{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T00:38:04Z","timestamp":1776299884610,"version":"3.50.1"},"reference-count":116,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,12,29]],"date-time":"2023-12-29T00:00:00Z","timestamp":1703808000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["61873086"],"award-info":[{"award-number":["61873086"]}]},{"name":"National Natural Science Foundation of China","award":["2022YFB4703402"],"award-info":[{"award-number":["2022YFB4703402"]}]},{"name":"National Key R&amp;D Program of China","award":["61873086"],"award-info":[{"award-number":["61873086"]}]},{"name":"National Key R&amp;D Program of China","award":["2022YFB4703402"],"award-info":[{"award-number":["2022YFB4703402"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>With the rapid development of object detection technology for unmanned aerial vehicles (UAVs), it is convenient to collect data from UAV aerial photographs. They have a wide range of applications in several fields, such as monitoring, geological exploration, precision agriculture, and disaster early warning. In recent years, many methods based on artificial intelligence have been proposed for UAV object detection, and deep learning is a key area in this field. Significant progress has been achieved in the area of deep-learning-based UAV object detection. Thus, this paper presents a review of recent research on deep-learning-based UAV object detection. This survey provides an overview of the development of UAVs and summarizes the deep-learning-based methods in object detection for UAVs. In addition, the key issues in UAV object detection are analyzed, such as small object detection, object detection under complex backgrounds, object rotation, scale change, and category imbalance problems. Then, some representative solutions based on deep learning for these issues are summarized. Finally, future research directions in the field of UAV object detection are discussed.<\/jats:p>","DOI":"10.3390\/rs16010149","type":"journal-article","created":{"date-parts":[[2023,12,29]],"date-time":"2023-12-29T06:18:13Z","timestamp":1703830693000},"page":"149","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":150,"title":["A Survey of Object Detection for UAVs Based on Deep Learning"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1542-6392","authenticated-orcid":false,"given":"Guangyi","family":"Tang","sequence":"first","affiliation":[{"name":"College of Artificial Intelligence and Automation, Hohai University, Changzhou 213200, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7130-8331","authenticated-orcid":false,"given":"Jianjun","family":"Ni","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence and Automation, Hohai University, Changzhou 213200, China"},{"name":"College of Information Science and Engineering, Hohai University, Changzhou 213200, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6810-8675","authenticated-orcid":false,"given":"Yonghao","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence and Automation, Hohai University, Changzhou 213200, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6698-479X","authenticated-orcid":false,"given":"Yang","family":"Gu","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence and Automation, Hohai University, Changzhou 213200, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0394-9639","authenticated-orcid":false,"given":"Weidong","family":"Cao","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence and Automation, Hohai University, Changzhou 213200, China"},{"name":"College of Information Science and Engineering, Hohai University, Changzhou 213200, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Li, J., Chen, M., Hou, S., Wang, Y., Luo, Q., and Wang, C. (2023). An Improved S2A-Net Algorithm for Ship Object Detection in Optical Remote Sensing Images. Remote Sens., 15.","DOI":"10.3390\/rs15184559"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"5615614","DOI":"10.1109\/TGRS.2023.3294241","article-title":"Global to Local: A Scale-Aware Network for Remote Sensing Object Detection","volume":"61","author":"Gao","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Gao, L., Gao, H., Wang, Y., Liu, D., and Momanyi, B.M. (2023). Center-Ness and Repulsion: Constraints to Improve Remote Sensing Object Detection via RepPoints. Remote Sens., 15.","DOI":"10.3390\/rs15061479"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"104900","DOI":"10.1016\/j.compag.2019.104900","article-title":"Citrus rootstock evaluation utilizing UAV-based remote sensing and artificial intelligence","volume":"164","author":"Ampatzidis","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1459","DOI":"10.1109\/TIP.2004.836169","article-title":"Statistical modeling of complex backgrounds for foreground object detection","volume":"13","author":"Li","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"374","DOI":"10.20517\/ir.2023.22","article-title":"Deep learning-based scene understanding for autonomous robots: A survey","volume":"3","author":"Ni","year":"2023","journal-title":"Intell. Robot."