{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T11:02:27Z","timestamp":1783940547536,"version":"3.55.0"},"reference-count":49,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T00:00:00Z","timestamp":1660262400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Department for Transport courtesy of the Future Aviation Security Solutions (FASS) programme"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>We present a convolutional neural network (CNN) that identifies drone models in real-life videos. The neural network is trained on synthetic images and tested on a real-life dataset of drone videos. To create the training and validation datasets, we show a method of generating synthetic drone images. Domain randomization is used to vary the simulation parameters such as model textures, background images, and orientation. Three common drone models are classified: DJI Phantom, DJI Mavic, and DJI Inspire. To test the performance of the neural network model, Anti-UAV, a real-life dataset of flying drones is used. The proposed method reduces the time-cost associated with manually labelling drones, and we prove that it is transferable to real-life videos. The CNN achieves an overall accuracy of 92.4%, a precision of 88.8%, a recall of 88.6%, and an f1 score of 88.7% when tested on the real-life dataset.<\/jats:p>","DOI":"10.3390\/jimaging8080218","type":"journal-article","created":{"date-parts":[[2022,8,15]],"date-time":"2022-08-15T01:47:21Z","timestamp":1660528041000},"page":"218","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Drone Model Classification Using Convolutional Neural Network Trained on Synthetic Data"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6795-6216","authenticated-orcid":false,"given":"Mariusz","family":"Wisniewski","sequence":"first","affiliation":[{"name":"Digital Aviation Research and Technology Centre (DARTeC), Cranfield University, Cranfield MK43 0FQ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7839-3949","authenticated-orcid":false,"given":"Zeeshan A.","family":"Rana","sequence":"additional","affiliation":[{"name":"Digital Aviation Research and Technology Centre (DARTeC), Cranfield University, Cranfield MK43 0FQ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8705-5308","authenticated-orcid":false,"given":"Ivan","family":"Petrunin","sequence":"additional","affiliation":[{"name":"Digital Aviation Research and Technology Centre (DARTeC), Cranfield University, Cranfield MK43 0FQ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wisniewski, M., Rana, Z.A., and Petrunin, I. (2021, January 3\u20137). Drone Model Identification by Convolutional Neural Network from Video Stream. Proceedings of the 2021 IEEE\/AIAA 40th Digital Avionics Systems Conference (DASC), San Antonio, TX, USA.","DOI":"10.1109\/DASC52595.2021.9594392"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Lykou, G., Moustakas, D., and Gritzalis, D. (2020). Defending Airports from UAS: A Survey on Cyber-Attacks and Counter-Drone Sensing Technologies. Sensors, 20.","DOI":"10.3390\/s20123537"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Farlik, J., Kratky, M., Casar, J., and Stary, V. (2019). Multispectral Detection of Commercial Unmanned Aerial Vehicles. Sensors, 19.","DOI":"10.3390\/s19071517"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3448115","article-title":"DronePrint: Acoustic Signatures for Open-set Drone Detection and Identification with Online Data","volume":"5","author":"Kolamunna","year":"2021","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Liu, H., Wei, Z., Chen, Y., Pan, J., Lin, L., and Ren, Y. (2017, January 19\u201321). Drone Detection Based on an Audio-Assisted Camera Array. Proceedings of the 2017 IEEE Third International Conference on Multimedia Big Data (BigMM), Laguna Hills, CA, USA.","DOI":"10.1109\/BigMM.2017.57"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ciaburro, G., Iannace, G., and Trematerra, A. (2020). Research for the Presence of Unmanned Aerial Vehicle inside Closed Environments with Acoustic Measurements. Buildings, 10.","DOI":"10.3390\/buildings10050096"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1177\/1351010X20917856","article-title":"Acoustical Unmanned Aerial Vehicle Detection in Indoor Scenarios Using Logistic Regression Model","volume":"28","author":"Iannace","year":"2021","journal-title":"Build. Acoust."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Jamil, S., Rahman, M., Ullah, A., Badnava, S., Forsat, M., and Mirjavadi, S.S. (2020). Malicious UAV Detection Using Integrated Audio and Visual Features for Public Safety Applications. Sensors, 20.","DOI":"10.3390\/s20143923"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Nemer, I., Sheltami, T., Ahmad, I., Yasar, A.U.H., and Abdeen, M.A.R. (2021). RF-Based UAV Detection and Identification Using Hierarchical Learning Approach. Sensors, 21.","DOI":"10.3390\/s21061947"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Chiper, F.L., Martian, A., Vladeanu, C., Marghescu, I., Craciunescu, R., and Fratu, O. (2022). Drone Detection and Defense Systems: Survey and a Software-Defined Radio-Based Solution. Sensors, 22.","DOI":"10.3390\/s22041453"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Yang, S., Qin, H., Liang, X., and