ESO-YOLO: Enhanced Small Object Detection Algorithm from Multiple Perspectives

ESO-YOLO: Enhanced Small Object Detection Algorithm from Multiple Perspectives | 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 ESO-YOLO: Enhanced Small Object Detection Algorithm from Multiple Perspectives Dong Wu, Wenhao Guan, Bingjie Zhang, Hao Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9249925/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Accurate recognition of low-resolution small objects represents a critical technical challenge across diverse fields. Such small objects—exemplified by traffic signs—are indispensable for behavioral decision-making in autonomous driving and target localization in disaster monitoring and rescue. However, prevailing object detection algorithms commonly suffer from small-object feature loss, insufficient detection accuracy, as well as complex network architectures and excessive parameter counts.To address the limitations in small-object detection, this study proposes ESO-YOLO, an enhanced small-object detection algorithm built upon the YOLOv11 framework. The algorithm achieves active feature fusion via the construction of an Efficient Feature Fusion Module (EFFM), mitigates information loss of small objects through the designed Lightweight Spatial Down-sampling (LSDown), and explicitly preserves shallow fine-grained features by proposing Learnable Shallow Bypass (LSBypass) integrated into the LSC3 module. These improvements enhance small-object detection performance at the levels of network architecture and feature processing, while maintaining the lightweight nature of the algorithm.Experimental validation is conducted on the TT100K traffic sign detection dataset and the VisDrone2019 UAV aerial dataset. The proposed model achieves significant improvements in both detection accuracy and recall rate, accompanied by a reduced parameter count, and demonstrates superior cross-scene detection capability in generalization experiments.Extensive comparative experiments and ablation studies verify that the model presented in this paper effectively alleviates feature loss and background interference in small-object detection. It enhances small-object detection performance while realizing network lightweighting, exhibiting favorable practical application value and strong generalization ability. Small Object Detection Unmanned Aerial Vehicle Autonomous Driving Lightweight Model Deep Learning YOLO Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 13 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviews received at journal 07 Apr, 2026 Reviewers agreed at journal 06 Apr, 2026 Reviewers invited by journal 06 Apr, 2026 Editor assigned by journal 04 Apr, 2026 Submission checks completed at journal 30 Mar, 2026 First submitted to journal 28 Mar, 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-9249925","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":619017060,"identity":"60639fcf-76c8-42d8-8469-165285048169","order_by":0,"name":"Dong Wu","email":"","orcid":"","institution":"Guilin university of technology","correspondingAuthor":false,"prefix":"","firstName":"Dong","middleName":"","lastName":"Wu","suffix":""},{"id":619017061,"identity":"57dec417-7a49-437b-b7cb-8296f8b1afe2","order_by":1,"name":"Wenhao Guan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYBCDBAYG5gMMD2BsIrWwJQBJA5K08BgQp0U+IvnZY56KO3n87T2fXyS2/WHgZ88xYPi5A7cWwxtp5sY8Z54VS5w5u80isc2AQbLnjQFj7xk8WmYkmEnnth1ObLiRu80ApMXgRo4BM2MbPi3p36Rz/x1OnH8j5xlYiz0hLfISOUBbGg4nbriRw/wAbIsEAS0GPG/KpP8cO5y48cwxM4aEc8Y8EmeeFRzsxWdLe/o2yRk1hxPnHW9+/OFDmZwcf3vyxgc/8dlyAMFmkwASPCDWAaxqYbY0INjMH/CpHAWjYBSMgpELAAQhVq1H+ul7AAAAAElFTkSuQmCC","orcid":"","institution":"Guilin university of technology","correspondingAuthor":true,"prefix":"","firstName":"Wenhao","middleName":"","lastName":"Guan","suffix":""},{"id":619017062,"identity":"914d31f0-c9f9-4759-8288-be46c86e6ff1","order_by":2,"name":"Bingjie Zhang","email":"","orcid":"","institution":"Guilin university of technology","correspondingAuthor":false,"prefix":"","firstName":"Bingjie","middleName":"","lastName":"Zhang","suffix":""},{"id":619017063,"identity":"999f146d-a02c-425a-bb4e-deb73e062788","order_by":3,"name":"Hao Chen","email":"","orcid":"","institution":"Guilin university of technology","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2026-03-28 05:39:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9249925/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9249925/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106960387,"identity":"5b894b9e-e3dd-4d54-8e1d-9e209545d688","added_by":"auto","created_at":"2026-04-15 09:20:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":631358,"visible":true,"origin":"","legend":"","description":"","filename":"ESOYOLO.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9249925/v1_covered_d566132c-800e-4185-8b31-ec7b5481705c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"ESO-YOLO: Enhanced Small Object Detection Algorithm from Multiple Perspectives","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":"[email protected]","identity":"international-journal-of-machine-learning-and-cybernetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jmlc","sideBox":"Learn more about [International Journal of Machine Learning and Cybernetics](http://actavetscand.biomedcentral.com/)","snPcode":"13042","submissionUrl":"https://submission.nature.com/new-submission/13042/3","title":"International Journal of Machine Learning and Cybernetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Small Object Detection, Unmanned Aerial Vehicle, Autonomous Driving, Lightweight Model, Deep Learning, YOLO","lastPublishedDoi":"10.21203/rs.3.rs-9249925/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9249925/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate recognition of low-resolution small objects represents a critical technical challenge across diverse fields. Such small objects\u0026mdash;exemplified by traffic signs\u0026mdash;are indispensable for behavioral decision-making in autonomous driving and target localization in disaster monitoring and rescue. However, prevailing object detection algorithms commonly suffer from small-object feature loss, insufficient detection accuracy, as well as complex network architectures and excessive parameter counts.To address the limitations in small-object detection, this study proposes ESO-YOLO, an enhanced small-object detection algorithm built upon the YOLOv11 framework. The algorithm achieves active feature fusion via the construction of an Efficient Feature Fusion Module (EFFM), mitigates information loss of small objects through the designed Lightweight Spatial Down-sampling (LSDown), and explicitly preserves shallow fine-grained features by proposing Learnable Shallow Bypass (LSBypass) integrated into the LSC3 module. 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