RT-FogNet: Real-Time Ship Detection under Low-Visibility Conditions in Inland Waterways

RT-FogNet: Real-Time Ship Detection under Low-Visibility Conditions in Inland Waterways | 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 Article RT-FogNet: Real-Time Ship Detection under Low-Visibility Conditions in Inland Waterways Pei Yuan, chuanzhen xu, Xiaolin Tan, Sihua Fu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7361383/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Real-time ship detection in inland waterways is critically challenged by low-visibility conditions such as dense fog, backlight, and surface reflections. To address this, we propose RT-FogNet, a lightweight and deployable detection framework tailored for practical inland waterway scenarios. RT-FogNet integrates a Water Surface Image Dehazing (WSID) module to improve image clarity through a domain-adaptive compression and alignment strategy, and builds upon an enhanced YOLOv10-based detection backbone, into which a Dilated Shared Spatial Pyramid Pooling Fast (DS-SPPF) module is incorporated to strengthen multi-scale feature representation with minimal computational overhead. To support robust evaluation under diverse weather conditions, we introduce the Inland Waterway Ship Dataset (IWSD), comprising over 9,000 annotated images collected from real-world inland scenes. Experiments conducted on COCO, SeaShips, and IWSD datasets demonstrate that RT-FogNet achieves significant performance gains, including over 15 percentage points improvement in AP on IWSD, while maintaining high inference speed and low parameter complexity. These results confirm the practical suitability of RT-FogNet for real-time deployment in vision-based ship monitoring systems. The dataset and code are publicly available at https://github.com/Object-Detection-01/RT-FogNet . Physical sciences/Engineering Earth and environmental sciences/Environmental sciences Physical sciences/Mathematics and computing Earth and environmental sciences/Ocean sciences Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 18 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 15 Dec, 2025 Reviews received at journal 13 Dec, 2025 Reviewers agreed at journal 04 Dec, 2025 Reviews received at journal 03 Dec, 2025 Reviewers agreed at journal 03 Dec, 2025 Reviewers invited by journal 02 Dec, 2025 Editor invited by journal 19 Aug, 2025 Editor assigned by journal 14 Aug, 2025 Submission checks completed at journal 13 Aug, 2025 First submitted to journal 13 Aug, 2025 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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Waterways","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7361383/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7361383/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Real-time ship detection in inland waterways is critically challenged by low-visibility conditions such as dense fog, backlight, and surface reflections. To address this, we propose RT-FogNet, a lightweight and deployable detection framework tailored for practical inland waterway scenarios. RT-FogNet integrates a Water Surface Image Dehazing (WSID) module to improve image clarity through a domain-adaptive compression and alignment strategy, and builds upon an enhanced YOLOv10-based detection backbone, into which a Dilated Shared Spatial Pyramid Pooling Fast (DS-SPPF) module is incorporated to strengthen multi-scale feature representation with minimal computational overhead. To support robust evaluation under diverse weather conditions, we introduce the Inland Waterway Ship Dataset (IWSD), comprising over 9,000 annotated images collected from real-world inland scenes. Experiments conducted on COCO, SeaShips, and IWSD datasets demonstrate that RT-FogNet achieves significant performance gains, including over 15 percentage points improvement in AP on IWSD, while maintaining high inference speed and low parameter complexity. These results confirm the practical suitability of RT-FogNet for real-time deployment in vision-based ship monitoring systems. 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