Gulliver, T. (2019). An Improved Unauthorized Unmanned Aerial Vehicle Detection Algorithm Using Radiofrequency-Based Statistical Fingerprint Analysis. Sensors, 19.","DOI":"10.3390\/s19020274"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Jahangir, M., and Baker, C. (2016, January 22\u201323). Robust Detection of Micro-UAS Drones with L-Band 3-D Holographic Radar. Proceedings of the 2016 Sensor Signal Processing for Defence (SSPD), Edinburgh, UK.","DOI":"10.1109\/SSPD.2016.7590610"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Dale, H., Baker, C., Antoniou, M., and Jahangir, M. (2020, January 28\u201330). An Initial Investigation into Using Convolutional Neural Networks for Classification of Drones. Proceedings of the 2020 IEEE International Radar Conference (RADAR), Washington, DC, USA.","DOI":"10.1109\/RADAR42522.2020.9114745"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1813","DOI":"10.1049\/el.2015.3038","article-title":"Classification of Loaded\/Unloaded Micro-drones Using Multistatic Radar","volume":"51","author":"Fioranelli","year":"2015","journal-title":"Electron. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1017\/S1759078714000282","article-title":"Classification of Small UAVs and Birds by Micro-Doppler Signatures","volume":"6","author":"Molchanov","year":"2014","journal-title":"Int. J. Microw. Wirel. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1109\/LGRS.2016.2624820","article-title":"Drone Classification Using Convolutional Neural Networks With Merged Doppler Images","volume":"14","author":"Kim","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1625","DOI":"10.1007\/s11554-019-00921-7","article-title":"Real-Time High-Resolution Omnidirectional Imaging Platform for Drone Detection and Tracking","volume":"17","author":"Demir","year":"2020","journal-title":"J. Real-Time Image Process."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Seidaliyeva, U., Akhmetov, D., Ilipbayeva, L., and Matson, E.T. (2020). Real-Time and Accurate Drone Detection in a Video with a Static Background. Sensors, 20.","DOI":"10.3390\/s20143856"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"137365","DOI":"10.1109\/ACCESS.2019.2941509","article-title":"Design and Training of Deep CNN-Based Fast Detector in Infrared SUAV Surveillance System","volume":"7","author":"Zhang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_20","unstructured":"Jiang, N., Wang, K., Peng, X., Yu, X., Wang, Q., Xing, J., Li, G., Zhao, J., Guo, G., and Han, Z. (2021). Anti-UAV: A Large Multi-Modal Benchmark for UAV Tracking. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Coluccia, A., Fascista, A., Schumann, A., Sommer, L., Ghenescu, M., Avenue, A.O., and Piatrik, T. (2021, January 16\u201319). Drone-vs-Bird Detection Challenge at IEEE AVSS2019. Proceedings of the 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Washington, DC, USA.","DOI":"10.1109\/AVSS.2019.8909876"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Isaac-Medina, B.K.S., Poyser, M., Organisciak, D., Willcocks, C.G., Breckon, T.P., and Shum, H.P.H. (2021, January 11\u201317). Unmanned Aerial Vehicle Visual Detection and Tracking Using Deep Neural Networks: A Performance Benchmark. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision Workshops (ICCVW), Montreal, BC, Canada.","DOI":"10.1109\/ICCVW54120.2021.00142"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Organisciak, D., Poyser, M., Alsehaim, A., Hu, S., Isaac-Medina, B.K.S., Breckon, T.P., and Shum, H.P.H. (2021). UAV-ReID: A Benchmark on Unmanned Aerial Vehicle Re-identification in Video Imagery. arXiv.","DOI":"10.5220\/0010836600003124"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Voigtlaender, P., Luiten, J., Torr, P.H.S., and Leibe, B. (2020). Siam R-CNN: Visual Tracking by Re-Detection. arXiv.","DOI":"10.1109\/CVPR42600.2020.00661"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Craye, C., and Ardjoune, S. (2019, January 18\u201321). Spatio-Temporal Semantic Segmentation for Drone Detection. Proceedings of the 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Taipei, Taiwan.","DOI":"10.1109\/AVSS.2019.8909854"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"13139","DOI":"10.1109\/ACCESS.2022.3147264","article-title":"An Object Detection Algorithm for Rotary-Wing UAV Based on AWin Transformer","volume":"10","author":"Fan","year":"2022","journal-title":"IEEE Access"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"128-1","DOI":"10.2352\/ISSN.2470-1173.2018.09.SRV-128","article-title":"Using Shape Descriptors for UAV Detection","volume":"30","author":"Unlu","year":"2018","journal-title":"Electron. Imaging"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Samadzadegan, F., Dadrass Javan, F., Ashtari Mahini, F., and Gholamshahi, M. (2022). Detection and Recognition of Drones Based on a Deep Convolutional Neural Network Using Visible Imagery. Aerospace, 9.","DOI":"10.3390\/aerospace9010031"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Lee, D., Gyu La, W., and Kim, H. (2018, January 17\u201319). Drone Detection and Identification System Using Artificial Intelligence. Proceedings of the 2018 International