{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T15:11:24Z","timestamp":1773414684798,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T00:00:00Z","timestamp":1687996800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China (NSFC)","doi-asserted-by":"publisher","award":["62101160"],"award-info":[{"award-number":["62101160"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China (NSFC)","doi-asserted-by":"publisher","award":["61975043"],"award-info":[{"award-number":["61975043"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The growing intelligence and prevalence of drones have led to an increase in their disorderly and illicit usage, posing substantial risks to aviation and public safety. This paper focuses on addressing the issue of drone detection through surveillance cameras. Drone targets in images possess distinctive characteristics, including small size, weak energy, low contrast, and limited and varying features, rendering precise detection a challenging task. To overcome these challenges, we propose a novel detection method that extends the input of YOLOv5s to a continuous sequence of images and inter-frame optical flow, emulating the visual mechanisms employed by humans. By incorporating the image sequence as input, our model can leverage both temporal and spatial information, extracting more features of small and weak targets through the integration of spatiotemporal data. This integration augments the accuracy and robustness of drone detection. Furthermore, the inclusion of optical flow enables the model to directly perceive the motion information of drone targets across consecutive frames, enhancing its ability to extract and utilize features from dynamic objects. Comparative experiments demonstrate that our proposed method of extended input significantly enhances the network\u2019s capability to detect small moving targets, showcasing competitive performance in terms of accuracy and speed. Specifically, our method achieves a final average precision of 86.87%, representing a noteworthy 11.49% improvement over the baseline, and the speed remains above 30 frames per second. Additionally, our approach is adaptable to other detection models with different backbones, providing valuable insights for domains such as Urban Air Mobility and autonomous driving.<\/jats:p>","DOI":"10.3390\/s23136037","type":"journal-article","created":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T01:14:12Z","timestamp":1688087652000},"page":"6037","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Enhancing UAV Detection in Surveillance Camera Videos through Spatiotemporal Information and Optical Flow"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2306-7200","authenticated-orcid":false,"given":"Yu","family":"Sun","sequence":"first","affiliation":[{"name":"Research Center for Space Optical Engineering, Harbin Institute of Technology, Harbin 150001, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5504-8480","authenticated-orcid":false,"given":"Xiyang","family":"Zhi","sequence":"additional","affiliation":[{"name":"Research Center for Space Optical Engineering, Harbin Institute of Technology, Harbin 150001, China"}]},{"given":"Haowen","family":"Han","sequence":"additional","affiliation":[{"name":"Research Center for Space Optical Engineering, Harbin Institute of Technology, Harbin 150001, China"}]},{"given":"Shikai","family":"Jiang","sequence":"additional","affiliation":[{"name":"Research Center for Space Optical Engineering, Harbin Institute of Technology, Harbin 150001, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3927-2436","authenticated-orcid":false,"given":"Tianjun","family":"Shi","sequence":"additional","affiliation":[{"name":"Research Center for Space Optical Engineering, Harbin Institute of Technology, Harbin 150001, China"}]},{"given":"Jinnan","family":"Gong","sequence":"additional","affiliation":[{"name":"Research Center for Space Optical Engineering, Harbin Institute of Technology, Harbin 150001, China"}]},{"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Research Center for Space Optical Engineering, Harbin Institute of Technology, Harbin 150001, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Behera, D.K., and Raj, A.B. (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_2","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.techsoc.2016.03.003","article-title":"A Technoethical Review of Commercial Drone Use in the Context of Governance, Ethics, and Privacy","volume":"46","author":"Luppicini","year":"2016","journal-title":"Technol. Soc."