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1109\/MGRS.2020.2979764","article-title":"Feature Extraction for Hyperspectral Imagery: The Evolution from Shallow to Deep: Overview and Toolbox","volume":"8","author":"Rasti","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"5966","DOI":"10.1109\/TGRS.2020.3015157","article-title":"Graph Convolutional Networks for Hyperspectral Image Classification","volume":"59","author":"Hong","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Liu, M., Wang, X., Zhou, A., Fu, X., Ma, Y., and Piao, C. (2020). Uav-yolo, Small object detection on unmanned aerial vehicle perspective. Sensors, 20.","DOI":"10.3390\/s20082238"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Yuanqiang, C., Du, D., Zhang, L., Wen, L., Wang, W., Wu, Y., and Lyu, S. (2020, January 12\u201316). Guided Attention Network for Object Detection and Counting on Drones. Proceedings of the 28th ACM International Conference on Multimedia, MM 2020, Virtual Event.","DOI":"10.1145\/3394171.3413816"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5001614","DOI":"10.1109\/TIM.2022.3146923","article-title":"An Improved Deep Network-Based Scene Classification Method for Self-Driving Cars","volume":"71","author":"Ni","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.isprsjprs.2018.04.003","article-title":"Multi-scale object detection in remote sensing imagery with convolutional neural networks","volume":"145","author":"Deng","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wu, X., Hong, D., Ghamisi, P., Li, W., and Tao, R. (2018). MsRi-CCF: Multi-scale and rotation-insensitive convolutional channel features for geospatial object detection. Remote Sens., 10.","DOI":"10.3390\/rs10121990"},{"key":"ref_16","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S.E., Fu, C.Y., and Berg, A.C. (2016). Proceedings of the Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, 11\u201314 October 2016, Springer."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5006915","DOI":"10.1109\/TIM.2023.3244819","article-title":"An Improved SSD-Like Deep Network-Based Object Detection Method for Indoor Scenes","volume":"72","author":"Ni","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Luo, X., Tian, X., Zhang, H., Hou, W., Leng, G., Xu, W., Jia, H., He, X., Wang, M., and Zhang, J. (2020). Fast automatic vehicle detection in UAV images using convolutional neural networks. Remote Sens., 12.","DOI":"10.3390\/rs12121994"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_20","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_21","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An incremental improvement. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Mehta, S., Rastegari, M., Shapiro, L., and Hajishirzi, H. (2019, January 16\u201320). ESPNetv2: A light-weight, power efficient, and general purpose convolutional neural network. Proceedings of the 32nd IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00941"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"165","DOI":"10.32604\/csse.2021.017016","article-title":"Deep learning for object detection: A survey","volume":"38","author":"Wang","year":"2021","journal-title":"Comput. Syst. Sci. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ni, J., Chen, Y., Chen, Y., Zhu, J., Ali, D., and Cao, W. (2020). A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods. Appl. Sci., 10.","DOI":"10.3390\/app10082749"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, Z., Wang, Y., Zhang, N., Zhang, Y., Zhao, Z., Xu, D., Ben, G., and Gao, Y. (2022). Deep Learning-Based Object Detection Techniques for Remote Sensing Images: A Survey. Remote Sens., 14.","DOI":"10.3390\/rs14102385"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.jmsy.2022.06.011","article-title":"Deep learning methods for object detection in smart manufacturing: A survey","volume":"64","author":"Ahmad","year":"2022","journal-title":"J. Manuf. