Deep Learning Based Micro-Drone Detection forReliable Counter-Drone Systems Using MultipleSensors

Deep Learning Based Micro-Drone Detection forReliable Counter-Drone Systems Using MultipleSensors | 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 Deep Learning Based Micro-Drone Detection forReliable Counter-Drone Systems Using MultipleSensors Tinotenda Markandreck Mapara, Srinu Sesham, Frans Shafuda This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7981707/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Micro-drones are easily attainable due to their affordability and ease of operation. However, their malicious use has led to increasing security concerns, necessitating the development of reliable counter-drone systems. In the literature, numerous deep learning models have been proposed to achieve high detection accuracy using image-based approaches. However, these methods tend to be unreliable under low-light or dark conditions. Hence, to ensure robust and reliable detection, a multimodal approach using both audio and image data is proposed. Datasets containing audio and aerial images of drones and non-drones are utilized, and data augmentation is applied to increase variability and improve generalization. PANNs-CNN10, YAMNet, and ResNet50 CNN models are explored for acoustic detection using micro-drone sounds, while InceptionV3, ResNet50, and DenseNet121 are investigated for optical detection using images. The optimal models from each category are selected for fusion. The performance of the proposed method is evaluated using standard metrics such as accuracy, precision, recall, F1-score, and ROC curves. Experimental results show that PANNs-CNN10 is lighweight and achieved near-perfect accuracy (99.8%), while DenseNet121 achieved 98.9%. The combined probability outputs of these optimal CNN models are fed into a logistic regression (meta-classifier) to exploit the complementary strengths of both modalities. The fusion model achieved an accuracy of 99.95%, representing a slightly higher improvement compared to the individual models. The ensemble approach effectively leverages the strengths of acoustic and optical modalities, addressing the limitations of standalone techniques and reducing false alarms to enhance reliability. Overall, this study contributes to the development of intelligent detection mechanisms that can strengthen counter-drone defense systems. Deep learning models Micro-drones Multi-modal fusion Reliable detection Computational efficiency Counter drone systems Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 04 Apr, 2026 Reviews received at journal 03 Apr, 2026 Reviewers agreed at journal 25 Mar, 2026 Reviews received at journal 12 Jan, 2026 Reviewers agreed at journal 31 Dec, 2025 Reviewers agreed at journal 04 Nov, 2025 Reviewers invited by journal 02 Nov, 2025 Editor assigned by journal 01 Nov, 2025 Submission checks completed at journal 31 Oct, 2025 First submitted to journal 29 Oct, 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. 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-7981707","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":540124588,"identity":"50a49d22-c9ba-42c2-948d-d7633aaa4bcc","order_by":0,"name":"Tinotenda Markandreck Mapara","email":"","orcid":"","institution":"University of Namibia","correspondingAuthor":false,"prefix":"","firstName":"Tinotenda","middleName":"Markandreck","lastName":"Mapara","suffix":""},{"id":540124589,"identity":"63d5d3d2-9afb-443a-bf45-dce3418ac91a","order_by":1,"name":"Srinu 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However, their malicious use has led to increasing security concerns, necessitating the development of reliable counter-drone systems. In the literature, numerous deep learning models have been proposed to achieve high detection accuracy using image-based approaches. However, these methods tend to be unreliable under low-light or dark conditions. Hence, to ensure robust and reliable detection, a multimodal approach using both audio and image data is proposed. Datasets containing audio and aerial images of drones and non-drones are utilized, and data augmentation is applied to increase variability and improve generalization. PANNs-CNN10, YAMNet, and ResNet50 CNN models are explored for acoustic detection using micro-drone sounds, while InceptionV3, ResNet50, and DenseNet121 are investigated for optical detection using images. The optimal models from each category are selected for fusion. The performance of the proposed method is evaluated using standard metrics such as accuracy, precision, recall, F1-score, and ROC curves. Experimental results show that PANNs-CNN10 is lighweight and achieved near-perfect accuracy (99.8%), while DenseNet121 achieved 98.9%. The combined probability outputs of these optimal CNN models are fed into a logistic regression (meta-classifier) to exploit the complementary strengths of both modalities. The fusion model achieved an accuracy of 99.95%, representing a slightly higher improvement compared to the individual models. The ensemble approach effectively leverages the strengths of acoustic and optical modalities, addressing the limitations of standalone techniques and reducing false alarms to enhance reliability. Overall, this study contributes to the development of intelligent detection mechanisms that can strengthen counter-drone defense systems.\u003c/p\u003e","manuscriptTitle":"Deep Learning Based Micro-Drone Detection forReliable Counter-Drone Systems Using MultipleSensors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-13 13:24:35","doi":"10.21203/rs.3.rs-7981707/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-04T05:07:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-04T03:27:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"329178539875928667200926950048332887788","date":"2026-03-26T02:39:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-12T14:41:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"224528595342410040006300492258019104415","date":"2026-01-01T03:34:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"229227639967189590585189563019208839790","date":"2025-11-05T03:20:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-03T03:18:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-01T17:28:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-31T09:58:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Intelligent \u0026 Robotic Systems","date":"2025-10-29T15:46:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-intelligent-and-robotic-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of Intelligent \u0026 Robotic Systems](https://link.springer.com/journal/10846)","snPcode":"10846","submissionUrl":"https://submission.springernature.com/new-submission/10846/3","title":"Journal of Intelligent \u0026 Robotic Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"707ceff7-6bf6-4988-be0a-5d84dfe883b5","owner":[],"postedDate":"November 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-06T10:39:29+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-13 13:24:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7981707","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7981707","identity":"rs-7981707","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}