Advancing UAV Multi-Object Tracking: Integrating YOLOv8, Nano Instance Segmentation, and Dueling Double Deep Q-Network

Advancing UAV Multi-Object Tracking: Integrating YOLOv8, Nano Instance Segmentation, and Dueling Double Deep Q-Network | 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 Advancing UAV Multi-Object Tracking: Integrating YOLOv8, Nano Instance Segmentation, and Dueling Double Deep Q-Network R Kiruthiga, Nithya B, Martin Prabhu S This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4854100/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 Unmanned Aerial Vehicles (UAVs) have become indispensable for navigating complex terrains, accessing remote or hazardous locations, and capturing high-resolution imagery. This paper presents an innovative approach to object detection specifically tailored for computer vision applications in UAVs. Traditional deep learning models such as RCNN, Fast RCNN, and YOLO often face challenges in detecting occluded, blurred, or clustered objects and struggle with simultaneously identifying and tracking multiple objects. To overcome these challenges, we propose a framework that integrates YOLOv8x, Nano Instance Segmentation (NIS), and Dueling Double Deep Q Network (DDDQN). YOLOv8x exhibits outstanding performance, achieving an Average Precision (AP) of 53.9% on the demanding MSCOCO dataset, outperforming previous versions. The DDDQN algorithm significantly enhances tracking capabilities by effectively estimating state values and state-dependent action advantages independently. The combination of YOLOv8x and DDDQN facilitates proficient management of obstacles, varying object sizes, and unpredictable movements. We simulated the proposed framework using the UAVDT and VisDrone datasets and compared its performance against approximately nine contemporary frameworks from recent literature. The results demonstrate that our framework significantly improves 1 object tracking in densely populated environments, offering a robust solution for real-world applications requiring precise and resilient object detection. UAVs Object Tracking Detection YOLOV8 Nano Instance Segmentation DDDQN 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-4854100","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":338873005,"identity":"75ca1ed0-25f1-4044-a079-4bf24d75653f","order_by":0,"name":"R Kiruthiga","email":"data:image/png;base64,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","orcid":"","institution":"National Institute of Technology Tiruchirappalli","correspondingAuthor":true,"prefix":"","firstName":"R","middleName":"","lastName":"Kiruthiga","suffix":""},{"id":338873007,"identity":"ba4546da-7250-45b6-989a-3f17cd701203","order_by":1,"name":"Nithya B","email":"","orcid":"","institution":"National Institute of Technology Tiruchirappalli","correspondingAuthor":false,"prefix":"","firstName":"Nithya","middleName":"","lastName":"B","suffix":""},{"id":338873009,"identity":"6f1f9823-cac8-4baf-83eb-cfb8bb7d63c4","order_by":2,"name":"Martin Prabhu S","email":"","orcid":"","institution":"St.Joseph’s College","correspondingAuthor":false,"prefix":"","firstName":"Martin","middleName":"Prabhu","lastName":"S","suffix":""}],"badges":[],"createdAt":"2024-08-03 15:59:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4854100/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4854100/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64002870,"identity":"02abd6e5-5fcd-4892-a0c4-d26b4aced8d3","added_by":"auto","created_at":"2024-09-04 20:36:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1155423,"visible":true,"origin":"","legend":"","description":"","filename":"MV.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4854100/v1_covered_e8a0c67d-8687-4a00-ada9-5936f344c67d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Advancing UAV Multi-Object Tracking: Integrating YOLOv8, Nano Instance Segmentation, and Dueling Double Deep Q-Network","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":"UAVs, Object Tracking, Detection, YOLOV8, Nano Instance Segmentation, DDDQN","lastPublishedDoi":"10.21203/rs.3.rs-4854100/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4854100/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Unmanned Aerial Vehicles (UAVs) have become indispensable for navigating complex terrains, accessing remote or hazardous locations, and capturing high-resolution imagery. This paper presents an innovative approach to object detection specifically tailored for computer vision applications in UAVs. Traditional deep learning models such as RCNN, Fast RCNN, and YOLO often face challenges in detecting occluded, blurred, or clustered objects and struggle with simultaneously identifying and tracking multiple objects. To overcome these challenges, we propose a framework that integrates YOLOv8x, Nano Instance Segmentation (NIS), and Dueling Double Deep Q Network (DDDQN). YOLOv8x exhibits outstanding performance, achieving an Average Precision (AP) of 53.9% on the demanding MSCOCO dataset, outperforming previous versions. The DDDQN algorithm significantly enhances tracking capabilities by effectively estimating state values and state-dependent action advantages independently. The combination of YOLOv8x and DDDQN facilitates proficient management of obstacles, varying object sizes, and unpredictable movements. 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