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Valavanis, K.P., and Vachtsevanos, G.J. (2015). Handbook of Unmanned Aerial Vehicles, Springer.","DOI":"10.1007\/978-90-481-9707-1"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Cazzato, D., Cimarelli, C., Sanchez-Lopez, J.L., Voos, H., and Leo, M. (2020). A survey of computer vision methods for 2d object detection from unmanned aerial vehicles. J. Imaging, 6.","DOI":"10.3390\/jimaging6080078"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1109\/MGRS.2021.3115137","article-title":"Deep Learning for Unmanned Aerial Vehicle-Based Object Detection and Tracking: A survey","volume":"10","author":"Wu","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3158","DOI":"10.1109\/JSTARS.2023.3259200","article-title":"Detecting Historical Terrain Anomalies with UAV-LiDAR Data Using Spline-Approximation and Support Vector Machines","volume":"16","author":"Storch","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wu, S., Wang, L., Zeng, X., Wang, F., Liang, Z., and Ye, H. (2022). UAV-Mounted GPR for Object Detection Based on Cross-Correlation Background Subtraction Method. Remote Sens., 14.","DOI":"10.3390\/rs14205132"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1007\/s00138-013-0570-5","article-title":"Thermal cameras and applications: A survey","volume":"25","author":"Gade","year":"2014","journal-title":"Mach. Vis. Appl."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Mehmood, K., Ali, A., Jalil, A., Khan, B., Cheema, K.M., Murad, M., and Milyani, A.H. (2021). Efficient online object tracking scheme for challenging scenarios. Sensors, 21.","DOI":"10.3390\/s21248481"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.isprsjprs.2022.11.008","article-title":"Manipal-UAV person detection dataset: A step towards benchmarking dataset and algorithms for small object detection","volume":"195","author":"Akshatha","year":"2023","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.isprsjprs.2023.04.009","article-title":"OGMN: Occlusion-guided multi-task network for object detection in UAV images","volume":"199","author":"Li","year":"2023","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Liu, H., Fan, K., Ouyang, Q., and Li, N. (2021). Real-time small drones detection based on pruned yolov4. Sensors, 21.","DOI":"10.3390\/s21103374"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2515513","DOI":"10.1109\/TIM.2022.3196319","article-title":"Dense and Small Object Detection in UAV-Vision Based on a Global-Local Feature Enhanced Network","volume":"71","author":"Ye","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_38","first-page":"2701217","article-title":"Parallel CNN Network Learning-Based Video Object Recognition for UAV Ground Detection","volume":"2022","author":"Liu","year":"2022","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.isprsjprs.2021.01.008","article-title":"Growing status observation for oil palm trees using Unmanned Aerial Vehicle (UAV) images","volume":"173","author":"Zheng","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1109\/JMASS.2020.3025970","article-title":"Multiclass Object Detection in UAV Images Based on Rotation Region Network","volume":"1","author":"Xiao","year":"2020","journal-title":"IEEE J. Miniaturization Air Space Syst."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"61546","DOI":"10.1109\/ACCESS.2023.3262601","article-title":"R-YOLOv5: A Lightweight Rotational Object Detection Algorithm for Real-Time Detection of Vehicles in Dense Scenes","volume":"11","author":"Li","year":"2023","journal-title":"IEEE Access"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Vinci, A., Brigante, R., Traini, C., and Farinelli, D. (2023). Geometrical Characterization of Hazelnut Trees in an Intensive Orchard by an Unmanned Aerial Vehicle (UAV) for Precision Agriculture Applications. Remote Sens., 15.","DOI":"10.3390\/rs15020541"},{"key":"ref_43","first-page":"3029","article-title":"Improved algorithm of UAV search based on electric field model and simulation analysis","volume":"52","author":"Zhu","year":"2022","journal-title":"Jilin Daxue Xuebao (Gongxueban)\/J. Jilin Univ. (Eng. Technol. Ed.)"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1327","DOI":"10.1051\/jnwpu\/20224061327","article-title":"Image enhancement method based on exposure fusion for UAV aerial photography","volume":"40","author":"Li","year":"2022","journal-title":"Xibei Gongye Daxue Xuebao\/J. Northwestern Polytech. Univ."},{"key":"ref_45","first-page":"573","article-title":"A new method for constructing roads map in forest area using UAV images","volume":"23","author":"Cheng","year":"2023","journal-title":"J. Comput. Methods Sci. Eng."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Tian, Z., Shen, C., Chen, H., and He, T. (November, January 27). FCOS: Fully Convolutional One-Stage Object Detection. