The DOI matches its Crossref record, with a small difference in title or year; 167 of the 169 references it cites were found in a registry or an index.
Detection and Tracking Meet Drones Challenge
Open accessAbstract
Drones, or general UAVs, equipped with cameras have been fast deployed with a wide range of applications, including agriculture, aerial photography, and surveillance. Consequently, automatic understanding of visual data collected from drones becomes highly demanding, bringing computer vision and drones more and more closely. To promote and track the developments of object detection and tracking algorithms, we have organized three challenge workshops in conjunction with ECCV 2018, ICCV 2019 and ECCV 2020, attracting more than 100 teams around the world. We provide a large-scale drone captured dataset, VisDrone, which includes four tracks, i.e., (1) image object detection, (2) video object detection, (3) single object tracking, and (4) multi-object tracking. In this paper, we first present a thorough review of object detection and tracking datasets and benchmarks, and discuss the challenges of collecting large-scale drone-based object detection and tracking datasets with fully manual annotations. After that, we describe our VisDrone dataset, which is captured over various urban/suburban areas of 14 different cities across China from North to South. Being the largest such dataset ever published, VisDrone enables extensive evaluation and investigation of visual analysis algorithms for the drone platform. We provide a detailed analysis of the current state of the field of large-scale object detection and tracking on drones, and conclude the challenge as well as propose future directions. We expect the benchmark largely boost the research and development in video analysis on drone platforms. All the datasets and experimental results can be downloaded from https://github.com/VisDrone/VisDrone-Dataset.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“It includes 263 video sequences and more than 10000 static images, all of which were collected using various drones in 14 different cities in diverse weather and lighting conditions and at various altitudes.”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [40]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Another relevant dataset is Visdrone [39], [40], the largest drone-based aerial dataset, which contains four tasks: object detection in images, videos, SOT, and MOT.”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [40]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Squeeze-and-Excitation Networks
Hu, J., Shen, L., & Sun, G. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00745 · Cited by 35,667
(2016) · Cited by 31,590
Feature Pyramid Networks for Object Detection
Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.106 · Cited by 30,939
Focal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.324 · Cited by 30,581
He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.322 · Cited by 29,137
Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105
Flagged first, then by citations
2 of the 169 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- 10.1007/978-3-030-58548-8_28 · DOI · Paper page
Cited by
Sorted by citations
Items 301–400 of 1,000
Xi, Y., Jia, W., Miao, Q., Feng, J., Liu, X., & Li, F. (2023) · Remote Sensing · doi:10.3390/rs15061487 · Cited by 12
Xaitk-Saliency: An Open Source Explainable AI Toolkit for Saliency
Hu, B., Tunison, P., RichardWebster, B., & Hoogs, A. J. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i13.26871 · Cited by 12
