Practical Pose Estimation Method for Industrial X-ray Radiography Based on Deep Learning and Local Template Matching

Practical Pose Estimation Method for Industrial X-ray Radiography Based on Deep Learning and Local Template Matching | 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 Practical Pose Estimation Method for Industrial X-ray Radiography Based on Deep Learning and Local Template Matching Dongsheng Ou, Yongshun Xiao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5831887/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 May, 2025 Read the published version in Journal of Nondestructive Evaluation → Version 1 posted 10 You are reading this latest preprint version Abstract With the increasing integration of industrial products, critical components are often enclosed within shells, making it difficult to measure their actual positions during assembly using contact measurement techniques. This often results in substandard product quality. X-ray imaging offers a non-destructive solution for inspecting internal structures and accurately positioning internal components. However, traditional pose estimation methods based on X-ray imaging rely on projection optimization, which is time-consuming and cannot meet the timely feedback requirements of assembly processes. In this work, we propose a pose estimation method for industrial X-ray radiography that combines neural networks for initial pose estimation with local template matching for pose refinement. This approach achieves high accuracy and efficiency in positioning internal targets. We conducted real X-ray imaging experiments on several objects, including a terahertz anode tube model. The mean alignment error was approximately 0.2 mm, which is lower than the spatial resolution (about 0.25 mm) of the CT images constructed from the same X-ray projections. The running time for pose estimation of a single object was about 10 seconds, significantly faster than conventional methods that take several minutes, making it suitable for timely feedback in industrial assembly processes. X-ray imaging Pose estimation Deep learning Template matching Assembly inspection Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 15 May, 2025 Read the published version in Journal of Nondestructive Evaluation → Version 1 posted Editorial decision: Revision requested 24 Mar, 2025 Reviews received at journal 23 Mar, 2025 Reviews received at journal 17 Mar, 2025 Reviewers agreed at journal 12 Mar, 2025 Reviewers agreed at journal 11 Mar, 2025 Reviewers agreed at journal 10 Mar, 2025 Reviewers invited by journal 03 Feb, 2025 Editor assigned by journal 23 Jan, 2025 Submission checks completed at journal 15 Jan, 2025 First submitted to journal 15 Jan, 2025 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. 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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-5831887","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":402531660,"identity":"0b8391eb-32f3-4c9f-b481-031da0989b8c","order_by":0,"name":"Dongsheng Ou","email":"","orcid":"","institution":"Tsinghua University","correspondingAuthor":false,"prefix":"","firstName":"Dongsheng","middleName":"","lastName":"Ou","suffix":""},{"id":402531661,"identity":"011c55b3-ae7d-4798-97c3-9ce668801d73","order_by":1,"name":"Yongshun Xiao","email":"data:image/png;base64,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","orcid":"","institution":"Tsinghua University","correspondingAuthor":true,"prefix":"","firstName":"Yongshun","middleName":"","lastName":"Xiao","suffix":""}],"badges":[],"createdAt":"2025-01-15 06:53:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5831887/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5831887/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10921-025-01191-z","type":"published","date":"2025-05-15T15:57:45+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83067961,"identity":"510428e8-c107-4746-a09f-f92f55e8ff98","added_by":"auto","created_at":"2025-05-19 16:08:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1351799,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript0114.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5831887/v1_covered_e679b91c-3ec5-4ed1-8e15-b91e5515e7a9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Practical Pose Estimation Method for Industrial X-ray Radiography Based on Deep Learning and Local Template Matching","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-nondestructive-evaluation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jone","sideBox":"Learn more about [Journal of Nondestructive Evaluation](http://link.springer.com/journal/10921)","snPcode":"10921","submissionUrl":"https://submission.nature.com/new-submission/10921/3","title":"Journal of Nondestructive Evaluation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"X-ray imaging, Pose estimation, Deep learning, Template matching, Assembly inspection","lastPublishedDoi":"10.21203/rs.3.rs-5831887/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5831887/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith the increasing integration of industrial products, critical components are often enclosed within shells, making it difficult to measure their actual positions during assembly using contact measurement techniques. 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