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00972"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., and Le, Q.V. (2020, January 13\u201319). EfficientDet: Scalable and efficient object detection. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"107017","DOI":"10.1016\/j.compag.2022.107017","article-title":"Drones in agriculture: A review and bibliometric analysis","volume":"198","author":"Rejeb","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Xu, X., Dong, S., Xu, T., Ding, L., Wang, J., Jiang, P., Song, L., and Li, J. (2023). FusionRCNN: LiDAR-Camera Fusion for Two-Stage 3D Object Detection. Remote Sens., 15.","DOI":"10.3390\/rs15071839"},{"key":"ref_50","unstructured":"Viola, P., and Jones, M. (2001, January 8\u201314). Rapid object detection using a boosted cascade of simple features. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Kauai, HI, USA."},{"key":"ref_51","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201325). Histograms of oriented gradients for human detection. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), San Diego, CA, USA."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","article-title":"Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition","volume":"37","author":"He","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Hossain, S., and Lee, D.J. (2019). Deep learning-based real-time multiple-object detection and tracking from aerial imagery via a flying robot with GPU-based embedded devices. Sensors, 19.","DOI":"10.3390\/s19153371"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Lu, Y., Guo, J., Guo, S., Fu, Q., and Xu, J. (2022, January 7\u201310). Study on Marine Fishery Law Enforcement Inspection System based on Improved YOLO V5 with UAV. Proceedings of the 2022 IEEE International Conference on Mechatronics and Automation, ICMA 2022, Guilin, China.","DOI":"10.1109\/ICMA54519.2022.9856327"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Lalak, M., and Wierzbicki, D. (2022). Automated Detection of Atypical Aviation Obstacles from UAV Images Using a YOLO Algorithm. Sensors, 22.","DOI":"10.3390\/s22176611"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Li, X., Yang, J., Li, Z., Yang, F., Chen, Y., Ren, J., and Duan, Y. (2022, January 17\u201322). Building Damage Detection for Extreme Earthquake Disaster Area Location from Post-Event Uav Images Using Improved SSD. Proceedings of the 2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpurs, Malaysia.","DOI":"10.1109\/IGARSS46834.2022.9884215"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Sommer, L.W., Schuchert, T., and Beyerer, J. (2017, January 24\u201331). Fast Deep Vehicle Detection in Aerial Images. Proceedings of the 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), Santa Rosa, CA, USA.","DOI":"10.1109\/WACV.2017.41"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1938","DOI":"10.1109\/LGRS.2015.2439517","article-title":"Fast Multiclass Vehicle Detection on Aerial Images","volume":"12","author":"Liu","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_60","first-page":"6004305","article-title":"Self-Attention Guidance and Multiscale Feature Fusion-Based UAV Image Object Detection","volume":"20","author":"Zhang","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Audebert, N., Le Saux, B., and Lefevre, S. (2017). Segment-before-detect: Vehicle detection and classification through semantic segmentation of aerial images. Remote Sens., 9.","DOI":"10.3390\/rs9040368"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.jvcir.2015.11.002","article-title":"Vehicle detection in aerial imagery: A small target detection benchmark","volume":"34","author":"Razakarivony","year":"2016","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_63","unstructured":"Li, C., Xu, C., Cui, Z., Wang, D., Jie, Z., Zhang, T., and Yang, J. (2019, January 16\u201320). Learning object-wise semantic representation for detection in remote sensing imagery. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Long Beach, CA, USA."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Xia, G.S., Bai, X., Ding, J., Zhu, Z., Belongie, S., Luo, J., Datcu, M., Pelillo, M., and Zhang, L. (2018, January 18\u201322). DOTA: A Large-Scale Dataset for Object Detection in Aerial Images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00418"},{"key":"ref_65","first-page":"173","article-title":"Learning Nonlocal Quadrature Contrast for Detection and Recognition of Infrared Rotary-Wing UAV Targets in Complex Background","volume":"60","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Uzkent, B., Yeh, C., and Ermon, S. (2020, January 1\u20135). Efficient object detection in large images using deep reinforcement learning. