{"paper_id":"175b80a9-3e91-4f94-896a-4c40aab4da1d","body_text":"Enhancing Foggy Weather Object Detection via Frequency Decoupling, Content-Adaptive Feature Fusion and Efficient Spatial Pyramid Pooling in YOLOv11 | 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 Enhancing Foggy Weather Object Detection via Frequency Decoupling, Content-Adaptive Feature Fusion and Efficient Spatial Pyramid Pooling in YOLOv11 Jiaxin Zhu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8796663/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract Foggy weather poses critical challenges to object detection systems due to atmospheric scattering effects: Mie scattering attenuates high-frequency edge information, forward scattering introduces low-frequency noise and contrast compression, and spatially non-uniform fog distribution complicates feature representation. Traditional two-stage dehazing-then-detection pipelines suffer from error accumulation and computational redundancy. This paper proposes a feature-aware detection framework built upon YOLOv11, directly integrating three enhancement modules targeting physical degradation mechanisms. The High-Low Frequency Decoupling (HLFD) module recovers atmospheric scattering-eroded features through frequency-domain decomposition and targeted enhancement. The Content-Adaptive Feature Modulation Fusion (CAFMFusion) module dynamically aligns shallow and deep features through dual-level adaptive gating based on local degradation levels. The Spatial Pyramid Pooling Fast with Cross Stage Partial Connection (SPPFCSPC) module efficiently expands global receptive fields via serial cascaded pooling. Systematic experiments on RTTS and Foggy Cityscapes datasets validate the method's effectiveness. On RTTS, the framework achieves 60.4% mAP and 83.3% [email protected] , improving 5.4 and 6.6 percentage points over YOLOv11 baseline and outperforming 11 mainstream detectors, while maintaining 95.5 FPS inference speed. The method demonstrates robust performance across varying fog densities, providing reliable technical support for visual perception systems under adverse weather conditions. Foggy Weather Object Detection Frequency-Domain Decoupling Content-Adaptive Feature Fusion Spatial Pyramid Pooling YOLOv11 Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 05 May, 2026 Reviews received at journal 04 May, 2026 Reviews received at journal 01 May, 2026 Reviewers agreed at journal 27 Apr, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers agreed at journal 21 Apr, 2026 Reviews received at journal 20 Apr, 2026 Reviewers agreed at journal 20 Apr, 2026 Reviewers invited by journal 19 Apr, 2026 Editor assigned by journal 19 Apr, 2026 Submission checks completed at journal 07 Feb, 2026 First submitted to journal 05 Feb, 2026 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-8796663\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":629401735,\"identity\":\"d4ecb936-131d-4150-9a5d-1a971996d915\",\"order_by\":0,\"name\":\"Jiaxin Zhu\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABE0lEQVRIie3QMUvEMBTA8VcKdYkEtydI7ys8OeghFvwqdYlLh5uKmw2FTnUvOPgZXG7OEbip1tXBoXJfoHLLDQrm7uAWYzmcHPIfMoT8SPIAXK5/GOdSdglhOOKF2myc7faD38lprTV103h8Xi8SMIjtTg8QUkKcdL24zlVKhxFQTQQJaU/mzWr1UQKbjCqCPtPAH3Kr8IoqMn/R/pF/P8O5IRclI69uNeCbshIfmgkZEnj58ww2hBaM/ONSA2FiJQGkERrCQKXLfk++BggDIQwRaAjgnngDBNEMOaGYzJAjbFo0REznVXvD8NVOrl6kfF9/4t0jL5b9bRaHpPVTt84uQ17byY9bt6vaPtnlcrlcf+4bJ6VibuzpIQAAAAAASUVORK5CYII=\",\"orcid\":\"\",\"institution\":\"Liaoning Technical University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Jiaxin\",\"middleName\":\"\",\"lastName\":\"Zhu\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2026-02-05 11:57:20\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-8796663/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-8796663/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":107870151,\"identity\":\"fecd7c58-55e6-4638-90a0-10fee61b7670\",\"added_by\":\"auto\",\"created_at\":\"2026-04-27 07:38:57\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":3129791,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SCI.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8796663/v1_covered_28eff79e-8795-4f96-b489-ee6d9cede547.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Enhancing Foggy Weather Object Detection via Frequency Decoupling, Content-Adaptive Feature Fusion and Efficient Spatial Pyramid Pooling in YOLOv11\",\"fulltext\":[],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":false,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":true,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":true,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"the-journal-of-supercomputing\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"\",\"sideBox\":\"Learn more about [The Journal of Supercomputing](https://www.springer.com/journal/11227)\",\"snPcode\":\"11227\",\"submissionUrl\":\"https://submission.nature.com/new-submission/11227/3\",\"title\":\"The Journal of Supercomputing\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false},\"keywords\":\"Foggy Weather Object Detection, Frequency-Domain Decoupling, Content-Adaptive Feature Fusion, Spatial Pyramid Pooling, YOLOv11\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8796663/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8796663/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eFoggy weather poses critical challenges to object detection systems due to atmospheric scattering effects: Mie scattering attenuates high-frequency edge information, forward scattering introduces low-frequency noise and contrast compression, and spatially non-uniform fog distribution complicates feature representation. Traditional two-stage dehazing-then-detection pipelines suffer from error accumulation and computational redundancy. This paper proposes a feature-aware detection framework built upon YOLOv11, directly integrating three enhancement modules targeting physical degradation mechanisms. The High-Low Frequency Decoupling (HLFD) module recovers atmospheric scattering-eroded features through frequency-domain decomposition and targeted enhancement. The Content-Adaptive Feature Modulation Fusion (CAFMFusion) module dynamically aligns shallow and deep features through dual-level adaptive gating based on local degradation levels. The Spatial Pyramid Pooling Fast with Cross Stage Partial Connection (SPPFCSPC) module efficiently expands global receptive fields via serial cascaded pooling. Systematic experiments on RTTS and Foggy Cityscapes datasets validate the method's effectiveness. On RTTS, the framework achieves 60.4% mAP and 83.3% mAP@.5, improving 5.4 and 6.6 percentage points over YOLOv11 baseline and outperforming 11 mainstream detectors, while maintaining 95.5 FPS inference speed. 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