Robust Tracking of Vibrating Rods for Precast Beams via Visual Fusion and Online Self-Correction

Robust Tracking of Vibrating Rods for Precast Beams via Visual Fusion and Online Self-Correction | 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 Tracking of Vibrating Rods for Precast Beams via Visual Fusion and Online Self-Correction Zhiping Lin, Guanguo Liu, Zhenghong Tian, Guanglei Liang, Zhiying Tan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8498692/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract The compactness of concrete vibration in precast box beams is a critical determinant of structural durability. Traditional manufacturing processes, however, rely heavily on manual experience and lack precise positioning and quantitative standards, frequently leading to defects such as missed vibration and under-vibration. Achieving precise positioning of vibrating rods presents a significant technical challenge in indoor precasting environments characterized by GNSS-denied conditions, dense reinforcement, and dust occlusion. To address these issues, this study proposes and validates a high-precision positioning and dynamic identification system for vibrating equipment based on multi-modal sensor fusion. This system effectively resolves the challenges of field-of-view coverage and dynamic occlusion across a standard 30-meter precasting pedestal. At the algorithmic level, a two-layer coordinate transformation framework comprising offline calibration and online correction is established. Simultaneously, the YOLOv13 deep learning network is employed for real-time object detection. By integrating point cloud filtering and plane fitting techniques, the system calculates the three-dimensional coordinates and spatial pose of the vibrating rod within the box beam coordinate system. Experimental results demonstrate the system's exceptional robustness in complex, real-world beam plant environments. The dynamic tracking refresh rate exceeds 10 Hz, with both static and dynamic positioning errors maintained within the centimeter-level range. This research holds significant theoretical importance and engineering application value for promoting the intelligent transformation of precast beam production. Vibration compactness Precise positioning Dynamic identification Camera fusion Global tracking Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 23 Feb, 2026 Reviews received at journal 22 Feb, 2026 Reviews received at journal 15 Feb, 2026 Reviewers agreed at journal 02 Feb, 2026 Reviewers agreed at journal 27 Jan, 2026 Reviewers invited by journal 27 Jan, 2026 Editor assigned by journal 02 Jan, 2026 Submission checks completed at journal 02 Jan, 2026 First submitted to journal 02 Jan, 2026 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-8498692","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":581487966,"identity":"d5d4bded-643c-4721-93e3-c2f1d2117ced","order_by":0,"name":"Zhiping Lin","email":"","orcid":"","institution":"Fujian Expressway Group Co.,L td","correspondingAuthor":false,"prefix":"","firstName":"Zhiping","middleName":"","lastName":"Lin","suffix":""},{"id":581487967,"identity":"60407bc2-ac09-44db-80f5-61799df30538","order_by":1,"name":"Guanguo 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