Artificial Intelligence Applications in Corrosion Inhibition: Future Directions

Artificial Intelligence Applications in Corrosion Inhibition: Future Directions | 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 Article Artificial Intelligence Applications in Corrosion Inhibition: Future Directions Sana Ahmed Khalil Ali This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6956467/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 Corrosion remains a critical threat to industrial infrastructure, contributing to global economic losses exceeding USD 2.5 trillion annually. Traditional detection methods like visual inspection and ultrasonic testing are often subjective, time-consuming, and lack scalability. This study uses deep learning models, YOLOv5 and Mask R-CNN, for automated corrosion detection and segmentation. Both models were trained and evaluated for accuracy and performance using an annotated dataset with bounding boxes and segmentation masks. YOLOv5 achieved faster inference and a high detection accuracy ( [email protected] = 0.71), proving effective for real-time applications. Mask R-CNN delivered superior segmentation quality, offering precise localization of corroded regions. The results highlight a trade-off between speed and spatial granularity, suggesting that model selection should depend on deployment context, real-time monitoring versus high-fidelity inspection. These findings demonstrate the potential of deep learning to enhance industrial corrosion management through automation and precision. Trial Registration: Not applicable. Physical sciences/Energy science and technology Physical sciences/Engineering Physical sciences/Materials science Corrosion Detection YOLOv5 Mask R-CNN Artificial Intelligence Real-Time Monitoring Segmentation IoT Integration Predictive Maintenance 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-6956467","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":485270019,"identity":"5a926825-f5bc-4b11-bc15-1d359acbaff9","order_by":0,"name":"Sana Ahmed Khalil Ali","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYLACxgYgcbyx8QGQkmFgYCNWy5nDzQYMDAY8JGi54d4mQZQW+fazBz/83LFNju8GY1vFx7Y/PPzsbQkMPyq24dRicCYvWbL3zG1jyduNbTdnthnwSPYcO8DYc+Y2bi0MOQYSvG23EzfcOdh2mxeoxeBGegMzYxtuLfL9b4x//m27Xb/hRmJbMVFaGG7kmEkDbUkwAGphhmhJO4BXi8GNN2bWsm23DWeeOdgsOeOcMcgvCQfx+UW+P8f45tu22/J8x9sffvhQJicHDDHDBz8q8DgMKzhAovpRMApGwSgYBWgAADOVXxOTC/atAAAAAElFTkSuQmCC","orcid":"","institution":"University of Tabuk","correspondingAuthor":true,"prefix":"","firstName":"Sana","middleName":"Ahmed Khalil","lastName":"Ali","suffix":""}],"badges":[],"createdAt":"2025-06-23 11:53:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6956467/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6956467/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89885320,"identity":"bea52a72-25df-48fc-a0db-6e6da9969111","added_by":"auto","created_at":"2025-08-26 06:24:24","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1735898,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptSana.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6956467/v1_covered_096de833-2b25-4ef4-ae06-2fd35c80869a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Artificial Intelligence Applications in Corrosion Inhibition: Future Directions","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":"Corrosion Detection, YOLOv5, Mask R-CNN, Artificial Intelligence, Real-Time Monitoring, Segmentation, IoT Integration, Predictive Maintenance","lastPublishedDoi":"10.21203/rs.3.rs-6956467/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6956467/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCorrosion remains a critical threat to industrial infrastructure, contributing to global economic losses exceeding USD 2.5 trillion annually. Traditional detection methods like visual inspection and ultrasonic testing are often subjective, time-consuming, and lack scalability. This study uses deep learning models, YOLOv5 and Mask R-CNN, for automated corrosion detection and segmentation. Both models were trained and evaluated for accuracy and performance using an annotated dataset with bounding boxes and segmentation masks. YOLOv5 achieved faster inference and a high detection accuracy ([email protected] = 0.71), proving effective for real-time applications. Mask R-CNN delivered superior segmentation quality, offering precise localization of corroded regions. The results highlight a trade-off between speed and spatial granularity, suggesting that model selection should depend on deployment context, real-time monitoring versus high-fidelity inspection. 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