{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:35:24Z","timestamp":1772120124210,"version":"3.50.1"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T00:00:00Z","timestamp":1760659200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T00:00:00Z","timestamp":1760659200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Artif Intell"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    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, 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 drone detection, hybrid features such as histogram of oriented gradients and ResNet features are extracted from images are used. 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 LRM demonstrating the best results that achieves 97% accuracy with optical features and 98% accuracy with acoustic features. In addition, LRM exhibits the lowest training complexity of\n                    <jats:inline-formula>\n                      <jats:tex-math>$$O(N \\cdot d \\cdot i)$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    and inference complexity of\n                    <jats:italic>O<\/jats:italic>\n                    (\n                    <jats:italic>d<\/jats:italic>\n                    ), making it suitable where limited computational resources are available. These findings suggest that LRM is the favourable LCML model for real-time detection unit for an ADS that offers a balance between accuracy and inference complexity.\n                  <\/jats:p>","DOI":"10.1007\/s44163-025-00532-1","type":"journal-article","created":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T12:18:46Z","timestamp":1760703526000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Lightweight machine learning models for drone detection using acoustic and optical features"],"prefix":"10.1007","volume":"5","author":[{"given":"Tinotenda Mark","family":"Mapara","sequence":"first","affiliation":[]},{"given":"Srinu","family":"Sesham","sequence":"additional","affiliation":[]},{"given":"Pavan Kumar","family":"Sesham","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,17]]},"reference":[{"key":"532_CR1","doi-asserted-by":"publisher","first-page":"42635","DOI":"10.1109\/ACCESS.2021.3065926","volume":"9","author":"S Park","year":"2021","unstructured":"Park S, Kim HT, Lee S, Joo H, Kim H. Survey on anti-drone systems: components, designs, and challenges. IEEE Access. 2021;9:42635\u201359.","journal-title":"IEEE Access"},{"issue":"5","key":"532_CR2","first-page":"1","volume":"6","author":"VU Castrillo","year":"2022","unstructured":"Castrillo VU, Manco A, Pascarella D, Gigante G. A review of counter-uas technologies for cooperative defensive teams of drones. Drones. 2022;6(5):1\u201320.","journal-title":"Drones"},{"issue":"8","key":"532_CR3","doi-asserted-by":"publisher","first-page":"24","DOI":"10.3390\/s24082427","volume":"24","author":"A Frid","year":"2024","unstructured":"Frid A, Ben-Shimol Y, Manor E, Greenberg S. Drones detection using a fusion of rf and acoustic features and deep neural networks. Sensors. 2024;24(8):24\u20137.","journal-title":"Sensors"},{"key":"532_CR4","doi-asserted-by":"crossref","unstructured":"Wang Y, Chu Z, et al. A large-scale uav audio dataset and audio-based uav classification using cnn. In: Sixth IEEE international conference on robotic computing. 2022;186\u2013189.","DOI":"10.1109\/IRC55401.2022.00039"},{"key":"532_CR5","doi-asserted-by":"crossref","unstructured":"Yuan M, Pengli X, et al Detection and localization of sound events based on principal components analysis. In: 2nd International conference on consumer electronics and computer engineering. 2022;507\u2013511.","DOI":"10.1109\/ICCECE54139.2022.9712717"},{"key":"532_CR6","doi-asserted-by":"crossref","unstructured":"Ahmed CA, et al. Acoustic based drone detection via machine learning. In: International conference on IT and industrial technologies. 2022;01\u201306.","DOI":"10.1109\/ICIT56493.2022.9989229"},{"key":"532_CR7","doi-asserted-by":"crossref","unstructured":"Tejera-Berengue D, Zhu-Zhou et al. Acoustic-based detection of uavs using machine learning: Analysis of distance and environmental effects. In: 2023 IEEE International conference on signal and audio processing (SAS) 2023;1\u20136.","DOI":"10.1109\/SAS58821.2023.10254127"},{"key":"532_CR8","doi-asserted-by":"crossref","unstructured":"Al-Emadi S, Al-Ali o. Audio based drone detection and identification using deep learning. In: 15th International wireless communications & mobile computing conference. 