An Improved YOLOv8 Detector for Multi-Scale Target Detection in Remote Sensing Images
Yue, M., Zhang, L., Zhang, Y., & Zhang, H. (2024) · IEEE Access · doi:10.1109/access.2024.3444606 · Cited by 12
A UAV Aerial Image Target Detection Algorithm Based on YOLOv7 Improved Model
Qin, J., Yu, W., Feng, X., Meng, Z., & Tan, C.-H. (2024) · Electronics · doi:10.3390/electronics13163277 · Cited by 12
Object Detection and Classification Framework for Analysis of Video Data Acquired from Indian Roads
Padia, A., Aryan, T. N., Thummagunti, S., Sharma, V., Vanahalli, M. K., Prasad, B. M. P., Girish, G. N., Kim, Y.-G., & N, P. K. B. (2024) · Sensors · doi:10.3390/s24196319 · Cited by 12
DKA-YOLO: Enhanced Small Object Detection via Dilation Kernel Aggregation Convolution Modules
Qiu, Y., Sha, F., & Niu, L. (2024) · IEEE Access · doi:10.1109/access.2024.3515201 · Cited by 12
FLDet: Faster and Lighter Aerial Object Detector
Wang, S., Liu, K., Huang, J., & Li, X. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3516760 · Cited by 12
MLSA-YOLO: a multi-level feature fusion and scale-adaptive framework for small object detection
Peng, J., Lv, K., Wang, G., Xiao, W., Ran, T., & Yuan, L. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-06961-0 · Cited by 13
SF‐YOLO: A Novel YOLO Framework for Small Object Detection in Aerial Scenes
Sun, M., Wang, L., Jiang, W., Dharejo, F. A., Mao, G., & Timofte, R. (2025) · IET Image Processing · doi:10.1049/ipr2.70027 · Cited by 12
Kos, A., Majek, K., & Belter, D. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110852 · Cited by 14
Experimental Validation of UAV Search and Detection System in Real Wilderness Environment
Dumenčić, S., Lanča, L., Jakac, K., & Ivić, S. (2025) · Drones · doi:10.3390/drones9070473 · Cited by 12
Ballesteros, J. R., Torres, G. S., & Branch, J. W. (2022) · Data · doi:10.3390/data7040050 · Cited by 12
Varroa Mite Detection Using Deep Learning Techniques
Divasón, J., Martínez-de-Pisón, F. J., Romero, A., Santolaria, P., & Yániz, J. L. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-40725-3_28 · Cited by 12
Gong, L., Huang, X., Chen, J., Xiao, M., & Chao, Y. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107270 · Cited by 12
A Review on Intention-aware and Interaction-aware Trajectory Prediction for Autonomous VehiclesPreprint
Gomes, I. P., & Wolf, D. F. (2022) · doi:10.36227/techrxiv.19337447 · Cited by 12
A Texture Reconstructive Downsampling for Multi-Scale Object Detection in UAV Remote-Sensing Images
Zheng, W., Xiong, B., Chen, J., Ou, Q., & Yu, L. (2025) · Sensors · doi:10.3390/s25051569 · Cited by 11
Three-Stage Global Channel Pruning for Resources-Limited Platform
Chen, Y., Li, R., Li, W., Wang, J., & Li, R. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3292152 · Cited by 12
Divasón, J., Romero, A., Martínez-de-Pisón, F. J., Casalongue, M., Silvestre, M. A., Santolaria, P., & Yániz, J. L. (2024) · Sensors · doi:10.3390/s24123828 · Cited by 14
Zheng, W., Xu, H., Li, P., Wang, R., & Shao, X. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3438459 · Cited by 15
SKYSCENES: A Synthetic Dataset for Aerial Scene Understanding
Khose, S., Pal, A., Agarwal, A., Deepanshi, Hoffman, J., & Chattopadhyay, P. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72986-7_2 · Cited by 12
SFTrack: A Robust Scale and Motion Adaptive Algorithm for Tracking Small and Fast Moving Objects
Song, I., & Lee, J. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10802537 · Cited by 11
HCTD: A CNN-transformer hybrid for precise object detection in UAV aerial imagery
Xue, H., Tang, Z., Xia, Y., Wang, L., & Li, L. (2025) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2025.104409 · Cited by 11
