Real-Time Detection and Monitoring of Structural Cracks Using ConcreteCrack

Real-Time Detection and Monitoring of Structural Cracks Using ConcreteCrack | 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 Real-Time Detection and Monitoring of Structural Cracks Using ConcreteCrack Yanting Song, Lei Xing, Yunlong Song, Jian Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7767819/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 Structural crack detection is a critical task in infrastructure monitoring and maintenance, as early identification of cracks can prevent severe structural damage and reduce maintenance costs. In this work, we propose ConcreteCrack , a YOLOv11n-based detection framework enhanced with hierarchical feature extraction (HGStem), multi-scale feature fusion (HGBlock), and dynamic feature alignment (DynamicAlignFusion) modules to accurately detect cracks of varying sizes, shapes, and orientations. Extensive experiments on benchmark crack datasets demonstrate that our method outperforms several state-of-the-art object detection algorithms, achieving high Precision, Recall, F1-score, and mAP, while maintaining real-time inference speed. Furthermore, Grad-CAM visualizations validate the interpretability of the model by highlighting actual crack regions, ensuring reliable detection even in complex scenarios. The proposed approach provides a robust and efficient solution for automated structural crack monitoring, enabling safer and more effective infrastructure inspection. ConcreteCrack HGStem Deep learning Full Text Additional Declarations No competing interests reported. Supplementary Files RealTimeDetectionandMonitoringofStructuralCracksUsingConcreteCrack2.pdf 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-7767819","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":533085607,"identity":"7433201d-2d9e-4162-a900-b94158c5f04b","order_by":0,"name":"Yanting Song","email":"","orcid":"","institution":"Liaoning JinZhou","correspondingAuthor":false,"prefix":"","firstName":"Yanting","middleName":"","lastName":"Song","suffix":""},{"id":533085608,"identity":"c4db02af-f605-40a1-9c30-42daa3d71b61","order_by":1,"name":"Lei 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In this work, we propose \u003cb\u003eConcreteCrack\u003c/b\u003e, a YOLOv11n-based detection framework enhanced with hierarchical feature extraction (HGStem), multi-scale feature fusion (HGBlock), and dynamic feature alignment (DynamicAlignFusion) modules to accurately detect cracks of varying sizes, shapes, and orientations. Extensive experiments on benchmark crack datasets demonstrate that our method outperforms several state-of-the-art object detection algorithms, achieving high Precision, Recall, F1-score, and mAP, while maintaining real-time inference speed. Furthermore, Grad-CAM visualizations validate the interpretability of the model by highlighting actual crack regions, ensuring reliable detection even in complex scenarios. 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