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Snowmass Village, CO, USA.","DOI":"10.1109\/WACV45572.2020.9093447"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/j.jvcir.2021.103058","article-title":"A lightweight multi-scale aggregated model for detecting aerial images captured by UAVs","volume":"77","author":"Li","year":"2021","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Du, D., Qi, Y., Yu, H., Yang, Y., Duan, K., Li, G., Zhang, W., Huang, Q., and Tian, Q. (2018, January 17\u201324). The unmanned aerial vehicle benchmark: Object detection and tracking. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-030-01249-6_23"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1758","DOI":"10.1109\/TCSVT.2019.2905881","article-title":"Small Object Detection in Unmanned Aerial Vehicle Images Using Feature Fusion and Scaling-Based Single Shot Detector with Spatial Context Analysis","volume":"30","author":"Liang","year":"2020","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"The pascal visual object classes (VOC) challenge","volume":"88","author":"Everingham","year":"2010","journal-title":"Int. J. Comput. Vis."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Wang, J., Shao, F., He, X., and Lu, G. (2022). A novel method of small object detection in uav remote sensing images based on feature alignment of candidate regions. Drones, 6.","DOI":"10.3390\/drones6100292"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1109\/LGRS.2016.2542358","article-title":"Convolutional Neural Network Based Automatic Object Detection on Aerial Images","volume":"13","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Zeng, S., Yang, W., Jiao, Y., Geng, L., and Chen, X. (2023). SCA-YOLO: A new small object detection model for UAV images. Vis. Comput., 1\u201317.","DOI":"10.1007\/s00371-023-02886-y"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Qian, Y., Wu, G., Sun, H., Li, W., and Xu, Y. (2021, January 8\u201313). Research on Small Object Detection in UAV Reconnaissance Images Based on Haar-Like Features and MobileNet-SSD Algorithm. Proceedings of the 2021 International Conference on Cyber Security Intelligence and Analytics (CSIA2021), Shenyang, China. Advances in Intelligent Systems and Computing.","DOI":"10.1007\/978-3-030-70042-3_101"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/j.neucom.2021.03.016","article-title":"A dual neural network for object detection in UAV images","volume":"443","author":"Tian","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Chen, Z., Wang, M., and Zhang, J. (2023). Object Detection in UAV Images Based on Improved YOLOv5, Springer.","DOI":"10.1007\/978-3-031-31775-0_28"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Wu, C., Liang, R., He, S., and Wang, H. (2022, January 7\u20139). Real-Time Vehicle Detection Method Based on Aerial Image in Complex Background. Proceedings of the China Conference on Command and Control, Beijing, China. Lecture Notes in Electrical Engineering.","DOI":"10.1007\/978-981-19-6052-9_46"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Xi, Y., Jia, W., Miao, Q., Liu, X., Fan, X., and Li, H. (2022). FiFoNet: Fine-Grained Target Focusing Network for Object Detection in UAV Images. Remote Sens., 14.","DOI":"10.3390\/rs14163919"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Yang, F., Fan, H., Chu, P., Blasch, E., and Ling, H. (November, January 27). Clustered Object Detection in Aerial Images. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00840"},{"key":"ref_80","unstructured":"Ju, S., Zhang, X., Mao, Z., and Du, H. (2022). Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery: Proceedings of the ICNC-FSKD 2021 17, Guiyang, China, 24\u201326 July 2021, Springer."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"4347","DOI":"10.1007\/s11042-018-5739-5","article-title":"A reinforcement learning approach for UAV target searching and tracking","volume":"78","author":"Wang","year":"2019","journal-title":"Multimed. Tools Appl."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"283","DOI":"10.3103\/S1060992X19040118","article-title":"Object Detection with Deep Neural Networks for Reinforcement Learning in the Task of Autonomous Vehicles Path Planning at the Intersection","volume":"28","author":"Yudin","year":"2019","journal-title":"Opt. Mem. Neural Netw. (Inf. Opt.)"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 3\u20137). Identity mappings in deep residual networks. Proceedings of the Computer Vision ECCV 2016: 14th European Conference, Scottsdale, AZ, USA. