Advancing UAV Landing Precision: A Comparative Study of Deep Learning Classifiers for Human Detection

Advancing UAV Landing Precision: A Comparative Study of Deep Learning Classifiers for Human Detection | 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 Advancing UAV Landing Precision: A Comparative Study of Deep Learning Classifiers for Human Detection Fariborz Rasouli, Leila Sharifi, Koorosh Aslansefat This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5709691/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 This study introduces and implements the use of evidence-based deep learning and Dirichlet distribution for optimal mobility and landing of unmanned aerial vehicles (UAVs). A new evidence-based deep learning model is developed by defining a new loss function on multiple datasets of aerial images captured by drones. The purpose is to identify and process the presence or absence of humans in the landing environment. In this paper we propose a novel deep learning architecture that is capable of providing uncertainty in classification and that can be deployed for input samples. Our project and implemented model are compared with other existing and robust models such as EfficientNET, DenseNet, MobileNet, VGG 16 and 19, Yolov8 and Resnet50. The results show that the use of Evidential Deep Learning (EDL) shows higher accuracy in detecting human presence or absence compared to other existing models. We also model and assess uncertainty for all models used. The use of different models shows the inverse relationship between reliability and uncertainty in person detection in UAVs (higher reliability at the same time as lower uncertainty and vice versa). Person detection Drone intelligence Autonomous drones Deep neural networks Evidential Deep Learning Uncertainty-aware 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-5709691","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":395868102,"identity":"5216f15c-2961-4a61-8be7-eb87785a8136","order_by":0,"name":"Fariborz Rasouli","email":"","orcid":"","institution":"Urmia University","correspondingAuthor":false,"prefix":"","firstName":"Fariborz","middleName":"","lastName":"Rasouli","suffix":""},{"id":395868105,"identity":"6ae375cc-f960-444b-9dfe-ea377eaf88ad","order_by":1,"name":"Leila Sharifi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIie3OocoCQRSG4U8GZsvBrWdY0VtYEVaT16IIpg2CRRD8N/1bvAMvw2oYmWDZCzCaNlm0CQZ30GSY1WaYtxzmwMMcwOf7wZqArAaHMsiqGT+32kXki6h/0t8RqIxHHx4mEZSn227ATXVOrsfZEGGuG2bhJNTvrktmGaW9TRpPwMUI+8JNJJPmlSUijQVwBPZZzWHqrqtfVGHJHzr1BElEljBZYhDXEkFJ1LKEpvOKHKhbjDMnCYO8VGe94k5utiK9L9vtgzEXF4F4exPQcAKfz+fzfdADyXE78/m7W+gAAAAASUVORK5CYII=","orcid":"","institution":"Urmia University","correspondingAuthor":true,"prefix":"","firstName":"Leila","middleName":"","lastName":"Sharifi","suffix":""},{"id":395868106,"identity":"21cf8f7c-5ba3-4ea7-96f9-05a31629b4b7","order_by":2,"name":"Koorosh Aslansefat","email":"","orcid":"","institution":"University of Hull","correspondingAuthor":false,"prefix":"","firstName":"Koorosh","middleName":"","lastName":"Aslansefat","suffix":""}],"badges":[],"createdAt":"2024-12-25 07:23:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5709691/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5709691/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108804680,"identity":"c4028957-c938-408d-930f-e0675b8794aa","added_by":"auto","created_at":"2026-05-08 15:22:45","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6101672,"visible":true,"origin":"","legend":"","description":"","filename":"InternationalJournalofIntelligentRoboticsandApplications2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5709691/v1_covered_5bf021df-cd58-49a2-bc39-44ad68103726.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Advancing UAV Landing Precision: A Comparative Study of Deep Learning Classifiers for Human Detection","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":"Person detection, Drone intelligence, Autonomous drones, Deep neural networks, Evidential Deep Learning, Uncertainty-aware","lastPublishedDoi":"10.21203/rs.3.rs-5709691/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5709691/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This study introduces and implements the use of evidence-based deep learning and Dirichlet distribution for optimal mobility and landing of unmanned aerial vehicles (UAVs). 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