2019;459\u2013464.","DOI":"10.1109\/IWCMC.2019.8766732"},{"key":"532_CR9","doi-asserted-by":"crossref","unstructured":"Yaacoub M, Younes o. Acoustic drone detection based on transfer learning and frequency domain features. In: International conference on smart systems and power management. 2022;47\u201351.","DOI":"10.1109\/IC2SPM56638.2022.9988816"},{"key":"532_CR10","doi-asserted-by":"crossref","unstructured":"Sliti M, Garai M. Drone detection and classification approaches based on ml algorithms. In: 28th asia pacific conference on communications (APCC), Sydney, Australia 2023;195\u2013201.","DOI":"10.1109\/APCC60132.2023.10460666"},{"key":"532_CR11","doi-asserted-by":"publisher","DOI":"10.22541\/au.168075364.45332093\/v1","author":"RA Zitar","year":"2023","unstructured":"Zitar RA, Kassab M, Fallah A, Barbaresco F. Bird\/drone detection and classification using classical and deep learning methods. Authorea Preprints. 2023. https:\/\/doi.org\/10.22541\/au.168075364.45332093\/v1.","journal-title":"Authorea Preprints"},{"key":"532_CR12","doi-asserted-by":"crossref","unstructured":"Dubey N, Nithin NMS, Tripathi S. Analysis and comparison of image-based UAV detection and identification. In: IEEE 9th Uttar Pradesh section international conference on electrical, electronics and computer engineering, India. 2022;1\u20136.","DOI":"10.1109\/UPCON56432.2022.9986447"},{"key":"532_CR13","doi-asserted-by":"crossref","unstructured":"al KA. A vision-based amateur drone detection algorithm for public safety applications. In: UK\/China emerging technologies, UK. 2019;1\u20135.","DOI":"10.1109\/UCET.2019.8881879"},{"issue":"4","key":"532_CR14","doi-asserted-by":"publisher","first-page":"3847","DOI":"10.1109\/TAES.2024.3368991","volume":"60","author":"M Kassab","year":"2024","unstructured":"Kassab M, Zitar RA, Barbaresco F. Drone detection with improved precision in traditional machine learning and less complexity in single-shot detectors. IEEE Trans Aerosp Electron Syst. 2024;60(4):3847\u201359.","journal-title":"IEEE Trans Aerosp Electron Syst"},{"key":"532_CR15","doi-asserted-by":"crossref","unstructured":"Adhikari N, al NRB. Modeling of optimal deep learning enabled object detection and classification on drone imagery. In: International conference on augmented intelligence and sustainable systems, India. 2022;303\u2013309.","DOI":"10.1109\/ICAISS55157.2022.10010957"},{"key":"532_CR16","doi-asserted-by":"publisher","first-page":"138669","DOI":"10.1109\/ACCESS.2019.2942944","volume":"7","author":"B Taha","year":"2019","unstructured":"Taha B, Shoufan A. Machine learning-based drone detection and classification: state-of-the-art in research. IEEE Access. 2019;7:138669\u201382.","journal-title":"IEEE Access"},{"issue":"1","key":"532_CR17","doi-asserted-by":"publisher","first-page":"125","DOI":"10.3390\/s24010125","volume":"24","author":"U Seidaliyeva","year":"2024","unstructured":"Seidaliyeva U, Ilipbayeva L, Taissariyeva K, Smailov N, Matson ET. Advances and challenges in drone detection and classification techniques: a state-of-the-art review. Sensors. 2024;24(1):125.","journal-title":"Sensors"},{"key":"532_CR18","doi-asserted-by":"publisher","DOI":"10.3389\/fphys.2022.805161","volume":"13","author":"Y Wu","year":"2022","unstructured":"Wu Y, Wang Z, Ripplinger CM, Sato D. Automated object detection in experimental data using combination of unsupervised and supervised methods. Front Physiol. 2022;13:805161.","journal-title":"Front Physiol"},{"key":"532_CR19","doi-asserted-by":"crossref","unstructured":"Christianti RF, Fuadi HL, Afandi MA, N, AS, Dharmawan A. Comparison of support vector machine and neural network algorithm in drone detection system. In: IEEE International conference on cybernetics and computational intelligence, Indonesia. 2022;421\u2013426.","DOI":"10.1109\/CyberneticsCom55287.2022.9865628"},{"key":"532_CR20","doi-asserted-by":"crossref","unstructured":"Kim J, Lee D, et al. Deep learning based malicious drone detection using acoustic and image data. In: Sixth IEEE international conference on robotic computing (IRC). 2022;91\u201392.","DOI":"10.1109\/IRC55401.2022.00024"},{"key":"532_CR21","doi-asserted-by":"crossref","unstructured":"Kanna RK, Joshi K. Classification of drone detection module using hybrid learning algorithms. In: 4th International conference on advance computing and innovative technologies in engineering. 