Image Classification for Defect Detection of Light Aircraft

Image Classification for Defect Detection of Light Aircraft | 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 Image Classification for Defect Detection of Light Aircraft Luke Connolly, James Garland, Diarmuid O'Gorman, Edmond Tobin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4274158/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 Visual inspections of aircraft are a vital part of routine procedures for maintenance personnel in the aviation industry. However, visual inspections take up a considerable amount of time to perform and are susceptible to human error. To mitigate this, utilising image classification for detecting defects is proposed. This approach utilises transfer learning of ResNet-50 within MATLAB to determine whether a defect is present in an image taken of the aircraft. The proposed method offers a solution for improving the efficiency and accuracy of defect detection during a general visual inspection in the aviation industry. Targeted defects here are damaged_skin and missing_rivets alongside a class denoting no_defect. Validation and testing accuracies achieved in this study are 88.33 % and 65.55 %, respectively. Transfer learning MATLAB ResNet-50 Defects Aircraft damage detection Full Text Additional Declarations No competing interests reported. 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-4274158","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":291928401,"identity":"92332e25-d9bd-4178-be48-4d2154e09601","order_by":0,"name":"Luke Connolly","email":"data:image/png;base64,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","orcid":"","institution":"South East Technological University","correspondingAuthor":true,"prefix":"","firstName":"Luke","middleName":"","lastName":"Connolly","suffix":""},{"id":291928402,"identity":"da057a34-5599-44c6-91d3-0943ea554971","order_by":1,"name":"James Garland","email":"","orcid":"","institution":"South East Technological University","correspondingAuthor":false,"prefix":"","firstName":"James","middleName":"","lastName":"Garland","suffix":""},{"id":291928403,"identity":"d59248e1-01bd-4add-8c43-baf7963e0916","order_by":2,"name":"Diarmuid O'Gorman","email":"","orcid":"","institution":"South East Technological University","correspondingAuthor":false,"prefix":"","firstName":"Diarmuid","middleName":"","lastName":"O'Gorman","suffix":""},{"id":291928404,"identity":"aab8e74f-91b0-47d6-a655-8ae45d3e6ec2","order_by":3,"name":"Edmond Tobin","email":"","orcid":"","institution":"South East Technological University","correspondingAuthor":false,"prefix":"","firstName":"Edmond","middleName":"","lastName":"Tobin","suffix":""}],"badges":[],"createdAt":"2024-04-16 07:44:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4274158/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4274158/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57673415,"identity":"28a84288-940a-4b4b-86eb-6748e9c780c9","added_by":"auto","created_at":"2024-06-04 07:07:17","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":358688,"visible":true,"origin":"","legend":"","description":"","filename":"SpringerNatureCEASAeronauticalJournalImageClassification.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4274158/v1_covered_03664bf3-7155-4730-aa7f-1fc00c044809.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Image Classification for Defect Detection of Light Aircraft","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"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":"Transfer learning, MATLAB, ResNet-50, Defects, Aircraft damage detection","lastPublishedDoi":"10.21203/rs.3.rs-4274158/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4274158/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Visual inspections of aircraft are a vital part of routine procedures for maintenance personnel in the aviation industry. 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