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Yang, X., Sun, H., Fu, K., Yang, J., Sun, X., Yan, M., and Guo, Z. (2018). Automatic ship detection in remote sensing images from google earth of complex scenes based on multiscale rotation Dense Feature Pyramid Networks. Remote Sens., 10.","DOI":"10.3390\/rs10010132"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"8448","DOI":"10.1007\/s10489-021-02893-3","article-title":"RSOD: Real-time small object detection algorithm in UAV-based traffic monitoring","volume":"52","author":"Sun","year":"2022","journal-title":"Appl. Intell."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Yang, X., Yang, J., Yan, J., Zhang, Y., Zhang, T., Guo, Z., Sun, X., and Fu, K. (November, January 27). SCRDet: Towards More Robust Detection for Small, Cluttered and Rotated Objects. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00832"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"2069","DOI":"10.1109\/TMM.2021.3075566","article-title":"Real-Time and Accurate UAV Pedestrian Detection for Social Distancing Monitoring in COVID-19 Pandemic","volume":"24","author":"Shao","year":"2022","journal-title":"IEEE Trans. Multimed."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Zhu, J., Yang, G., Feng, X., Li, X., Fang, H., Zhang, J., Bai, X., Tao, M., and He, Y. (2022). Detecting Wheat Heads from UAV Low-Altitude Remote Sensing Images Using Deep Learning Based on Transformer. Remote Sens., 14.","DOI":"10.3390\/rs14205141"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.isprsjprs.2014.10.002","article-title":"Multi-class geospatial object detection and geographic image classification based on collection of part detectors","volume":"98","author":"Cheng","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Xu, X., Wang, L., Shu, M., Liang, X., Ghafoor, A.Z., Liu, Y., Ma, Y., and Zhu, J. (2022). Detection and Counting of Maize Leaves Based on Two-Stage Deep Learning with UAV-Based RGB Image. Remote Sens., 14.","DOI":"10.3390\/rs14215388"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Ding, J., Xue, N., Long, Y., Xia, G.S., and Lu, Q. (2019, January 15\u201320). Learning roi transformer for oriented object detection in aerial images. Proceedings of the Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00296"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1016\/j.neucom.2022.06.049","article-title":"TS4Net: Two-stage sample selective strategy for rotating object detection","volume":"501","author":"Zhou","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Avola, D., Cinque, L., Diko, A., Fagioli, A., Foresti, G.L., Mecca, A., Pannone, D., and Piciarelli, C. (2021). MS-Faster R-CNN: Multi-stream backbone for improved Faster R-CNN object detection and aerial tracking from UAV images. Remote Sens., 13.","DOI":"10.3390\/rs13091670"},{"key":"ref_94","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 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., and Wei, Y. (2017, January 22\u201329). Deformable Convolutional Networks. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.89"},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Liu, Z., Gao, G., Sun, L., and Fang, Z. (2021, January 5\u20139). Hrdnet: High-Resolution Detection Network for Small Objects. Proceedings of the 2021 IEEE International Conference on Multimedia and Expo (ICME), Shenzhen, China.","DOI":"10.1109\/ICME51207.2021.9428241"},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Li, Q., Sun, M., Dong, L., Gao, X., Wang, Z., and Zhang, H. (2022, January 22\u201325). HCD-Mask: A multi-task model for small object detection and instance segmentation in high-resolution UAV images. Proceedings of the 2022 IEEE International Conference on Industrial Technology (ICIT), Shanghai, China.","DOI":"10.1109\/ICIT48603.2022.10002808"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"1452","DOI":"10.1109\/TPAMI.2020.2974745","article-title":"Gliding Vertex on the Horizontal Bounding Box for Multi-Oriented Object Detection","volume":"43","author":"Xu","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2020.3038405","article-title":"Object detection in UAV images via global density fused convolutional network","volume":"12","author":"Zhang","year":"2020","journal-title":"Remote Sens."},{"key":"ref_100","first-page":"267","article-title":"Center-Boundary Dual Attention for Oriented Object Detection in Remote Sensing Images","volume":"60","author":"Liu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Cores, D., Brea, V., and Mucientes, M. (2021, January 8\u201313). Spatio-Temporal Object Detection from UAV On-Board Cameras. Proceedings of the International Conference on Computer Analysis of Images and Patterns, Virtual Online. