Modular Integrated Construction Detection Algorithm for Optimized YOLOv8

Modular Integrated Construction Detection Algorithm for Optimized 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 Article Modular Integrated Construction Detection Algorithm for Optimized YOLOv8 Xinqi Liu, Longlong Liao, Weifeng Zhou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4600387/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 Detecting module components at the factory is crucial for safety monitoring, quality control, and productivity enhancement. However, traditional detection methods are not cost-effective and also lack real-time performance. To address these challenges, this study proposes an improved YOLOv8 modular integrated construction detection algorithm, introducing the construction of a small object-YOLO that optimizes the YOLOv8 model by replacing the basic module with a new cross-stage partial network fusion module. In contrast, this module uses deformable convolution Networks v2 to handle geometric variations of objects and focus on relevant image regions. Additionally, the Wise-IoU strategy reduces the competitiveness of high-quality anchor boxes and harmful gradients generated by low-quality examples. The Multi-Head self-attention further improves detection accuracy by capturing the relationship between the image and important objects, making it more suitable for the modular integrated construction dataset. As construction images are often taken from a top or bird's-eye view, small objects can be difficult to detect. Therefore, this algorithm introduces a small object algorithm to enhance the model's ability to detect small objects. Experimental results demonstrate that the improved YOLOv8 model that effectively identifies moving objects. Compared to the original YOLOv8 model, the improved model achieves a 4.4% increase in mAP and a 4.3% increase in F1 score while reducing parameters by 54.05% and GFLOPs by 55.39%. The proposed algorithm will be a reference for automatic detection methods of modular integrated construction at the factory. Physical sciences/Engineering/Civil engineering Physical sciences/Mathematics and computing/Computer science YOLOv8 detection Construction factory Deformable Convolutional Network Small object 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-4600387","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":323698300,"identity":"cbf138a5-d7fc-4216-882b-1bfaf43dce38","order_by":0,"name":"Xinqi Liu","email":"","orcid":"","institution":"The University of Hong Kong","correspondingAuthor":false,"prefix":"","firstName":"Xinqi","middleName":"","lastName":"Liu","suffix":""},{"id":323698301,"identity":"40f390a6-ea5e-4b77-b334-72d0280ec137","order_by":1,"name":"Longlong Liao","email":"","orcid":"","institution":"Fu Zhou University","correspondingAuthor":false,"prefix":"","firstName":"Longlong","middleName":"","lastName":"Liao","suffix":""},{"id":323698302,"identity":"d783b8c8-5334-46a9-b5e8-579ca027fb06","order_by":2,"name":"Weifeng Zhou","email":"data:image/png;base64,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","orcid":"","institution":"The Hong Kong Polytechnic University Hong Kong","correspondingAuthor":true,"prefix":"","firstName":"Weifeng","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2024-06-18 13:39:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4600387/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4600387/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66201548,"identity":"dcfd0b51-4b02-4af6-8611-3c1b74f581fe","added_by":"auto","created_at":"2024-10-08 15:35:00","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1043408,"visible":true,"origin":"","legend":"","description":"","filename":"ModularIntegratedConstructiondetectionAlgorithmforOptimizedYOLOv8.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4600387/v1_covered_897773cd-8134-4afe-800f-38e8abbbb17a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Modular Integrated Construction Detection Algorithm for Optimized 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":true,"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":"YOLOv8, detection, Construction factory, Deformable Convolutional Network, Small object","lastPublishedDoi":"10.21203/rs.3.rs-4600387/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4600387/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDetecting module components at the factory is crucial for safety monitoring, quality control, and productivity enhancement. 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