Enhanced Obstacle Detection Using Bilateral Vision-Aided Transformer Neural Network for Visually Impaired Persons

Enhanced Obstacle Detection Using Bilateral Vision-Aided Transformer Neural Network for Visually Impaired Persons | 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 Enhanced Obstacle Detection Using Bilateral Vision-Aided Transformer Neural Network for Visually Impaired Persons Ala Alarood, Mohammed Salem Atoum, Azizah Abdul Manaf, Adamu Abubakar, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6845141/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Obstacle detection remains vital in autonomous navigation and assistive technologies, especially for visually impaired individuals. This work introduces an enhanced obstacle detection framework based on a Bilateral Vision Transformer and Convolution Kernel Neural Network (BViT-CKNN). The system incorporates stereo vision data and applies a bilateral filter to reduce noise while preserving edge details. A Vision Transformer (ViT) model is then used for global feature extraction, and a Convolution Kernel Neural Network (CKNN) captures fine-grained local features. Evaluated using the COCO dataset, the proposed BViT-CKNN achieves superior performance in precision (0.93), recall (0.91), F1-score (0.92), and Mean Absolute Error (MAE) reduction (3.16%) compared to existing methods. Visually Impaired Obstacle Detection Bilateral Filter Vision Transformer Convolution Kernel Neural Network Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 15 Jul, 2025 Reviews received at journal 08 Jul, 2025 Reviewers agreed at journal 24 Jun, 2025 Reviewers agreed at journal 22 Jun, 2025 Reviews received at journal 17 Jun, 2025 Reviewers agreed at journal 17 Jun, 2025 Reviewers agreed at journal 11 Jun, 2025 Reviewers invited by journal 10 Jun, 2025 Editor assigned by journal 09 Jun, 2025 Submission checks completed at journal 09 Jun, 2025 First submitted to journal 07 Jun, 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. We do this by developing innovative software and high quality services for the global research community. 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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-6845141","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":469620482,"identity":"4f70b5e0-bfe8-47d3-b3c6-16f9bac46e3b","order_by":0,"name":"Ala Alarood","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIie3PoQoCQRCA4RFBi3B1DeorjAgaRHyVXQSvbLhoMGw6ow/gS2wSbMKBVxasJ1vWosVwTyDOmUzL2QT3D8OG+RgWIBT61Vw12goQoFmT8Gp0jl8T9p41SLTJr46v9oNJ94EJrKZCRZnzEmbkCLmxw8NOIoKJhWJL9J85yhYTqW1oS6SRZkTATwbn+42Jp53riyHyJBLlpZdgwcdMKCt00SGiiID0XxkWD/rLyS60WSb0iEcpk4mX9M/x1ZVrO9N5tsdyPe1to1x7yWct5NWsvU813TfboVAo9D+9AGVmS+Hv+IpGAAAAAElFTkSuQmCC","orcid":"","institution":"University of Jeddah","correspondingAuthor":true,"prefix":"","firstName":"Ala","middleName":"","lastName":"Alarood","suffix":""},{"id":469620483,"identity":"f1529c9a-4e36-4ea4-a5a3-851f7e6aeed2","order_by":1,"name":"Mohammed Salem Atoum","email":"","orcid":"","institution":"University of Jordan","correspondingAuthor":false,"prefix":"","firstName":"Mohammed","middleName":"Salem","lastName":"Atoum","suffix":""},{"id":469620484,"identity":"6d983d6e-1798-4953-851d-6c40830d0291","order_by":2,"name":"Azizah Abdul Manaf","email":"","orcid":"","institution":"International Islamic University Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Azizah","middleName":"Abdul","lastName":"Manaf","suffix":""},{"id":469620485,"identity":"b5ec325b-bd59-4868-b4b2-25373b755bd6","order_by":3,"name":"Adamu Abubakar","email":"","orcid":"","institution":"International Islamic University Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Adamu","middleName":"","lastName":"Abubakar","suffix":""},{"id":469620486,"identity":"f0a87e57-4385-4739-9c43-70f7f2606bd2","order_by":4,"name":"Izzat Alsmadi","email":"","orcid":"","institution":"Texas A\u0026M San Antonio","correspondingAuthor":false,"prefix":"","firstName":"Izzat","middleName":"","lastName":"Alsmadi","suffix":""}],"badges":[],"createdAt":"2025-06-08 02:38:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6845141/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6845141/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84499264,"identity":"66c0425a-556a-49c9-9380-f464e378fcaf","added_by":"auto","created_at":"2025-06-12 16:17:58","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1167125,"visible":true,"origin":"","legend":"","description":"","filename":"ENHANCEDOBSTACLEDETECTIONUSINGBILATERALVISIONAIDEDTRANSFORMERNN06052025V1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6845141/v1_covered_dbf56eeb-131a-4a97-a42a-3416fdc1aeb4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enhanced Obstacle Detection Using Bilateral Vision-Aided Transformer Neural Network for Visually Impaired Persons","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":"cluster-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Cluster Computing](https://www.springer.com/journal/10586)","snPcode":"10586","submissionUrl":"https://submission.nature.com/new-submission/10586/3","title":"Cluster Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Visually Impaired, Obstacle Detection, Bilateral Filter Vision Transformer, Convolution Kernel Neural Network","lastPublishedDoi":"10.21203/rs.3.rs-6845141/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6845141/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObstacle detection remains vital in autonomous navigation and assistive technologies, especially for visually impaired individuals. 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