Real-Time Object Detection with QuantizedYOLOv11 and YOLOv8 on Raspberry Pi 5 for Low-Speed ADAS

Real-Time Object Detection with QuantizedYOLOv11 and YOLOv8 on Raspberry Pi 5 for Low-Speed ADAS | 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 Real-Time Object Detection with QuantizedYOLOv11 and YOLOv8 on Raspberry Pi 5 for Low-Speed ADAS Furkan Şimşek This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8584571/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 This study investigates the object detection performance differences between YOLOv11 and YOLOv8 architectures for Advanced Driver Assistance Systems (ADAS) under CPU-only constraints. Speed and accuracy evaluations were conducted on a Raspberry Pi 5 (8 GB), with the CPU governor configured in performance mode to ensure stable benchmarking conditions. Since hardware accelerators such as GPUs or NPUs are not universally available in all vehicle platforms, this work focuses exclusively on CPU-based inference, which remains critical for practical ADAS deployment. Four models were evaluated: YOLOv11n, YOLOv11s, YOLOv8n, and YOLOv8s. Additionally, model optimization was performed using ONNX with INT8 post-training quantization to improve inference efficiency. Experimental results on the KITTI dataset demonstrate that the quantized YOLOv11n model achieved approximately 13 FPS with an average latency of 76.78 ms, whereas the YOLOv11s model achieved around 7 FPS with a latency of 152.20 ms. This corresponds to an approximately 46\% latency reduction for the nano model compared to the small variant. The findings indicate that nano-scale YOLO models provide a more favorable speed–accuracy trade-off for real-time, CPU-based ADAS applications on low-power embedded platforms. Artificial Intelligence and Machine Learning Deep Learning Object Detection ADAS INT8 Quantization Raspberry Pi 5 YOLOv11 Full Text Additional Declarations The authors declare no competing interests. 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. 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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-8584571","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":573463798,"identity":"d6c3a837-03e2-407c-9513-ef77b6cbd2c2","order_by":0,"name":"Furkan Şimşek","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYFAC5gMfGBgSwMwDQGxAhBa2xBmoWtgIauExhGthIEqLbnvPx2bePWny5gzsDw/d+GNjzCDf+wCvFrMzZzc28zzLMdzZwGNwOLctzYyBjR2/f8xu5G5/zHOggnHD/TcMh3MbDtswsBFwmdn9Nw+bgVrsNxxgf3A4589/IrTc4GEEaslJ3HCAweBwDtsBM8JazqQZNs45kJa84QDYL8nGbGxpBLQcP/yw4c2BZFugwx5/zvljZ9jPfAy/FkxAOCZHwSgYBaNgFBAEALgjSPMZ/Z3IAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0007-6540-6246","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Furkan","middleName":"","lastName":"Şimşek","suffix":""}],"badges":[],"createdAt":"2026-01-12 18:10:38","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8584571/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8584571/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100371028,"identity":"030679e4-e124-42b4-aabe-0bb28d7d3fb3","added_by":"auto","created_at":"2026-01-16 08:09:13","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":773993,"visible":true,"origin":"","legend":"","description":"","filename":"article.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8584571/v1_covered_042111dd-4a1c-4514-bfb0-dee2b1096fd3.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eReal-Time Object Detection with QuantizedYOLOv11 and YOLOv8 on Raspberry Pi 5 for Low-Speed ADAS\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Deep Learning, Object Detection, ADAS, INT8 Quantization, Raspberry Pi 5, YOLOv11","lastPublishedDoi":"10.21203/rs.3.rs-8584571/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8584571/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the object detection performance differences between YOLOv11 and YOLOv8 architectures for Advanced Driver Assistance Systems (ADAS) under CPU-only constraints. 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