Lightweight Machine Learning Models for Drone Detection Using Acoustic and Optical Features

Lightweight Machine Learning Models for Drone Detection Using Acoustic and Optical Features | 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 Lightweight Machine Learning Models for Drone Detection Using Acoustic and Optical Features Tinotenda Mark Mapara, Srinu Sesham, Pavan Kumar Sesham This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6688717/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Oct, 2025 Read the published version in Discover Artificial Intelligence → Version 1 posted 16 You are reading this latest preprint version Abstract The widespread deployment of drones has triggered major concerns over privacy and security, creating a demand for robust anti-drone systems (ADS). A critical component of ADS is the detection unit/model, which identifies drones in an unauthorized area. Recently, various statistical and machine/deep learning methods have been developed for drone detection units. Statistical methods are traditionally applied which often suffer from uncertain thresholds under varying noise distributions. While deep learning-based methods are highly popular, they frequently face challenges related to high computational complexity. This study explores the potential of low-complexity machine learning (LCML) models, including logistic regression model (LRM), support vector machines (SVM), and random forest algorithm (RFA) for drone detection using acoustic and optical features. For optical-based detection, histogram of oriented gradients (HOG) features extracted from images are used, whereas for acoustic-based detection HOG features are derived from log-mel spectrograms of drone acoustic signals. The LCML models are assessed using various performance metrics for binary classification, with RFA demonstrating the best results that achieves 87.5% accuracy with optical features and 89% accuracy with acoustic features. In addition, it outperforms SVM and LRM irrespective of the input feature. However, LRM exhibits the lowest training and testing complexity, making it a preferable choice where limited computational resources are available. These findings suggest that RFA is the most promising LCML model for real-time detection unit for an ADS that offers a balance between accuracy and inference complexity. Anti drone systems Low complex machine learning models HOG features Detection accuracy Computational efficiency Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 17 Oct, 2025 Read the published version in Discover Artificial Intelligence → Version 1 posted Editorial decision: Revision requested 18 Jul, 2025 Reviews received at journal 16 Jul, 2025 Reviews received at journal 14 Jul, 2025 Reviewers agreed at journal 06 Jul, 2025 Reviewers agreed at journal 06 Jul, 2025 Reviewers agreed at journal 05 Jul, 2025 Reviewers agreed at journal 04 Jul, 2025 Reviews received at journal 19 Jun, 2025 Reviewers agreed at journal 19 Jun, 2025 Reviews received at journal 18 Jun, 2025 Reviewers agreed at journal 13 Jun, 2025 Reviewers agreed at journal 06 Jun, 2025 Reviewers invited by journal 06 Jun, 2025 Editor assigned by journal 21 May, 2025 Submission checks completed at journal 21 May, 2025 First submitted to journal 17 May, 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. 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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-6688717","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":467441061,"identity":"6dc7f25e-b3f3-4cd9-a8b9-d85d63f97575","order_by":0,"name":"Tinotenda Mark Mapara","email":"","orcid":"","institution":"University of Namibia","correspondingAuthor":false,"prefix":"","firstName":"Tinotenda","middleName":"Mark","lastName":"Mapara","suffix":""},{"id":467441062,"identity":"9e90f1fa-38f8-4814-8e18-b320623f9ed5","order_by":1,"name":"Srinu Sesham","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAr0lEQVRIiWNgGAWjYJACZiCWI12LMelaEhuIVi7ff/jw54Kabekbbjc/3cC4w46wFoMbaWnSM47dzt1w55jZDcYzyURokeAxY+ZhA2q5kQDU0sZMjMPOf/7M8+92usGN9G9ALfWEtTAcyGGQ5m27nWBwIwdky2EiHHYjzUyat++24cwbOWU3EtuOE+Oww48/83y7Lc93I33bjY9t1UQ4DAUkkKphFIyCUTAKRgF2AAB94D0UwNHH8wAAAABJRU5ErkJggg==","orcid":"","institution":"University of Namibia","correspondingAuthor":true,"prefix":"","firstName":"Srinu","middleName":"","lastName":"Sesham","suffix":""},{"id":467441063,"identity":"2d81228f-7e92-4516-abbf-9a34ef5e6b39","order_by":2,"name":"Pavan Kumar Sesham","email":"","orcid":"","institution":"Andhra University","correspondingAuthor":false,"prefix":"","firstName":"Pavan","middleName":"Kumar","lastName":"Sesham","suffix":""}],"badges":[],"createdAt":"2025-05-17 19:23:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6688717/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6688717/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s44163-025-00532-1","type":"published","date":"2025-10-17T15:58:38+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":93956709,"identity":"53399bd5-fed2-4588-8f4a-6659aa93c80b","added_by":"auto","created_at":"2025-10-20 16:11:56","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1287768,"visible":true,"origin":"","legend":"","description":"","filename":"SpringerDAI.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6688717/v1_covered_905e89d3-531c-410c-8f65-f5e676f309b0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Lightweight Machine Learning Models for Drone Detection Using Acoustic and Optical Features","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":"discover-artificial-intelligence","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diai","sideBox":"Learn more about [Discover Artificial Intelligence](https://www.springer.com/44163)","snPcode":"","submissionUrl":"","title":"Discover Artificial Intelligence","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Anti drone systems, Low complex machine learning models, HOG features, Detection accuracy, Computational efficiency","lastPublishedDoi":"10.21203/rs.3.rs-6688717/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6688717/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe widespread deployment of drones has triggered major concerns over privacy and security, creating a demand for robust anti-drone systems (ADS). 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