Deep learning-based particle tracking velocimetry (PTV) for spherical and non-spherical particles: Application to sediment transport

Deep learning-based particle tracking velocimetry (PTV) for spherical and non-spherical particles: Application to sediment transport | 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 Deep learning-based particle tracking velocimetry (PTV) for spherical and non-spherical particles: Application to sediment transport Wenzheng Su, Lu Jing, Mengzhen Xu, Xudong Fu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9184032/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 Particle-resolved measurements in physical experiments are crucial to the study of sediment transport. Conventional particle tracking velocimetry (PTV) relies on template matching of simple shapes via intensity thresholding and nearest-neighbor linking. However, it fails in dense particle systems where particles overlap, rotate, and vary in their apparent shapes. Here, we propose a novel computer vision-based PTV combining the YOLO algorithm for arbitrary object detection and the Kalman filter for robust data association under occlusion and noise. The framework captures particle-scale kinematics and leads to accurate measurements of the flow-depth profiles of the particle shear rate (γ ̇), rotational velocity (ω), and granular temperature (T), under various transport conditions, for idealized and natural particle shapes. Both T and ω show a clear dependence on γ ̇, consistent with predictions from granular flow theory. Moreover, comparing to spheres, irregular particles exhibit steeper T–γ ̇ and ω–γ ̇ relationships, indicating greater collisional agitation, rotation, and flow energy dissipation. These granular shape effects are more significant as the Shields number is increased, highlighting the microscopic control of particle shape on bedload dynamics. The proposed PTV technique and its potential applications are expected to provide more inslights into the fundamental mechanics of sediment transport in future research. Geophysics sediment transport particle tracking granular flow Full Text Additional Declarations The authors declare no competing interests. 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. 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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-9184032","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":609785497,"identity":"5a692ff2-d76d-47de-b7cf-164fa3f7a97c","order_by":0,"name":"Wenzheng Su","email":"","orcid":"","institution":"Tsinghua university","correspondingAuthor":false,"prefix":"","firstName":"Wenzheng","middleName":"","lastName":"Su","suffix":""},{"id":609785624,"identity":"c1891a9d-1dce-4c66-912d-543d4e60f518","order_by":1,"name":"Lu Jing","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDklEQVRIiWNgGAWjYBACPgaGhAMMDDYQXgIYAQEPHi1sEC1pQCYz8VpA4DBECwNRWiQSHh4u+HXeXr6B//CHhztq8wyOH2B88LaNQd4ct5aEwzP7bic2NjCzSSSeOV5scCaB2XBuG4PhzgY8Wnh7bicwMzCzMSS2HUvccCCBTZq3jSHB4ABeLefs2RiYmT+AtZx/wP6boBaeHwcYe4Del0hsq0nccCOBjRmvFp4HQFsakhNnMDObAbUcSJx542Gz5JxzEoYbcGjhZ89J/szzx85evr3x8cefbXWJfeeTD354U2Yjj8sWYBQkMDC2McCiBRRBjA1AQgKXeiBgBxr2B86rw6NyFIyCUTAKRioAAEkmXcMx7nitAAAAAElFTkSuQmCC","orcid":"","institution":"Tsinghua university","correspondingAuthor":true,"prefix":"","firstName":"Lu","middleName":"","lastName":"Jing","suffix":""},{"id":609785625,"identity":"9d716f4b-488e-4898-9d14-99f2e1db3d8e","order_by":2,"name":"Mengzhen Xu","email":"","orcid":"","institution":"Tsinghua university","correspondingAuthor":false,"prefix":"","firstName":"Mengzhen","middleName":"","lastName":"Xu","suffix":""},{"id":609785626,"identity":"a9b5252e-8a44-4fd3-b621-d36632784de0","order_by":3,"name":"Xudong Fu","email":"","orcid":"","institution":"Tsinghua university","correspondingAuthor":false,"prefix":"","firstName":"Xudong","middleName":"","lastName":"Fu","suffix":""}],"badges":[],"createdAt":"2026-03-21 07:36:37","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9184032/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9184032/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105565148,"identity":"5a203ce9-92ce-423f-b6a7-1f4dc8891623","added_by":"auto","created_at":"2026-03-27 12:52:07","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1889034,"visible":true,"origin":"","legend":"","description":"","filename":"Preprintmanuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9184032/v1_covered_74a87499-d221-455f-a4d0-1ed214fb4cf1.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eDeep learning-based particle tracking velocimetry (PTV) for spherical and non-spherical particles: Application to sediment transport\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Tsinghua University","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":"sediment transport, particle tracking, granular flow","lastPublishedDoi":"10.21203/rs.3.rs-9184032/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9184032/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eParticle-resolved measurements in physical experiments are crucial to the study of sediment transport. 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