YOLOv10n-SD:a novel real-time object detectionmodel for Driver distracted driving detection

YOLOv10n-SD:a novel real-time object detectionmodel for Driver distracted driving 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 YOLOv10n-SD:a novel real-time object detectionmodel for Driver distracted driving detection Yi Liu, QiaoXing Li, Lu Xiao, Sen Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6586232/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 Distracted driving is one of the leading causes of traffic accidents, and monitor-ing driver’s distracted states is crucial for improving road safety. In this paper,we adopt the Swin Transformer as the backbone network for YOLOv10. TheTransformer architecture effectively captures global dependencies between var-ious positions in the sequence, enhancing the model’s ability to capture globalobject relationships. Additionally, we design and implement a novel attentionmechanism module, DECS (Directional Enhanced Channel Spatial AttentionModule), to replace the SPA module in YOLOv10, which further strengthens themodel’s capability to identify critical features. We constructed a large-scale anddiverse driver monitoring image dataset,CBTDDD, which encompasses variousvehicle types, including buses, trucks, and sedans. Experimental results demon-strate that our model achieves a significant improvement of 4.3% in the mAP50metric, validating the effectiveness of integrating the Swin Transformer with theDECS module. This work provides a new technical pathway for distracted driv-ing detection, enhances detection accuracy and robustness, and holds substantialpractical application value. Distracted driving detection YOLOv10n multi-scale features global dependencies Swin Transformer 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-6586232","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":452861353,"identity":"fa3e2a3c-1df8-40a8-bba6-86bd93135e52","order_by":0,"name":"Yi Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIie3PsWrDMBCAYQlDvBxoVYb2GVQEXRzIkBfRLfFSQiBLhkJPBOwxqzPnBQKBzA6GZnHp6tF5A2cuhDqdOikaC9W/HdzHSYyFQn8wEXFSZjl6E4xTP0f3yTBf0byrp2ZI3JIXUfWJLpusMqr8OeNBWIN2D4Nqpj+3ecuWCVL8UToFL3ClAdLFc3PuH1anSDAzThJJzDTIhB+aoyWeVUgSlJMMJOZfoCK+L27k6kEAjvRUmAnuhO0JeRAZW1JdOdWy4bYw76nO4MVNxlXcKryOHsX61Hbda/Kwjms3+XXPMGZuv/Pc7xOl/24oFAr9r74B7Z1Oj6ftUucAAAAASUVORK5CYII=","orcid":"","institution":"Guizhou University","correspondingAuthor":true,"prefix":"","firstName":"Yi","middleName":"","lastName":"Liu","suffix":""},{"id":452861355,"identity":"8dcf3318-3ea5-40c6-b4c2-6b67963b6c2a","order_by":1,"name":"QiaoXing Li","email":"","orcid":"","institution":"Guizhou University","correspondingAuthor":false,"prefix":"","firstName":"QiaoXing","middleName":"","lastName":"Li","suffix":""},{"id":452861357,"identity":"43b76968-fef1-4e0c-b6ba-bdfeffe109ce","order_by":2,"name":"Lu Xiao","email":"","orcid":"","institution":"GuiZhou university of commerce","correspondingAuthor":false,"prefix":"","firstName":"Lu","middleName":"","lastName":"Xiao","suffix":""},{"id":452861358,"identity":"3bd04488-1f9d-4fa2-9e14-85a45627b9e4","order_by":3,"name":"Sen Zhang","email":"","orcid":"","institution":"The Key Laboratory of New Power System Operation Control of Guizhou Province","correspondingAuthor":false,"prefix":"","firstName":"Sen","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-05-04 01:53:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6586232/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6586232/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84508886,"identity":"bef1577a-b8b4-44bd-b60d-7c00d7fe8080","added_by":"auto","created_at":"2025-06-12 20:01:42","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3349490,"visible":true,"origin":"","legend":"","description":"","filename":"YOLOv10nSD.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6586232/v1_covered_daf01ae1-8340-4205-9eab-aa44382af67a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"YOLOv10n-SD:a novel real-time object detectionmodel for Driver distracted driving detection","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":"Distracted driving detection,YOLOv10n, multi-scale features, global dependencies, Swin Transformer","lastPublishedDoi":"10.21203/rs.3.rs-6586232/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6586232/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Distracted driving is one of the leading causes of traffic accidents, and monitor-ing driver’s distracted states is crucial for improving road safety. In this paper,we adopt the Swin Transformer as the backbone network for YOLOv10. TheTransformer architecture effectively captures global dependencies between var-ious positions in the sequence, enhancing the model’s ability to capture globalobject relationships. Additionally, we design and implement a novel attentionmechanism module, DECS (Directional Enhanced Channel Spatial AttentionModule), to replace the SPA module in YOLOv10, which further strengthens themodel’s capability to identify critical features. We constructed a large-scale anddiverse driver monitoring image dataset,CBTDDD, which encompasses variousvehicle types, including buses, trucks, and sedans. Experimental results demon-strate that our model achieves a significant improvement of 4.3% in the mAP50metric, validating the effectiveness of integrating the Swin Transformer with theDECS module. This work provides a new technical pathway for distracted driv-ing detection, enhances detection accuracy and robustness, and holds substantialpractical application value.","manuscriptTitle":"YOLOv10n-SD:a novel real-time object detectionmodel for Driver distracted driving detection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-12 07:55:57","doi":"10.21203/rs.3.rs-6586232/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":"b116b254-d068-4aaf-8da8-9074a49922f3","owner":[],"postedDate":"May 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-12T19:53:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-12 07:55:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6586232","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6586232","identity":"rs-6586232","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}