Cheating Detection in Examinations Using YOLOv11: A Comprehensive Real-Time Object Detection Framework for Enhancing Academic Integrity

Cheating Detection in Examinations Using YOLOv11: A Comprehensive Real-Time Object Detection Framework for Enhancing Academic Integrity | 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 Cheating Detection in Examinations Using YOLOv11: A Comprehensive Real-Time Object Detection Framework for Enhancing Academic Integrity Seung Jin . Kim, Myuhng Joo . Kim This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6376844/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 Academic integrity in examinations is crucial forensuring fair and credible assessments, yet cheating remainsa pervasive challenge in educational institutions. Traditionalinvigilation methods, which rely on human proctors, are ofteninadequate due to scalability issues and human error. Thispaper proposes a novel real-time cheating detection systemusing YOLOv11, a state-of-the-art object detection model, tomonitor exam environments. Our system identifies six distinctbehaviors: examiner, leaning to copy, looking around, normalsitting, talking and cheating, and walking. We trained YOLOv11on a custom dataset of 5,000 exam images, achieving a meanAverage Precision (mAP) of 0.991 at a confidence threshold of0.5. The system’s performance was comprehensively evaluatedusing training and validation losses, precision-recall curves, F1-score curves, confusion matrices, and real-time detection resultsat 30 frames per second (FPS). Detection results on real examimages demonstrate high confidence in identifying key behaviors,with scores ranging from 0.9 to 1.0 for most classes. The proposedsystem offers a scalable, non-intrusive, and efficient solution forenhancing academic integrity, making it suitable for large-scaleexam settings. Future improvements include addressing classimbalance, incorporating temporal analysis, and deploying thesystem in diverse real-world environments. Cheating Detection YOLOv11 Object Detec-tion Exam Monitoring Computer Vision Deep Learning Aca-demic Integrity Real-Time Systems Full Text Additional Declarations Competing interest reported. The authors declare no competing interests. No funding, personal relationships, or affiliations exist that could have influenced the work reported in this manuscript titled 'Cheating Detection in Examinations Using YOLOv11: A Comprehensive Real-Time Object Detection Framework for Enhancing Academic Integrity 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-6376844","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":438509185,"identity":"895f3e02-7477-4613-ba4d-f146d0b210f2","order_by":0,"name":"Seung Jin . 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