},{"key":"ref_3","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_4","doi-asserted-by":"crossref","unstructured":"Nex, F., and Remondino, F. (2019). Preface: Latest Developments, Methodologies, and Applications Based on UAV Platforms. Drones, 3.","DOI":"10.3390\/drones3010026"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"de Angelis, E.L., Giulietti, F., Rossetti, G., Turci, M., and Albertazzi, C. (2023). Toward Smart Air Mobility: Control System Design and Experimental Validation for an Unmanned Light Helicopter. Drones, 7.","DOI":"10.3390\/drones7050288"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"69575","DOI":"10.1109\/ACCESS.2019.2919332","article-title":"Convolutional Neural Network-Based Real-Time Object Detection and Tracking for Parrot AR Drone 2","volume":"7","author":"Rohan","year":"2019","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"772","DOI":"10.1049\/iet-smt.2018.5563","article-title":"Extraction of Doppler Signature of Micro-to-Macro Rotations\/Motions Using Continuous Wave Radar-Assisted Measurement System","volume":"14","author":"Kumawat","year":"2020","journal-title":"IET Sci. Meas. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Seo, Y., Jang, B., and Im, S. (2018, January 27\u201330). Drone Detection Using Convolutional Neural Networks with Acoustic STFT Features. Proceedings of the 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Munich, Germany.","DOI":"10.1109\/AVSS.2018.8639425"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"60","DOI":"10.2352\/ISSN.2470-1173.2017.10.IMAWM-168","article-title":"Drone Detection by Acoustic Signature Identification","volume":"10","author":"Bernardini","year":"2017","journal-title":"Electron. Imaging"},{"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":"Ferreira, R., Gaspar, J., Sebasti\u00e3o, P., and Souto, N. (2022). A Software Defined Radio Based Anti-UAV Mobile System with Jamming and Spoofing Capabilities. Sensors, 22.","DOI":"10.3390\/s22041487"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Mahdavi, F., and Rajabi, R. (2020, January 5\u20137). Drone Detection Using Convolutional Neural Networks. Proceedings of the 2020 6th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS), Tehran, Iran.","DOI":"10.1109\/ICSPIS51611.2020.9349620"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Liu, H. (July, January 28). Unmanned Aerial Vehicle Detection and Identification Using Deep Learning. Proceedings of the 2021 International Wireless Communications and Mobile Computing (IWCMC), Harbin, China.","DOI":"10.1109\/IWCMC51323.2021.9498629"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Singha, S., and Aydin, B. (2021). Automated Drone Detection Using YOLOv4. Drones, 5.","DOI":"10.3390\/drones5030095"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"416","DOI":"10.3390\/eng4010025","article-title":"Drone Detection Using YOLOv5","volume":"4","author":"Aydin","year":"2023","journal-title":"Eng"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"138669","DOI":"10.1109\/ACCESS.2019.2942944","article-title":"Machine Learning-Based Drone Detection and Classification: State-of-the-Art in Research","volume":"7","author":"Taha","year":"2019","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3703","DOI":"10.1016\/S0042-6989(00)00226-1","article-title":"Eye Movements and Visible Persistence Explain the Mislocalization of the Final Position of a Moving Target","volume":"40","author":"Kerzel","year":"2000","journal-title":"Vision Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1017\/S0140525X08003804","article-title":"Visual Prediction: Psychophysics and Neurophysiology of Compensation for Time Delays","volume":"31","author":"Nijhawan","year":"2008","journal-title":"Behav. Brain Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1098\/rspb.1986.0022","article-title":"Seeing Objects in Motion","volume":"227","author":"Burr","year":"1986","journal-title":"Proc. R. Soc. Lond. B Biol. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A.C. (2016, January 11\u201314). SSD: Single Shot Multibox Detector. Proceedings of the Computer Vision\u2014ECCV 2016: 14th European Conference, Amsterdam, The Netherlands. Part I 14.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_21","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (July, January 27). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal Loss for Dense Object Detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Hassan, S.A., Rahim, T., and Shin, S.Y. (2019, January 16\u201318). Real-Time UAV Detection Based on Deep Learning Network. Proceedings of the 2019 International Conference on Information and Communication Technology Convergence (ICTC), Jeju Island, Republic of Korea.","DOI":"10.1109\/ICTC46691.2019.8939564"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_25","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv."},{"key":"ref_26","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015, January 7\u201312). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. Proceedings of the Advances in Neural Information Processing Systems 28, Montreal, QC, Canada."