YOLO-ESFM:A Multi-scale YOLO Algorithm for Sea Surface Object Detection.

YOLO-ESFM:A Multi-scale YOLO Algorithm for Sea Surface Object 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 YOLO-ESFM:A Multi-scale YOLO Algorithm for Sea Surface Object Detection. Fei Yan, Keyu Chen, En Cheng, Puhui Qu, Jikang Ma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4623645/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 Environmental perception and object detection are pivotalresearch topics in the marine domain. The sea surface presents unique challenges, including harsh weather conditions, wave interference, and multi-scale targets, often resulting in suboptimal detection results. To address these issues, we present an innovative solution: integrating the Efficient Scale Fusion Module (ESFM) into the advanced YOLO architecture, resulting in the enhanced model, YOLO-ESFM. The ESFM serves as both the backbone and detection head of the network, significantly improving performance compared to the baseline models in YOLOv5s, YOLOv7-tiny, and YOLOv7. Furthermore, to tackle the limitations of the CIOU in YOLOv7, we introduce an improved method, ZIOU, which has been rigorously evaluated and proven effective on the Sea Surface Target Dataset. Comparative studies demonstrate that YOLO-ESFM not only maintains efficiency in terms of parameters and FLOPs but also surpasses YOLOv7 in detection accuracy on both the Sea Surface Target Dataset and the PASCAL VOC 07+12 Dataset. Object Detection Scale Fusion YOLO Deep Learning 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. 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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-4623645","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":321027302,"identity":"94a997ec-d6e1-4404-8809-f08eaf138f61","order_by":0,"name":"Fei Yan","email":"","orcid":"","institution":"Xiamen University","correspondingAuthor":false,"prefix":"","firstName":"Fei","middleName":"","lastName":"Yan","suffix":""},{"id":321027303,"identity":"3a5b84d0-861b-436f-aa70-817a44445a08","order_by":1,"name":"Keyu Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYDACCQYGxgYgzQ/hMpOgRbKBZC0GB4jVwj+7+djDGRWHEzffyE6TYKiwTmxgP3sAvyV3jqUbbjhzOHHbmbPbJBjOpCc28OQl4NViIJFjJvmwDajleO82CUYgo0GCx4CAlvxvkg//AR3WzAvU8o8oLTlskhsbDiduYAfZ0kCEFokbaWaSM46lG884c3azRQKQ0caTg18L/4zkZ5I9Nday/TNyN974AGKwn8GvBQqaIVQCELMRox4I6ohUNwpGwSgYBSMSAAAE1khhoyyPHQAAAABJRU5ErkJggg==","orcid":"","institution":"Xiamen University","correspondingAuthor":true,"prefix":"","firstName":"Keyu","middleName":"","lastName":"Chen","suffix":""},{"id":321027304,"identity":"b5606929-8aa5-4a1c-9a43-51c3f4b9270d","order_by":2,"name":"En Cheng","email":"","orcid":"","institution":"Xiamen University","correspondingAuthor":false,"prefix":"","firstName":"En","middleName":"","lastName":"Cheng","suffix":""},{"id":321027305,"identity":"3a50fc9c-cb06-4ec1-8868-15c456152441","order_by":3,"name":"Puhui Qu","email":"","orcid":"","institution":"Xiamen University","correspondingAuthor":false,"prefix":"","firstName":"Puhui","middleName":"","lastName":"Qu","suffix":""},{"id":321027306,"identity":"987aa984-9eca-4257-bfdf-8294e6e7577f","order_by":4,"name":"Jikang Ma","email":"","orcid":"","institution":"Xiamen University","correspondingAuthor":false,"prefix":"","firstName":"Jikang","middleName":"","lastName":"Ma","suffix":""}],"badges":[],"createdAt":"2024-06-23 03:23:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4623645/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4623645/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60945790,"identity":"adeec296-07d7-46d8-b400-bade7cf3be28","added_by":"auto","created_at":"2024-07-23 22:38:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":839144,"visible":true,"origin":"","legend":"","description":"","filename":"YOLOESFMAMultiscaleYOLOAlgorithmforMarineObjectDetection.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4623645/v1_covered_e5bbcced-73c8-4648-8979-872762f3f20a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"YOLO-ESFM:A Multi-scale YOLO Algorithm for Sea Surface Object Detection.","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Object Detection, Scale Fusion, YOLO, Deep Learning","lastPublishedDoi":"10.21203/rs.3.rs-4623645/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4623645/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEnvironmental perception and object detection are pivotalresearch topics in the marine domain. 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