Cross-DINO: Cross the Deep MLP and Transformer for Small Object Detection
Cao, G., Huang, W., Lan, X., Zhang, J., Jiang, D., & Wang, Y. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3599074 · Cited by 13
Qin, H., Wu, Y., Dong, F., & Sun, S. (2022) · IET Computer Vision · doi:10.1049/cvi2.12089 · Cited by 11
A Versatile Detection Method for Various Contrast Enhancement Manipulations
Bi, X., Shang, Y., Liu, B., Xiao, B., Li, W., & Gao, X. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3204789 · Cited by 11
SOMPT22: A Surveillance Oriented Multi-pedestrian Tracking Dataset
Şimşek, F. E., Çığla, C., & Kayabol, K. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25072-9_44 · Cited by 11
REACT: Streaming Video Analytics On The Edge With Asynchronous Cloud Support
Ghosh, A., Iyengar, S., Lee, S., Rathore, A., & Padmanabhan, V. N. (2023) · IEEE/ACM International Conference on Internet-of-Things Design and Implementation (IoTDI) · doi:10.1145/3576842.3582385 · Cited by 11
Song, T., Zhang, X., Yang, D., Ye, Y., Liu, C., Zhou, J. N., & Song, Y. (2023) · Image and Vision Computing · doi:10.1016/j.imavis.2023.104855 · Cited by 11
Eisemann, L., Fehling-Kaschek, M., Gommel, H., Hermann, D. J., Klemp, M., Lauer, M., Lickert, B., Lüttner, F., Moss, R., Neis, N., et al. (2023) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc57777.2023.10422676 · Cited by 11
Object tracking method based on edge detection and morphology
Xu, J., Niu, S., & Wang, Z. (2024) · EURASIP Journal on Advances in Signal Processing · doi:10.1186/s13634-024-01144-0 · Cited by 11
Single target tracking in high-resolution satellite videos: a comprehensive review
Huang, X., Wang, D., Zhu, Q., Zheng, Y. F., & Guan, Q. (2024) · Geo-spatial Information Science · doi:10.1080/10095020.2024.2305912 · Cited by 11
Pattern Corruption-Assisted Physical Attacks Against Object Detection in UAV Remote Sensing
Zhang, Y., Gong, Z., Wen, H., Hu, X., Xia, X., Jiang, H., & Zhong, P. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3422377 · Cited by 11
Research on Pedestrian Detection Model and Compression Technology for UAV Images
Liu, X., Wang, C., & Liu, L. (2022) · Sensors · doi:10.3390/s22239171 · Cited by 11
YOLOv5-based Dense Small Target Detection Algorithm for Aerial Images Using DIOU-NMS
Wang, Y., Zou, X., Shi, J., & Liu, M. (2023) · Radioengineering · doi:10.13164/re.2024.0012 · Cited by 10
Visual Contextual Semantic Reasoning for Cross-Modal Drone Image-Text Retrieval
Huang, J., Chen, Y., Xiong, S., & Lu, X. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3443197 · Cited by 10
RFSC-net: Re-parameterization forward semantic compensation network in low-light environments
Zhang, W., Xu, H., Zhu, X., Si, Y., Dong, Y., Huang, X., & Li, H. (2024) · Image and Vision Computing · doi:10.1016/j.imavis.2024.105271 · Cited by 10
Weighted Learnable Recursive Aggregation Network for Visible Remote Sensing Image Detection
Duan, X., Li, Z., Zhang, J., Li, S., Lei, J., & Zhan, L. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3551509 · Cited by 10
RTL-Net: real-time lightweight Urban traffic object detection algorithm
Cui, Z., Yuan, J., Xu, H., Wei, Y., & Ding, Z. (2025) · Complex & Intelligent Systems · doi:10.1007/s40747-025-01875-z · Cited by 10
LMSOE-Net: lightweight multi-scale small object enhancement network for UAV aerial images
Ma, Z., Luo, P., & Shen, X. (2025) · Complex & Intelligent Systems · doi:10.1007/s40747-025-01971-0 · Cited by 12
A Road Damage Detection Model Based on Improved RT-DETR for Complex Environments
