YOLOv8-BCC: Lightweight Object Detection Model Boosts Urban Traffic Safety

YOLOv8-BCC: Lightweight Object Detection Model Boosts Urban Traffic Safety | 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 YOLOv8-BCC: Lightweight Object Detection Model Boosts Urban Traffic Safety Tang Jun, Zhouxian Lai, Caixian Ye, lijun Xu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4148973/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 With the rapid development of urbanization, the role of urban transportation systems has become increasingly prominent. However, traditional methods of traffic management are struggling to cope with the growing demands of traffic and the complexity of urban environments. In response to this situation, we propose the YOLOv8-BCC algorithm to address existing shortcomings. Leveraging advanced technologies such as CFNet, CBAM attention modules, and BIFPN structure, our algorithm aims to enhance the accuracy, real-time performance, and adaptability of urban traffic intelligent detection systems. Experimental results demonstrate significant improvements in detection accuracy and real-time performance compared to traditional methods. The introduction of the YOLOv8-BCC algorithm provides a robust solution for enhancing urban traffic safety and intelligent management. Physical sciences/Mathematics and computing/Computer science Physical sciences/Engineering/Electrical and electronic engineering Urban Transportation Intelligent Detection Systems YOLOv8 Traffic Management Real-time Performance CBAM 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-4148973","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":287157159,"identity":"f26648a0-4f23-438f-bebd-ee00f21ad824","order_by":0,"name":"Tang Jun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYLACCQYGORABAQcIqOaBajEmUQsQJDYQrcWe/ezhF5Ztd9I33G5/JvmzjUGO70YC4+cCfLbw5KVZSLY9y50554yZNG8bg7HkjQRm6Rl4HZZjZiDZdji3XyKHTZqxjSFxw40ENmYefFr434C1pLNJpIMdVk9Yi0SO8QOglgR+iQQzCaDDEgwIarnxxoxB4txhw5kzcoytec5JGM4887BZGp8W9v4c488SZYflDW6kP7z5o8xGnu948sHP+LQAAZs0NEpYJMCxysDYgF8DAwPzxw9QxgdCSkfBKBgFo2BkAgBDqEe6z/RLiwAAAABJRU5ErkJggg==","orcid":"","institution":"Guangzhou Xinhua University","correspondingAuthor":true,"prefix":"","firstName":"Tang","middleName":"","lastName":"Jun","suffix":""},{"id":287157160,"identity":"57c974ac-272d-4f7c-b530-90ef0a3da6a2","order_by":1,"name":"Zhouxian Lai","email":"","orcid":"","institution":"Guangzhou Business School","correspondingAuthor":false,"prefix":"","firstName":"Zhouxian","middleName":"","lastName":"Lai","suffix":""},{"id":287157161,"identity":"f5ee2344-cb7e-498d-97ae-3a6fe53d284d","order_by":2,"name":"Caixian Ye","email":"","orcid":"","institution":"Guangzhou Xinhua University","correspondingAuthor":false,"prefix":"","firstName":"Caixian","middleName":"","lastName":"Ye","suffix":""},{"id":287157162,"identity":"decde901-11d3-4d97-aa68-271361c391ca","order_by":3,"name":"lijun Xu","email":"","orcid":"","institution":"Guangzhou Xinhua University","correspondingAuthor":false,"prefix":"","firstName":"lijun","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2024-03-22 10:24:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4148973/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4148973/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60463594,"identity":"6cd93acb-7325-45bf-aa67-4101accddb60","added_by":"auto","created_at":"2024-07-17 04:29:28","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":908050,"visible":true,"origin":"","legend":"","description":"","filename":"latex.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4148973/v1_covered_a4ba3d2f-12f1-4453-a4f8-1443a6c37259.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"YOLOv8-BCC: Lightweight Object Detection Model Boosts Urban Traffic Safety","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":"Urban Transportation, Intelligent Detection Systems, YOLOv8, Traffic Management, Real-time Performance, CBAM","lastPublishedDoi":"10.21203/rs.3.rs-4148973/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4148973/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith the rapid development of urbanization, the role of urban transportation systems has become increasingly prominent. However, traditional methods of traffic management are struggling to cope with the growing demands of traffic and the complexity of urban environments. In response to this situation, we propose the YOLOv8-BCC algorithm to address existing shortcomings. Leveraging advanced technologies such as CFNet, CBAM attention modules, and BIFPN structure, our algorithm aims to enhance the accuracy, real-time performance, and adaptability of urban traffic intelligent detection systems. Experimental results demonstrate significant improvements in detection accuracy and real-time performance compared to traditional methods. The introduction of the YOLOv8-BCC algorithm provides a robust solution for enhancing urban traffic safety and intelligent management.\u003c/p\u003e","manuscriptTitle":"YOLOv8-BCC: Lightweight Object Detection Model Boosts Urban Traffic Safety","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-05 13:01:09","doi":"10.21203/rs.3.rs-4148973/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":"3a506c15-2009-4013-b122-7581f0f2b638","owner":[],"postedDate":"April 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":30227282,"name":"Physical sciences/Mathematics and computing/Computer science"},{"id":30227283,"name":"Physical sciences/Engineering/Electrical and electronic engineering"}],"tags":[],"updatedAt":"2024-07-17T04:21:21+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-05 13:01:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4148973","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4148973","identity":"rs-4148973","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}