A Study on Autonomous Navigation and Obstacle Avoidance Systems for UAVs Using Deep Learning Techniques

A Study on Autonomous Navigation and Obstacle Avoidance Systems for UAVs Using Deep Learning Techniques | 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 Study on Autonomous Navigation and Obstacle Avoidance Systems for UAVs Using Deep Learning Techniques Chahira CHERIF, Mohammed MAIZA, Abdelmalik TALEB-AHMED This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7303597/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 Unmanned Aerial Vehicles (UAVs) play a vital role in applications such as aerial surveillance, disaster management , and urban air mobility. To ensure safe operation in dynamic environments, robust autonomous navigation and obstacle avoidance systems are essential. Conventional methods, which depend on predefined rules and sensor-based heuristics, often lack real-time adaptability in complex scenarios. To address this limitation, we propose a Deep Learning (DL)-enhanced Backtracking Search-optimized Customized YOLOv5 (BS-CYOLOv5) framework, which combines multi-sensor data fusion, real-time obstacle detection, and intelligent path planning. The dataset consists of UAV flight data gathered from RGB cameras, LiDAR, IMU, and GPS sensors. Pre-processing techniques, including normalization, are applied to improve model performance and generalization. For obstacle detection, the Customized YOLOv5 (CYOLOv5) model is utilized due to its high-speed inference and detection accuracy, enabling real-time obstacle recognition in diverse environments. Navigation and path planning are optimized using the Backtracking Search Algorithm (BSA), which dynamically adjusts flight paths to ensure collision-free and efficient trajectory planning. Experimental results demonstrate the effectiveness of the proposed approach, significantly improving obstacle avoidance accuracy and navigation efficiency. The CYOLOv5 model achieves an inference speed of 17.2 ms and a detection accuracy of 95%, while BSA ensures adaptive path optimization, minimizing collision risks. This research advances intelligent UAV systems by integrating cutting-edge DL and optimization techniques, enhancing autonomy, safety, and reliability in real-world operations. Unmanned Aerial Vehicles Autonomous Navigation Obstacle Avoidance BS-CYOLOv5 Deep Learning 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-7303597","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":506746805,"identity":"040ead12-eb20-48e8-a6db-81737e7332bd","order_by":0,"name":"Chahira CHERIF","email":"data:image/png;base64,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","orcid":"","institution":"Ahmed Benbella Oran 1 University","correspondingAuthor":true,"prefix":"","firstName":"Chahira","middleName":"","lastName":"CHERIF","suffix":""},{"id":506746806,"identity":"4501dcd3-2982-4c06-ae8e-13074f424b5b","order_by":1,"name":"Mohammed MAIZA","email":"","orcid":"","institution":"Ahmed Benbella Oran 1 University","correspondingAuthor":false,"prefix":"","firstName":"Mohammed","middleName":"","lastName":"MAIZA","suffix":""},{"id":506746808,"identity":"786bda16-6375-439e-bc1a-b17ecbe0c774","order_by":2,"name":"Abdelmalik TALEB-AHMED","email":"","orcid":"","institution":"Polytechnic University of Hauts-de-France, University of Lille","correspondingAuthor":false,"prefix":"","firstName":"Abdelmalik","middleName":"","lastName":"TALEB-AHMED","suffix":""}],"badges":[],"createdAt":"2025-08-05 19:23:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7303597/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7303597/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91138208,"identity":"610fcda8-e51f-48e9-8e99-1fac1dc0937c","added_by":"auto","created_at":"2025-09-12 04:09:00","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1265999,"visible":true,"origin":"","legend":"","description":"","filename":"snarticle.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7303597/v1_covered_4d5318f4-eaf2-4272-a188-c09210a0af87.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Study on Autonomous Navigation and Obstacle Avoidance Systems for UAVs Using Deep Learning Techniques","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":"Unmanned Aerial Vehicles, Autonomous Navigation, Obstacle Avoidance, BS-CYOLOv5, Deep Learning","lastPublishedDoi":"10.21203/rs.3.rs-7303597/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7303597/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Unmanned Aerial Vehicles (UAVs) play a vital role in applications such as aerial surveillance, disaster management , and urban air mobility. To ensure safe operation in dynamic environments, robust autonomous navigation and obstacle avoidance systems are essential. Conventional methods, which depend on predefined rules and sensor-based heuristics, often lack real-time adaptability in complex scenarios. To address this limitation, we propose a Deep Learning (DL)-enhanced Backtracking Search-optimized Customized YOLOv5 (BS-CYOLOv5) framework, which combines multi-sensor data fusion, real-time obstacle detection, and intelligent path planning. The dataset consists of UAV flight data gathered from RGB cameras, LiDAR, IMU, and GPS sensors. Pre-processing techniques, including normalization, are applied to improve model performance and generalization. For obstacle detection, the Customized YOLOv5 (CYOLOv5) model is utilized due to its high-speed inference and detection accuracy, enabling real-time obstacle recognition in diverse environments. Navigation and path planning are optimized using the Backtracking Search Algorithm (BSA), which dynamically adjusts flight paths to ensure collision-free and efficient trajectory planning. Experimental results demonstrate the effectiveness of the proposed approach, significantly improving obstacle avoidance accuracy and navigation efficiency. The CYOLOv5 model achieves an inference speed of 17.2 ms and a detection accuracy of 95%, while BSA ensures adaptive path optimization, minimizing collision risks. 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