Robust Object Detection in Industrial Safety Environments Using YOLOv8

Robust Object Detection in Industrial Safety Environments Using YOLOv8 | 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Article Robust Object Detection in Industrial Safety Environments Using YOLOv8 Hani Siraj This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7293213/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 In industrial and safety-critical environments, real-time object detection plays a crucial role in hazard prevention and operational efficiency. This shared task presents a high-performance object detection pipeline using YOLOv8 to identify three critical object categories: Fire Extinguishers, Toolboxes, and Oxygen Tanks. The system was trained and fine-tuned on a dataset of 1,139 annotated images with a balanced class distribution. The project focused on optimizing model performance through data augmentation, hyperparameter tuning, and occlusion handling. A final [email protected] score of 85.1% was achieved. The study addresses key challenges such as overfitting, occlusion-based misclassification, and lighting sensitivity. This work demonstrates an effective approach to deploying lightweight yet accurate object detection models in real-world industrial settings. 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-7293213","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":499029033,"identity":"2bb56ee4-4530-4f46-99b9-bd17d05e3c46","order_by":0,"name":"Hani Siraj","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYBACCfbmg4//GEjIgXk8IBGCWniOJRvwFNgYk6BFIsdMgudDWmID0VokGxKMDSQMDqdvl0hgfPC2jSFPsoGAFmmGA4kPDAwO5+6ckcBsOLeNoViakC1yjA2HDRKAWjbcSGCT5m1jSJxHUAszY5vEAaDDDG4ksP8mSos0GzObZINBWgJQCxszSMtsQloke9iYjRkMbAw3nHnYLDnnnEQxQe9L3H//8THDHwl5g+PJBz+8KbPJkzhAyBoEYAQZL5FAvAYYIEPLKBgFo2AUDHcAACtcPfpV2oCEAAAAAElFTkSuQmCC","orcid":"","institution":"National University of Computer and Emerging Sciences","correspondingAuthor":true,"prefix":"","firstName":"Hani","middleName":"","lastName":"Siraj","suffix":""}],"badges":[],"createdAt":"2025-08-04 16:38:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7293213/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7293213/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102598063,"identity":"a976e23e-6a92-4300-b01b-722377febb0f","added_by":"auto","created_at":"2026-02-13 12:27:12","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1340407,"visible":true,"origin":"","legend":"","description":"","filename":"RobustObjectDetectioninIndustrialSafetyEnvironmentsUsingYOLOv8.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7293213/v1_covered_ee819c60-869d-4b75-bf80-e1735411c06d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Robust Object Detection in Industrial Safety Environments Using YOLOv8","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":"","lastPublishedDoi":"10.21203/rs.3.rs-7293213/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7293213/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In industrial and safety-critical environments, real-time object detection plays a crucial role in hazard prevention and operational efficiency. 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