EAI-YOLO：Faster, and More Accurate for Real-Time Dynamic Object Detection

EAI-YOLO：Faster, and More Accurate for Real-Time Dynamic 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 EAI-YOLO：Faster, and More Accurate for Real-Time Dynamic Object Detection Jiahao Chen, Kexue Sun, Zhipeng You, Lingqi Xiang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6997041/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Sep, 2025 Read the published version in Signal, Image and Video Processing → Version 1 posted 5 You are reading this latest preprint version Abstract In sports event scenarios, precise detection of dynamic objects is crucial for real-time monitoring and event analysis. However, traditional dynamic object detection methods face challenges such as small target size, similar appearances, disordered motion, and dense occlusions, which often lead to problems like false detections, low accuracy, and poor robustness. To address these challenges, this paper proposes a real-time dynamic object detection algorithm EAI-YOLO based on improved YOLOv8s. First, the Efficient Channel Attention (ECA) is introduced to enhance the capability of capturing detailed features of small-scale moving targets. Second, the traditional Path Aggregation Feature Pyramid Network (PAFPN) is replaced by an Adaptive Feature Pyramid Network (AFPN) to improve the detection performance for densely occluded objects. Finally, a scale-adaptive Inner-CIoU loss is designed to enhance the detection generalization ability for blurry targets and those with abrupt scale changes. Experimental results show that on the self-made dataset, EAI-YOLO achieves an [email protected] of 83.7% and an [email protected] –0.95 of 45.2%, with an inference time of only 0.034 seconds per frame, balancing real-time performance and detection accuracy. This study overcomes the issues of insufficient shallow feature extraction and performance degradation in occluded scenes of existing methods in complex motion scenarios, providing a high-precision technical solution for scenarios such as real-time sports event monitoring, athlete motion analysis, and intelligent referee systems. It has important application value for promoting the construction of "Smart Venues" and the intelligent development of competitive sports. Deep learning dynamic object detection YOLOv8s ECA AFPN Inner-CIoU Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 19 Sep, 2025 Read the published version in Signal, Image and Video Processing → Version 1 posted Editorial decision: Revision requested 10 Jul, 2025 Reviewers invited by journal 10 Jul, 2025 Editor assigned by journal 29 Jun, 2025 Submission checks completed at journal 29 Jun, 2025 First submitted to journal 28 Jun, 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. 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-6997041","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":483286208,"identity":"3e3863b4-1690-4bfd-b502-a9e7ddbb1243","order_by":0,"name":"Jiahao Chen","email":"","orcid":"","institution":"Nanjing University of Posts and Telecommunications, Jiangsu Nanjing","correspondingAuthor":false,"prefix":"","firstName":"Jiahao","middleName":"","lastName":"Chen","suffix":""},{"id":483286209,"identity":"120eee0c-6d92-4f56-9b67-ef5dc8782c2a","order_by":1,"name":"Kexue Sun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYHACZgaGChABAmxEazkDIphJ0cLYBtVKlBb5GbmHjXnn3WE3uN1/gOFD2WEG/tkN+LUY3MhLTpy57RmzwZ3DDIwzzh1mkLhzgIAWiRzjAx+3HWY2uJHMwMzbdhgokkDIYUAtiXOgWv4So4XhRo5xwscGqBZGYrQYnHljbDjj2GFmyTuHDQ72nEvnkbhByGHtOcbSPDWHk/luNz588KPMWo5/BiGHQUEygwQDwwEgg4c49UBgB9IyCkbBKBgFowArAAC0NUFkR4oBgAAAAABJRU5ErkJggg==","orcid":"","institution":"Nanjing University of Posts and Telecommunications, Jiangsu Nanjing","correspondingAuthor":true,"prefix":"","firstName":"Kexue","middleName":"","lastName":"Sun","suffix":""},{"id":483286210,"identity":"c0e655f2-f8ec-41ad-ba19-3c9673bd73a2","order_by":2,"name":"Zhipeng You","email":"","orcid":"","institution":"Nanjing University of Posts and Telecommunications, Jiangsu Nanjing","correspondingAuthor":false,"prefix":"","firstName":"Zhipeng","middleName":"","lastName":"You","suffix":""},{"id":483286215,"identity":"d740aea1-8d5e-4bab-8c30-617bf8f973c1","order_by":3,"name":"Lingqi Xiang","email":"","orcid":"","institution":"Nanjing University of Posts and Telecommunications, Jiangsu Nanjing","correspondingAuthor":false,"prefix":"","firstName":"Lingqi","middleName":"","lastName":"Xiang","suffix":""}],"badges":[],"createdAt":"2025-06-28 10:08:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6997041/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6997041/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11760-025-04775-4","type":"published","date":"2025-09-19T15:57:15+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":91889863,"identity":"34997229-1d6d-487b-b045-d1836739d40c","added_by":"auto","created_at":"2025-09-22 16:02:53","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":687660,"visible":true,"origin":"","legend":"","description":"","filename":"EAIYOLO.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6997041/v1_covered_9376838e-de96-4e01-862e-fa9610a7525f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"EAI-YOLO：Faster, and More Accurate for Real-Time Dynamic Object Detection","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":"signal-image-and-video-processing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sivp","sideBox":"Learn more about [Signal, Image and Video Processing](http://link.springer.com/journal/11760)","snPcode":"11760","submissionUrl":"https://submission.nature.com/new-submission/11760/3","title":"Signal, Image and Video Processing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Deep learning, dynamic object detection, YOLOv8s, ECA, AFPN, Inner-CIoU","lastPublishedDoi":"10.21203/rs.3.rs-6997041/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6997041/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn sports event scenarios, precise detection of dynamic objects is crucial for real-time monitoring and event analysis. However, traditional dynamic object detection methods face challenges such as small target size, similar appearances, disordered motion, and dense occlusions, which often lead to problems like false detections, low accuracy, and poor robustness. To address these challenges, this paper proposes a real-time dynamic object detection algorithm EAI-YOLO based on improved YOLOv8s. First, the Efficient Channel Attention (ECA) is introduced to enhance the capability of capturing detailed features of small-scale moving targets. Second, the traditional Path Aggregation Feature Pyramid Network (PAFPN) is replaced by an Adaptive Feature Pyramid Network (AFPN) to improve the detection performance for densely occluded objects. Finally, a scale-adaptive Inner-CIoU loss is designed to enhance the detection generalization ability for blurry targets and those with abrupt scale changes. Experimental results show that on the self-made dataset, EAI-YOLO achieves an [email protected] of 83.7% and an [email protected]\u0026ndash;0.95 of 45.2%, with an inference time of only 0.034 seconds per frame, balancing real-time performance and detection accuracy. This study overcomes the issues of insufficient shallow feature extraction and performance degradation in occluded scenes of existing methods in complex motion scenarios, providing a high-precision technical solution for scenarios such as real-time sports event monitoring, athlete motion analysis, and intelligent referee systems. 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