzJxmYHCGhhlgDaksB2jNmw5+w2aR6GA4nbCGlhk39gANTyn3H/jZxnxGkBKjI4kNjGxtg4I4eNOC0SDGeADjvDlszYc8zYco4BEX6xP3D88McfFeCofHjjTYWdHEEtqFbyEBs1SFpI1TEKRsEoGAUjAgAABwFOa0R5vjcAAAAASUVORK5CYII=","orcid":"","institution":"Hebei University of Architecture and Engineering","correspondingAuthor":true,"prefix":"","firstName":"Wen","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-07-19 12:38:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7164495/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7164495/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95801867,"identity":"9ae147f3-96eb-4ba2-9fae-87f8d31ad695","added_by":"auto","created_at":"2025-11-13 08:26:21","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":851115,"visible":true,"origin":"","legend":"","description":"","filename":"paper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7164495/v1_covered_526ba2d0-6342-4e2f-b314-354fd81bcfe4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"DGP-DETR : A real-time detection algorithm for underwater target detection","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"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},"keywords":"Underwater target detection, RT-DETR, Attention mechanism, Feature fusion","lastPublishedDoi":"10.21203/rs.3.rs-7164495/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7164495/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe exploration of contemporary underwater visual information collection faces issues of poor image fidelity and the complexity of heterogeneous backgrounds. 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