Campus Risk Detection Using the S-YOLOv10-SIC Network and a Self-Calibrated Illumination Algorithm

Campus Risk Detection Using the S-YOLOv10-SIC Network and a Self-Calibrated Illumination Algorithm | 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 Campus Risk Detection Using the S-YOLOv10-SIC Network and a Self-Calibrated Illumination Algorithm Qiang Zhao, Sha Liu, Shihao Zhang, Baijuan Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5128936/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 8 You are reading this latest preprint version Abstract In order to realize intelligent and accurate campus risk detection, this paper proposes an improved YOLOv10 algorithm that integrates Self-Calibrated Illumination algorithm. The algo-rithm optimizes the loss function by introducing an auxiliary bounding box, and accelerates model convergence. StarNet is employed to enhance the original network structure, feature extraction capability, and decrease parameter count and calculations. The Convolutional Block Attention Module is incorporated into the small-object layer to boost network attention, subdue background noise, and enhance recognition accuracy and generalization capability. The Self-Calibrated Illumination algorithm is integrated to enhance the detection performance of the model under low light conditions. The experimental results show that compared with the original YOLOv10 network, the classification loss of the model generated by the improved algorithm is reduced by about 20%, the feature point loss is reduced by about 16%, and the Parameters, Gradients and GFLOPs are reduced by more than 80%. Precision, Recall, F1, and mAP all saw improvements, with increases of 0.99, 3.31, 2.15, and 1.23 percentage points respectively. The enhanced model excels at efficiently and accurately classifying and detecting campus risks in low-light environments. This model lays a solid foundation for the development of a smarter campus. Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Information technology campus risk detection improved YOLOv10 algorithm Self-Calibrated Illumination algorithm Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 07 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 09 May, 2025 Reviews received at journal 09 May, 2025 Reviews received at journal 07 May, 2025 Reviewers agreed at journal 30 Apr, 2025 Reviewers agreed at journal 30 Apr, 2025 Reviewers invited by journal 30 Apr, 2025 Submission checks completed at journal 18 Apr, 2025 First submitted to journal 07 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-5128936","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":450207993,"identity":"4ef9e184-194f-4c7c-aeea-9609e1755a5c","order_by":0,"name":"Qiang Zhao","email":"","orcid":"","institution":"Wuhan Donghu University","correspondingAuthor":false,"prefix":"","firstName":"Qiang","middleName":"","lastName":"Zhao","suffix":""},{"id":450207994,"identity":"b2534600-7285-4e15-b02d-eb6ca9c01744","order_by":1,"name":"Sha Liu","email":"","orcid":"","institution":"Wuhan Donghu University","correspondingAuthor":false,"prefix":"","firstName":"Sha","middleName":"","lastName":"Liu","suffix":""},{"id":450207995,"identity":"e96ece90-9624-400f-b62e-3b713710d3f1","order_by":2,"name":"Shihao Zhang","email":"","orcid":"","institution":"Wuhan Donghu University","correspondingAuthor":false,"prefix":"","firstName":"Shihao","middleName":"","lastName":"Zhang","suffix":""},{"id":450207996,"identity":"ca33ffdb-b564-44f6-9a67-a8d675889993","order_by":3,"name":"Baijuan Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCElEQVRIie3QMUsDMRTA8RwP0iV2ztFiv0JK4BQE9aOkHNgllY43HIUuuUV0bRc/hnNK4VxObj1wKTh6w4EgNxUfbg6XdiyYPySE8H5DQojPd6IBIRwXCXYqSc9pL7NHExBNkcs+K9Qx5Dcarg1MnvmtcI6L17ft5zy97OHBDhilU8OJIm3y0k2K+7urVc4BD0oyNpyZwdIGD8V7J4msjiSjSKwWMeN0ZoZWQWAcpKyR7JGUtdgyAVPKlXCTSsuPM4Ok0uPlSoE6SG6qOoL1I4ewqiVpbD42+Mkb11vCJy2/5t+LuF/qi3ayT0ejLNvs2qSbYJTjFv+5sq55DBrcrg8M+Xw+33/uB1DDVuYIk9jPAAAAAElFTkSuQmCC","orcid":"","institution":"Yunnan Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Baijuan","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-09-21 13:47:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5128936/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5128936/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-08924-0","type":"published","date":"2025-07-07T15:57:48+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86699430,"identity":"d72538c7-cb2d-4ff4-a34d-a75de51c1f80","added_by":"auto","created_at":"2025-07-14 16:09:33","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1362609,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5128936/v1_covered_6082c5dc-46cb-4019-8719-a6cab5270be9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Campus Risk Detection Using the S-YOLOv10-SIC Network and a Self-Calibrated Illumination Algorithm","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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"campus risk detection, improved YOLOv10 algorithm, Self-Calibrated Illumination algorithm","lastPublishedDoi":"10.21203/rs.3.rs-5128936/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5128936/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In order to realize intelligent and accurate campus risk detection, this paper proposes an improved YOLOv10 algorithm that integrates Self-Calibrated Illumination algorithm. 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