Shearographic Anomaly Detection Dataset (SADD)

Shearographic Anomaly Detection Dataset (SADD) | 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 Shearographic Anomaly Detection Dataset (SADD) Jessica Plassmann, Nicolas Schuler, Michael Schuth, Georg von Freymann This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8639283/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Shearography is an emerging optical method for non-destructive testing and is gaining increasing attention in industrial inspection scenarios. However, the development and systematic comparison of machine learning methods to allow higher degrees of shearographic inspection is limited by the lack of publicly available, well-characterized datasets. This paper introduces a curated shearography dataset designed specifically for machine vision research. The dataset comprises systematically designed defect geometries with controlled sizes and orientations, complemented by defect-free samples and annotated measurement artefacts that frequently occur in practical measurements. All annotations are performed by domain experts and are supported by a detailed description of the underlying deformation physics, which explain characteristic shearographic signatures and class ambiguities. This physical context motivates the experimental design and supports informed interpretation of learning-based results beyond purely statistical correlations. The dataset enables learning-based methods across unsupervised, supervised, and zero-shot paradigms, demonstrated through three representative use cases: defect detection, multi-class classification, and text-based automated labeling of shearographic measurements. It provides a standardized and reproducible benchmark for systematic machine vision research, supporting the application of foundation models and other advanced methods in industry-specific inspection scenarios. All data and code are publicly available. Machine Vision Dataset Shearography Defect Detection Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 02 Feb, 2026 Editor assigned by journal 20 Jan, 2026 Submission checks completed at journal 20 Jan, 2026 First submitted to journal 19 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. 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-8639283","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":584521759,"identity":"00ff5ae1-4064-4d08-9ea9-b2947a110a1c","order_by":0,"name":"Jessica Plassmann","email":"data:image/png;base64,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","orcid":"","institution":"Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau","correspondingAuthor":true,"prefix":"","firstName":"Jessica","middleName":"","lastName":"Plassmann","suffix":""},{"id":584521760,"identity":"9c00f716-0de5-47b6-9a9c-e2e82ebd4c06","order_by":1,"name":"Nicolas Schuler","email":"","orcid":"","institution":"Trier University of Applied Sciences","correspondingAuthor":false,"prefix":"","firstName":"Nicolas","middleName":"","lastName":"Schuler","suffix":""},{"id":584521761,"identity":"c1bd11fd-5607-44d4-8035-e9c6dfd47776","order_by":2,"name":"Michael Schuth","email":"","orcid":"","institution":"Trier University of Applied Sciences","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Schuth","suffix":""},{"id":584521762,"identity":"c9239678-f19e-4330-925b-eef10d3fae29","order_by":3,"name":"Georg von Freymann","email":"","orcid":"","institution":"Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau","correspondingAuthor":false,"prefix":"","firstName":"Georg","middleName":"","lastName":"von Freymann","suffix":""}],"badges":[],"createdAt":"2026-01-19 12:09:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8639283/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8639283/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101881747,"identity":"3f8e81e3-5e1b-4fd7-81db-d2140ecf101c","added_by":"auto","created_at":"2026-02-04 15:16:03","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11544904,"visible":true,"origin":"","legend":"","description":"","filename":"SADDUploadVersion.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8639283/v1_covered_c857daa2-55e5-4cc9-bd9e-e8030c40776c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Shearographic Anomaly Detection Dataset (SADD)","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"machine-vision-and-applications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mvap","sideBox":"Learn more about [Machine Vision and Applications](https://www.springer.com/journal/138)","snPcode":"138","submissionUrl":"https://submission.springernature.com/new-submission/138/3","title":"Machine Vision and Applications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Machine Vision, Dataset, Shearography, Defect Detection","lastPublishedDoi":"10.21203/rs.3.rs-8639283/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8639283/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eShearography is an emerging optical method for non-destructive testing and is gaining increasing attention in industrial inspection scenarios. 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