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-030-89131-2_13"},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Wu, J., Song, L., Wang, T., Zhang, Q., and Yuan, J. (2020, January 12\u201316). Forest r-cnn: Large-vocabulary long-tailed object detection and instance segmentation. Proceedings of the 28th ACM international Conference on Multimedia, Seattle, WA, USA.","DOI":"10.1145\/3394171.3413970"},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Robicquet, A., Sadeghian, A., Alahi, A., and Savarese, S. (2016, January 8\u201316). Learning social etiquette: Human trajectory understanding in crowded scenes. Proceedings of the Computer Vision ECCV 2016: 14th European Conference, Scottsdale, AZ, USA. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-319-46484-8_33"},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Mueller, M., Smith, N., and Ghanem, B. (2016, January 8\u201316). A benchmark and simulator for UAV tracking. Proceedings of the Computer Vision ECCV 2016: 14th European Conference, Amsterdam, The Netherlands. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-319-46448-0_27"},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Hsieh, M.R., Lin, Y.L., and Hsu, W.H. (2017, January 22\u201329). Drone-Based Object Counting by Spatially Regularized Regional Proposal Network. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.446"},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Barekatain, M., Marti, M., Shih, H.F., Murray, S., Nakayama, K., Matsuo, Y., and Prendinger, H. (2017, January 21\u201326). Okutama-Action: An Aerial View Video Dataset for Concurrent Human Action Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.267"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1109\/TPAMI.2019.2932429","article-title":"DAC-SDC Low Power Object Detection Challenge for UAV Applications","volume":"43","author":"Xu","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Mandal, M., Kumar, L.K., and Vipparthi, S.K. (2020, January 12\u201316). MOR-UAV: A Benchmark Dataset and Baselines for Moving Object Recognition in UAV Videos. Proceedings of the 28th ACM International Conference on Multimedia, Virtual Online.","DOI":"10.1145\/3394171.3413934"},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Bozcan, I., and Kayacan, E. (August, January 31). AU-AIR: A Multi-modal Unmanned Aerial Vehicle Dataset for Low Altitude Traffic Surveillance. Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France.","DOI":"10.1109\/ICRA40945.2020.9196845"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.neucom.2020.08.074","article-title":"An empirical study of multi-scale object detection in high resolution UAV images","volume":"421","author":"Zhang","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"4412012","DOI":"10.1109\/TGRS.2022.3183157","article-title":"Earthquake Crack Detection From Aerial Images Using a Deformable Convolutional Neural Network","volume":"60","author":"Yu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"He, X., Tang, Z., Deng, Y., Zhou, G., Wang, Y., and Li, L. (2023). UAV-based road crack object-detection algorithm. Autom. Constr., 154.","DOI":"10.1016\/j.autcon.2023.105014"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1038\/s41597-023-02799-4","article-title":"RescueNet: A High Resolution UAV Semantic Segmentation Dataset for Natural Disaster Damage Assessment","volume":"10","author":"Rahnemoonfar","year":"2023","journal-title":"Sci. Data"},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"108051","DOI":"10.1016\/j.compag.2023.108051","article-title":"Object detection and tracking on UAV RGB videos for early extraction of grape phenotypic traits","volume":"211","author":"Ariza","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Zhang, C., Ding, H., Shi, Q., and Wang, Y. (2022). Grape Cluster Real-Time Detection in Complex Natural Scenes Based on YOLOv5s Deep Learning Network. Agriculture, 12.","DOI":"10.3390\/agriculture12081242"},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Pichhika, H.C., and Subudhi, P. (2023, January 4\u20136). Detection of Multi-varieties of On-tree Mangoes using MangoYOLO5. Proceedings of the 2023 11th International Symposium on Electronic Systems Devices and Computing (ESDC), Sri City, India.","DOI":"10.1109\/ESDC56251.2023.10149849"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/1\/149\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:44:07Z","timestamp":1760132647000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/1\/149"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,29]]},"references-count":116,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["rs16010149"],"URL":"https:\/\/doi.org\/10.3390\/rs16010149","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,29]]}}}