2024;672\u2013676","DOI":"10.1109\/ICACITE60783.2024.10616656"},{"issue":"2","key":"532_CR22","doi-asserted-by":"publisher","first-page":"227","DOI":"10.4218\/etrij.2023-0485","volume":"47","author":"K Jha","year":"2024","unstructured":"Jha K, Srivastava S, Jain A. A novel speaker verification approach featuring multidomain acoustics based on the weighted city block minkowski distance. ETRI J. 2024;47(2):227\u201343.","journal-title":"ETRI J"},{"key":"532_CR23","unstructured":"Al-Emadi S. Drone audio dataset. Online. https:\/\/github.com\/saraalemadi\/DroneAudioDataset 2018."},{"key":"532_CR24","unstructured":"Cybersimar08: Drone detection. Accessed: 2025-03-05. https:\/\/www.kaggle.com\/datasets\/cybersimar08\/drone-detection 2025."},{"key":"532_CR25","first-page":"1","volume":"72","author":"D Hao","year":"2023","unstructured":"Hao D, Liu J, et al. Drone detection method based on the time-frequency complementary enhancement model. IEEE Trans Instrum Meas. 2023;72:1\u201312.","journal-title":"IEEE Trans Instrum Meas"},{"key":"532_CR26","doi-asserted-by":"publisher","first-page":"780","DOI":"10.1007\/s42452-025-07433-z","volume":"7","author":"U Panigrahi","year":"2025","unstructured":"Panigrahi U, Sahoo PK, Panda MK, et al. An enhanced deep learning-based feature extraction framework for moving object detection. Discov Appl Sci. 2025;7:780.","journal-title":"Discov Appl Sci"},{"key":"532_CR27","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: IEEE conference on computer vision and pattern recognition (CVPR). 2016;770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"532_CR28","doi-asserted-by":"crossref","unstructured":"Mapara TM, Sesham S, Shafuda F, Chembe DK. Performance of machine learning models in drone detection using optical features. In: Second international conference on cognitive robotics and intelligent systems (ICC - ROBINS). 2025;396\u2013400.","DOI":"10.1109\/ICC-ROBINS64345.2025.11086287"},{"key":"532_CR29","doi-asserted-by":"crossref","unstructured":"Srinu S, Jeremia M, FMS, A. Development of logistic regression based spectrum sensing algorithm using extreme eigenvalues. In: International conference in advances in power, signal, and information technology. 2023;123\u2013130.","DOI":"10.1109\/APSIT58554.2023.10201699"},{"issue":"7","key":"532_CR30","doi-asserted-by":"publisher","first-page":"4041","DOI":"10.3390\/su14074041","volume":"14","author":"Z Uddin","year":"2022","unstructured":"Uddin Z, Qamar A, Alharbi AG, Orakzai FA, Ahmad A. Detection of multiple drones in a time-varying scenario using acoustic signals. Sustainability. 2022;14(7):4041.","journal-title":"Sustainability"},{"key":"532_CR31","doi-asserted-by":"publisher","first-page":"1440727","DOI":"10.3389\/frcmn.2024.1440727","volume":"5","author":"M Mrabet","year":"2024","unstructured":"Mrabet M, Sliti M, Ben Ammar L. Machine learning algorithms applied for drone detection and classification: benefits and challenges. Front Commun Netw. 2024;5:1440727.","journal-title":"Front Commun Netw"},{"issue":"1","key":"532_CR32","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1007\/s10846-024-02216-x","volume":"111","author":"J Najafi","year":"2025","unstructured":"Najafi J, Mirzakuchaki S, Shamaghdari S. Autonomous drone detection and classification using computer vision and prony algorithm-based frequency feature extraction. J Intell Robot Syst. 2025;111(1):8.","journal-title":"J Intell Robot Syst"}],"container-title":["Discover Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-025-00532-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44163-025-00532-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-025-00532-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T12:18:57Z","timestamp":1760703537000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44163-025-00532-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,17]]},"references-count":32,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["532"],"URL":"https:\/\/doi.org\/10.1007\/s44163-025-00532-1","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-6688717\/v1","asserted-by":"object"}]},"ISSN":["2731-0809"],"issn-type":[{"value":"2731-0809","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,17]]},"assertion":[{"value":"17 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 October 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"273"}}