Driver Drowsiness Shield (DDSH): A Real-time Driver Drowsiness Detection System

Driver Drowsiness Shield (DDSH): A Real-time Driver Drowsiness Detection System | 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 Driver Drowsiness Shield (DDSH): A Real-time Driver Drowsiness Detection System Archita Bhanja, Dibyajyoti Parhi, Dipankar Gajendra, Kreetish Sinha, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5998363/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 May, 2025 Read the published version in ROBOMECH Journal → Version 1 posted 8 You are reading this latest preprint version Abstract Detecting drowsiness is crucial for improving traffic safety and preventing fatigue-related accidents. This paper aims to develop an advanced real-time drowsiness detection system using deep learning algorithms. For this purpose, we utilized an eye image dataset from the MRL Eye Dataset and performed extensive feature engineering and preprocessing to prepare the data for analysis. An algorithm has been proposed to classify eye states as open or closed using Transfer Learning based on the MobileNet architecture. Using a balanced dataset, the model was trained to distinguish between open and closed eyes accurately. To further validate the system, we integrated the trained model into a real-time camera application that monitors the eye conditions of drivers. The application analyzes real-time video streams, detects faces and eyes, and uses instances of closed eyelids to predict signs of drowsiness. The efficiency of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score. The results indicate that our approach accurately identifies fatigue indicators, presenting a viable solution for real-time drowsiness monitoring to help prevent accidents caused by exhaustion. Future studies will explore incorporating additional physiological information and applying advanced deep-learning techniques to enhance detection accuracy and system robustness. MobileNet Transfer Learning Deep Learning Drowsiness Detection CNN Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 15 May, 2025 Read the published version in ROBOMECH Journal → Version 1 posted Editorial decision: Accepted 28 Apr, 2025 Reviews received at journal 26 Apr, 2025 Reviews received at journal 22 Apr, 2025 Reviewers agreed at journal 22 Apr, 2025 Reviewers agreed at journal 22 Apr, 2025 Reviewers invited by journal 22 Apr, 2025 Submission checks completed at journal 19 Apr, 2025 First submitted to journal 18 Apr, 2025 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-5998363","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":446303927,"identity":"ee21e8f3-ba00-41a1-bc39-b36389ed531e","order_by":0,"name":"Archita Bhanja","email":"","orcid":"","institution":"Siksha ‘O’ Anusandhan (Deemed to be) University","correspondingAuthor":false,"prefix":"","firstName":"Archita","middleName":"","lastName":"Bhanja","suffix":""},{"id":446303928,"identity":"249ad456-c392-43c9-9a4d-30d70bf0b19f","order_by":1,"name":"Dibyajyoti Parhi","email":"","orcid":"","institution":"Siksha ‘O’ Anusandhan (Deemed to be) University","correspondingAuthor":false,"prefix":"","firstName":"Dibyajyoti","middleName":"","lastName":"Parhi","suffix":""},{"id":446303929,"identity":"1375e590-bbad-4fcb-be66-bbf7c96b5594","order_by":2,"name":"Dipankar Gajendra","email":"","orcid":"","institution":"Siksha ‘O’ Anusandhan (Deemed to be) University","correspondingAuthor":false,"prefix":"","firstName":"Dipankar","middleName":"","lastName":"Gajendra","suffix":""},{"id":446303931,"identity":"18b13eea-ab12-4c35-9808-360bb2a74cd1","order_by":3,"name":"Kreetish Sinha","email":"","orcid":"","institution":"Siksha ‘O’ Anusandhan (Deemed to be) University","correspondingAuthor":false,"prefix":"","firstName":"Kreetish","middleName":"","lastName":"Sinha","suffix":""},{"id":446303934,"identity":"16f67569-09ce-4ca1-be19-1aa59c86c705","order_by":4,"name":"Arup Kumar Sahoo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYJACxgYIzQbENjBBCaK1pJGu5TBhR/HPPmP2cGbbNnnz/sPPHnyoOG/Pz36A8cMPBos8XFokzuWYG25su20450aaueGMM7cTZ/YkMEv2MEgU47TmDI+Z5MO224wzJBjMpHnbbicYHEhgkAaaldiAQ4c8VIv9DP7j34BaztkbnH/A/BufFgOQFqDDEmcw5IBsOcC44UYCG15bDM+wlRvOOHc7eYZETpnkjDPJiTNnPGyz7DHArUXuDPO2hz1lt22BDtsm8aHCzp6fP/nwjR8VdTi1MECiAwWA4skAt3psWkbBKBgFo2AUoAIAz6ZVSqBegHMAAAAASUVORK5CYII=","orcid":"","institution":"Siksha ‘O’ Anusandhan (Deemed to be) University","correspondingAuthor":true,"prefix":"","firstName":"Arup","middleName":"Kumar","lastName":"Sahoo","suffix":""}],"badges":[],"createdAt":"2025-02-10 10:53:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5998363/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5998363/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40648-025-00307-4","type":"published","date":"2025-05-15T15:57:49+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83067827,"identity":"31d0e038-7d9a-4ae3-a534-2848b6a42d19","added_by":"auto","created_at":"2025-05-19 16:06:43","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":481382,"visible":true,"origin":"","legend":"","description":"","filename":"Robomech.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5998363/v1_covered_f141b971-b097-4b53-af4f-ccc17fe61153.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Driver Drowsiness Shield (DDSH): A Real-time Driver Drowsiness Detection System","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"robomech-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"robo","sideBox":"Learn more about [ROBOMECH Journal](http://robomechjournal.springeropen.com/)","snPcode":"40520","submissionUrl":"https://submission.nature.com/new-submission/40520/3","title":"ROBOMECH Journal","twitterHandle":"@SpringerEng","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"MobileNet, Transfer Learning, Deep Learning, Drowsiness Detection, CNN","lastPublishedDoi":"10.21203/rs.3.rs-5998363/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5998363/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Detecting drowsiness is crucial for improving traffic safety and preventing fatigue-related accidents. 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