Conference on Information and Communication Technology Convergence (ICTC), Jeju, Korea.","DOI":"10.1109\/ICTC.2018.8539442"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Behera, D.K., and Bazil Raj, A. (2020, January 13\u201315). Drone Detection and Classification Using Deep Learning. Proceedings of the 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India.","DOI":"10.1109\/ICICCS48265.2020.9121150"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Seidaliyeva, U., Alduraibi, M., Ilipbayeva, L., and Almagambetov, A. (2020, January 9\u201311). Detection of Loaded and Unloaded UAV Using Deep Neural Network. Proceedings of the 2020 Fourth IEEE International Conference on Robotic Computing (IRC), Taichung, Taiwan.","DOI":"10.1109\/IRC.2020.00093"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Chen, Y., Aggarwal, P., Choi, J., and Kuo, C.C.J. (2017, January 12\u201315). A Deep Learning Approach to Drone Monitoring. Proceedings of the 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Kuala Lumpur, Malaysia.","DOI":"10.1109\/APSIPA.2017.8282120"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Peng, J., Zheng, C., Cui, T., Cheng, Y., and Si, L. (June, January 28). Using Images Rendered by PBRT to Train Faster R-CNN for UAV Detection. Proceedings of the 26th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision\u20192017, Plzen, Czech Republic.","DOI":"10.24132\/CSRN.2018.2802.3"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Scholes, S., Ruget, A., Mora-Martin, G., Zhu, F., Gyongy, I., and Leach, J. (2021). DronePose: The Identification, Segmentation, and Orientation Detection of Drones via Neural Networks. arXiv.","DOI":"10.1109\/ACCESS.2022.3162866"},{"key":"ref_35","unstructured":"Li, C., Sun, S.C., Wei, Z., Tsourdos, A., and Guo, W. (2021). Scarce Data Driven Deep Learning of Drones via Generalized Data Distribution Space. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Tremblay, J., Prakash, A., Acuna, D., Brophy, M., Jampani, V., Anil, C., To, T., Cameracci, E., Boochoon, S., and Birchfield, S. (2018). Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization. arXiv.","DOI":"10.1109\/CVPRW.2018.00143"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Tobin, J., Fong, R., Ray, A., Schneider, J., Zaremba, W., and Abbeel, P. (2017, January 24\u201328). Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World. Proceedings of the 2017 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, Canada.","DOI":"10.1109\/IROS.2017.8202133"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Ward, C.M., Harguess, J., and Hilton, C. (2018, January 22\u201325). Ship Classification from Overhead Imagery Using Synthetic Data and Domain Adaptation. Proceedings of the OCEANS 2018 MTS\/IEEE Charleston, Charleston, SC, USA.","DOI":"10.1109\/OCEANS.2018.8604662"},{"key":"ref_39","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L. (2019, January 8\u201314). PyTorch: An Imperative Style, High-Performance Deep Learning Library. Proceedings of the 33rd Annual Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, BC, Canada."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Li, F.-F. (2009, January 20\u201325). ImageNet: A Large-Scale Hierarchical Image Database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_41","unstructured":"Qian, N. (2021, January 3\u20137). On the Momentum TeArmlgoinritGhrmads Ient Descent Learning. Proceedings of the IEEE\/AIAA 40th Digital Avionics Systems Conference (DASC), San Antonio, TX, USA."},{"key":"ref_42","unstructured":"Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R.R. (2012). Improving Neural Networks by Preventing Co-Adaptation of Feature Detectors. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.ipm.2009.03.002","article-title":"A Systematic Analysis of Performance Measures for Classification Tasks","volume":"45","author":"Sokolova","year":"2009","journal-title":"Inf. Process. Manag."},{"key":"ref_44","unstructured":"Hinterstoisser, S., Lepetit, V., Wohlhart, P., and Konolige, K. (2017). On Pre-Trained Image Features and Synthetic Images for Deep Learning. arXiv."},{"key":"ref_45","unstructured":"Smilkov, D., Thorat, N., Kim, B., Vi\u00e9gas, F., and Wattenberg, M. (2017). SmoothGrad: Removing Noise by Adding Noise. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015). Deep Residual Learning for Image Recognition. arXiv.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Zagoruyko, S., and Komodakis, N. (2017). Wide Residual Networks. arXiv.","DOI":"10.5244\/C.30.87"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van der Maaten, L., and Weinberger, K.Q. (2018). Densely Connected Convolutional Networks. arXiv.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_49","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/8\/8\/218\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:07:59Z","timestamp":1760141279000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/8\/8\/218"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,12]]},"references-count":49,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["jimaging8080218"],"URL":"https:\/\/doi.org\/10.3390\/jimaging8080218","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,12]]}}}