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Cai, Z., and Vasconcelos, N. (2018, January 18\u201323). Cascade R-CNN: Delving into High Quality Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00644"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Magoulianitis, V., Ataloglou, D., Dimou, A., Zarpalas, D., and Daras, P. (2019, January 18\u201321). Does Deep Super-Resolution Enhance UAV Detection?. Proceedings of the 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Taipei, Taiwan.","DOI":"10.1109\/AVSS.2019.8909865"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhu, X., Wang, Y., Dai, J., Yuan, L., and Wei, Y. (2017, January 22\u201329). Flow-Guided Feature Aggregation for Video Object Detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.52"},{"key":"ref_31","unstructured":"Liu, M., and Zhu, M. (2018, January 18\u201323). Mobile Video Object Detection with Temporally-Aware Feature Maps. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA."},{"key":"ref_32","unstructured":"Wu, H., Chen, Y., Wang, N., and Zhang, Z. (November, January 27). Sequence Level Semantics Aggregation for Video Object Detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Xiao, F., and Lee, Y.J. (2018, January 8\u201314). Video Object Detection with an Aligned Spatial-Temporal Memory. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01237-3_30"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"846","DOI":"10.1109\/TMM.2020.2990070","article-title":"Single Shot Video Object Detector","volume":"23","author":"Deng","year":"2020","journal-title":"IEEE Trans. Multimed."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Chen, Y., Cao, Y., Hu, H., and Wang, L. (2020, January 14\u201319). Memory Enhanced Global-Local Aggregation for Video Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01035"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"105483","DOI":"10.1016\/j.engappai.2022.105483","article-title":"Transformer-Based Moving Target Tracking Method for Unmanned Aerial Vehicle","volume":"116","author":"Sun","year":"2022","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Li, J., Ye, D.H., Chung, T., Kolsch, M., Wachs, J., and Bouman, C. (2016, January 9\u201314). Multi-Target Detection and Tracking from a Single Camera in Unmanned Aerial Vehicles (UAVs). Proceedings of the 2016 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, Republic of Korea.","DOI":"10.1109\/IROS.2016.7759733"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"4661","DOI":"10.2352\/ISSN.2470-1173.2018.10.IMAWM-466","article-title":"Deep Learning for Moving Object Detection and Tracking from a Single Camera in Unmanned Aerial Vehicles (UAVs)","volume":"10","author":"Ye","year":"2018","journal-title":"Electron. Imaging"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1109\/TPAMI.2016.2564408","article-title":"Detecting Flying Objects Using a Single Moving Camera","volume":"39","author":"Rozantsev","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Ashraf, M.W., Sultani, W., and Shah, M. (2021, January 19\u201325). Dogfight: Detecting Drones from Drones Videos. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00699"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature Pyramid Networks for Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Liao, H.-Y.M., Wu, Y.-H., Chen, P.-Y., Hsieh, J.-W., and Yeh, I.-H. (2020, January 14\u201319). CSPNet: A New Backbone That Can Enhance Learning Capability of CNN. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00203"},{"key":"ref_43","unstructured":"Targ, S., Almeida, D., and Lyman, K. (2016). ResNet in ResNet: Generalizing Residual Architectures. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1145\/212094.212141","article-title":"The Computation of Optical Flow","volume":"27","author":"Beauchemin","year":"1995","journal-title":"ACM Comput. Surv."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1007\/s11554-014-0423-0","article-title":"Massively Parallel Lucas Kanade Optical Flow for Real-Time Video Processing Applications","volume":"11","author":"Plyer","year":"2016","journal-title":"J. Real-Time Image Process."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zhang, H., Wang, Y., Dayoub, F., and Sunderhauf, N. (2021, January 19\u201325). VarifocalNet: An IoU-Aware Dense Object Detector. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Montreal, QC, Canada.","DOI":"10.1109\/CVPR46437.2021.00841"},{"key":"ref_47","unstructured":"Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., and Ren, D. (2020, January 7\u201312). Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression. Proceedings of the AAAI Conference on Artificial Intelligence 2020, New York, NY, USA."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/13\/6037\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:03:49Z","timestamp":1760126629000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/13\/6037"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,29]]},"references-count":47,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["s23136037"],"URL":"https:\/\/doi.org\/10.3390\/s23136037","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,29]]}}}