A IoT-enabled Obstacle Detection and Recognition Technique for Blind Persons

A IoT-enabled Obstacle Detection and Recognition Technique for Blind 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 A IoT-enabled Obstacle Detection and Recognition Technique for Blind Persons Sunnia Ikram, Prof. Imran Bajwa, Sujan Gyawali, Amna Ikram, Najah Alsubaie This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5482522/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 Object detection is a critical task in computer vision, essential for real-time applications ranging from autonomous vehicles to surveillance systems. This proposed work presents a comparative evaluation of Single Shot Multibox Detector (SSD algorithms). YOLOv3, MobileNetv3, RetinaNet and Faster R-CNN, in the context of real-time obstacle detection from camera images. The study evaluates these algorithms on performance metrics Precision, Recall and F1 score across various Intersection Over Union (IoU) thresholds. Also, the computational efficiency in terms of the time taken per frame is assessed to determine the effectiveness of each algorithm. The workflow includes image processing, augmentation, and application of SSD models to detect objects like vehicles, pedestrians and traffic signals. Results indicate that YOLOv3 achieves the highest precision of 96% demonstrating robust performance in real-time scenarios, while MobileNetv3 follows closely with 92%, RetinaNet and Faster RCNN achieves accuracy 90% and 90% respectively. These findings contribute to understanding the trade-offs between accuracy and computational efficiency in selected suitable SSD models for practical deployment. Obstacle Detection Performance Evaluation Real-time detection Single Shot Multibox Detector (SSD) Visually Impaired 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-5482522","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":379970359,"identity":"69445d55-c2b5-48cf-ab46-a19c391c047e","order_by":0,"name":"Sunnia Ikram","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYBAC+QYgkcDAwCMB4n0wALEZDPBqMTiApIVxBlFaYAyQFmYeBmK0SOQee/CghkFGcvbhp5ttCg7nMbA3b5NgqLGJxumXGXnpBgnHGHik+dLMbucYHC5m4DlWJsFwLC23AZeeGzlmEglsDDxyPAxgLYkNEkARxobDBLT8A2lh/3bbAqRF/g0RWhLbgA7j4TG7zQC2hQe/FoMzQDMT+yR4JHt4ym72GKQXs/GkFVsk4PGLfHuOmeSPbzb2EmfYt9348cc6j5/98MYbH2pscDsMAiQQTDYQkYBf+SgYBaNgFIwCAgAAF1FPsSpoeG0AAAAASUVORK5CYII=","orcid":"","institution":"The Islamia University of Bahawalpur","correspondingAuthor":true,"prefix":"","firstName":"Sunnia","middleName":"","lastName":"Ikram","suffix":""},{"id":379970360,"identity":"232395f9-dc84-4c21-ad3b-8b5c2b64a132","order_by":1,"name":"Prof. Imran Bajwa","email":"","orcid":"","institution":"The Islamia University of Bahawalpur","correspondingAuthor":false,"prefix":"","firstName":"Prof.","middleName":"Imran","lastName":"Bajwa","suffix":""},{"id":379970361,"identity":"c1477a51-cfc4-499d-b92b-a7ada5110cd5","order_by":2,"name":"Sujan Gyawali","email":"","orcid":"","institution":"Lamar University","correspondingAuthor":false,"prefix":"","firstName":"Sujan","middleName":"","lastName":"Gyawali","suffix":""},{"id":379970362,"identity":"9d448890-d638-4596-80e1-4cafd5734faf","order_by":3,"name":"Amna Ikram","email":"","orcid":"","institution":"Govt Sadiq College University","correspondingAuthor":false,"prefix":"","firstName":"Amna","middleName":"","lastName":"Ikram","suffix":""},{"id":379970363,"identity":"afa9694a-036f-431c-a9e5-12848884f3c2","order_by":4,"name":"Najah Alsubaie","email":"","orcid":"","institution":"Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University (PNU","correspondingAuthor":false,"prefix":"","firstName":"Najah","middleName":"","lastName":"Alsubaie","suffix":""}],"badges":[],"createdAt":"2024-11-19 10:23:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5482522/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5482522/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":70831996,"identity":"c4b1595d-898c-40e8-a708-56a73e657631","added_by":"auto","created_at":"2024-12-07 13:46:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1539977,"visible":true,"origin":"","legend":"","description":"","filename":"MainManuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5482522/v1_covered_afff1725-3145-4d33-90f4-7d435046885c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A IoT-enabled Obstacle Detection and Recognition Technique for Blind Persons","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":"Obstacle Detection, Performance Evaluation, Real-time detection, Single Shot Multibox Detector (SSD), Visually Impaired","lastPublishedDoi":"10.21203/rs.3.rs-5482522/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5482522/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Object detection is a critical task in computer vision, essential for real-time applications ranging from autonomous vehicles to surveillance systems. 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