UAVOD-Net: A Lightweight Yolov11n-Bob Cat Optimization Framework for High Precision Object Detection in Aerial Imagery

UAVOD-Net: A Lightweight Yolov11n-Bob Cat Optimization Framework for High Precision Object Detection in Aerial Imagery | 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 UAVOD-Net: A Lightweight Yolov11n-Bob Cat Optimization Framework for High Precision Object Detection in Aerial Imagery Dama Durga Bhavani, Usha Rani Nelakuditi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8170533/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 The rapid deployment of Unmanned Aerial Vehicles (UAVs) for tasks such as traffic monitoring, agricultural inspection, and public safety has significantly increased the volume of aerial visual data, yet efficient, real-time object detection remains a critical challenge, especially in complex urban environments. Existing studies show that object detection models often struggle with occlusion, small object sizes, and varying lighting conditions prevalent in drone-captured imagery, leading to sub-optimal performance and higher false detection rates. To address these limitations, this work proposes an enhanced UAV Object Detection (UAVOD-Net) framework leveraging the VisDrone dataset. Robust image preprocessing techniques are applied to improve contrast, remove noise, and standardize input dimensions, enhancing feature clarity. For detection, the You Only Look at Once with Version-11 based Nano variant (YOLOv11n) model, known for its lightweight architecture and high-speed processing, is deployed to efficiently localize objects such as pedestrians, vehicles, and bicycles. Further performance improvement is achieved through the integration of the Bob Cat Optimization (BCO) algorithm, a nature-inspired metaheuristic approach designed to optimize network weights and hyperparameters. BCO enhances convergence speed, reduces model loss, and improves detection accuracy under challenging conditions like occlusions and variable scales. This combined methodology significantly boosts object detection precision and reliability for UAV-based surveillance and monitoring applications. The proposed UAVOD-Net achieved an overall Precision of 0.594, Recall of 0.485, mAP@50 of 0.516, and mAP@50–95 of 0.326 for all object classes. These results demonstrate improved detection accuracy and robustness for UAV-based aerial imagery. Furthermore, the same UAVOD-Net applied on Detection in Adverse Weather Nature (DAWN) dataset, where proposed UAVOD-Net resulted in superior object detection and classification performance in presence of adverse weather conditions compared to existing approaches. Physical sciences/Engineering Physical sciences/Mathematics and computing VisDrone dataset UAV object detection aerial imagery YOLOv11n Bob Cat Optimization loss reduction real-time detection image preprocessing metaheuristic optimization drone surveillance 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. 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-8170533","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":558152429,"identity":"2601e578-5698-4ec1-994d-80ba3639715f","order_by":0,"name":"Dama Durga 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Imagery","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":"VisDrone dataset, UAV object detection, aerial imagery, YOLOv11n, Bob Cat Optimization, loss reduction, real-time detection, image preprocessing, metaheuristic optimization, drone surveillance","lastPublishedDoi":"10.21203/rs.3.rs-8170533/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8170533/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe rapid deployment of Unmanned Aerial Vehicles (UAVs) for tasks such as traffic monitoring, agricultural inspection, and public safety has significantly increased the volume of aerial visual data, yet efficient, real-time object detection remains a critical challenge, especially in complex urban environments. Existing studies show that object detection models often struggle with occlusion, small object sizes, and varying lighting conditions prevalent in drone-captured imagery, leading to sub-optimal performance and higher false detection rates. To address these limitations, this work proposes an enhanced UAV Object Detection (UAVOD-Net) framework leveraging the VisDrone dataset. Robust image preprocessing techniques are applied to improve contrast, remove noise, and standardize input dimensions, enhancing feature clarity. For detection, the You Only Look at Once with Version-11 based Nano variant (YOLOv11n) model, known for its lightweight architecture and high-speed processing, is deployed to efficiently localize objects such as pedestrians, vehicles, and bicycles. Further performance improvement is achieved through the integration of the Bob Cat Optimization (BCO) algorithm, a nature-inspired metaheuristic approach designed to optimize network weights and hyperparameters. BCO enhances convergence speed, reduces model loss, and improves detection accuracy under challenging conditions like occlusions and variable scales. This combined methodology significantly boosts object detection precision and reliability for UAV-based surveillance and monitoring applications. The proposed UAVOD-Net achieved an overall Precision of 0.594, Recall of 0.485, mAP@50 of 0.516, and mAP@50\u0026ndash;95 of 0.326 for all object classes. These results demonstrate improved detection accuracy and robustness for UAV-based aerial imagery. Furthermore, the same UAVOD-Net applied on Detection in Adverse Weather Nature (DAWN) dataset, where proposed UAVOD-Net resulted in superior object detection and classification performance in presence of adverse weather conditions compared to existing approaches.\u003c/p\u003e","manuscriptTitle":"UAVOD-Net: A Lightweight Yolov11n-Bob Cat Optimization Framework for High Precision Object Detection in Aerial Imagery","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-15 15:30:20","doi":"10.21203/rs.3.rs-8170533/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"92dc606f-6f27-4304-a6f8-4ab7efac4962","owner":[],"postedDate":"December 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":59411684,"name":"Physical sciences/Engineering"},{"id":59411685,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2025-12-29T05:39:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-15 15:30:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8170533","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8170533","identity":"rs-8170533","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}