Luo, X., Liu, R., He, X., & Wang, H. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3582316 · Cited by 10
Progress and Perspectives on UAV Visual Object Tracking
Wang, Q., Zhou, L., Xu, C., Shang, Y., Jin, P., Cao, C., & Shen, T. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3593286 · Cited by 10
LersGAN: A GAN-Based Model for Low-Light Remote Sensing Image Enhancement
Li, T., Guo, T., & Xiang, D. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3608696 · Cited by 10
Ulmamei, A., D’Adamo, T., Vasile, C.-E., & Hobincu, R. (2025) · Journal of Imaging · doi:10.3390/jimaging11120436 · Cited by 11
Lightweight Small Target Detection Algorithm with Multi-Feature Fusion
Yang, R., Zhang, J., Shang, X., Li, W., & Zhu, D. (2023) · Electronics · doi:10.3390/electronics12122739 · Cited by 10
CNXResNet: A Light-weight Backbone based on PP-YOLOE for Drone-captured Scenarios
Cai, Z., Hong, Z., Yu, W., & Zhang, W. (2023) · doi:10.1109/icsip57908.2023.10271013 · Cited by 10
Chung, M., Wang, T.-H., & Lin, C.-W. (2023) · Urban Science · doi:10.3390/urbansci7040108 · Cited by 10
Li, L., Liu, X., Chen, X., Yin, F., Chen, B., Wang, Y., & Meng, F. (2024) · Geocarto International · doi:10.1080/10106049.2024.2339294 · Cited by 10
Enhancing Detection of Remotely-Sensed Floating Objects via Data Augmentation for Maritime SAR
Mahmoud, H., Kurniawan, I. F., Aneiba, A., & Asyhari, A. T. (2024) · Journal of the Indian Society of Remote Sensing · doi:10.1007/s12524-024-01869-3 · Cited by 11
ADD-YOLO: a new model for object detection in aerial images
Yang, Y., Feng, Z., Jin, W., & Miao, P. (2025) · Multimedia Systems · doi:10.1007/s00530-025-01688-7 · Cited by 10
RMVAD-YOLO: A Robust Multi-View Aircraft Detection Model for Imbalanced and Similar Classes
Li, K., Zheng, X., Bi, J., Zhang, G., Cui, Y., & Leí, T. (2025) · Remote Sensing · doi:10.3390/rs17061001 · Cited by 11
Deep Learning-Based Multi-class Multiple Object Tracking in UAV Video
Micheal, A. A., & Vani, K. S. (2022) · Journal of the Indian Society of Remote Sensing · doi:10.1007/s12524-022-01615-7 · Cited by 10
VME: A Satellite Imagery Dataset and Benchmark for Detecting Vehicles in the Middle East and Beyond
Emadi, N. A., Weber, I. G., Yang, Y., & Ofli, F. (2025) · Scientific Data · doi:10.1038/s41597-025-04567-y · Cited by 11
HAM-Transformer: A Hybrid Adaptive Multi-Scaled Transformer Net for Remote Sensing in Complex Scenes
Ren, K., Chen, X., Wang, Z., Liang, X., Chen, Z., & Miao, X. (2023) · Remote Sensing · doi:10.3390/rs15194817 · Cited by 9
Anunnaki: A Modular Framework for Developing Trusted Artificial Intelligence
Langford, M. A., Zilberman, S., & Cheng, B. H. C. (2024) · ACM Transactions on Autonomous and Adaptive Systems · doi:10.1145/3649453 · Cited by 9
Failure-Resilient Distributed Inference With Model Compression Over Heterogeneous Edge Devices
Wang, L., Li, L., Xu, L., Peng, X., & Fei, A. (2024) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2024.3419831 · Cited by 9
Tang, D., Tang, S., & Fan, Z. (2024) · PLoS ONE · doi:10.1371/journal.pone.0315267 · Cited by 9
Pathak, S., Shrestha, S., & AlMahmoud, A. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10802588 · Cited by 9
YOLO–DTAD: Dynamic Task Alignment Detection Model for Multicategory Power Defects Image
Jiao, R., Liu, J., Li, K., Qiao, R., Liu, Y., & Zhang, W. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3541692 · Cited by 9
Iaboni, C., Kelly, T., & Abichandani, P. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02418-2 · Cited by 10
RCSD-UAV: An object detection dataset for unmanned aerial vehicles in realistic complex scenarios
Geng, W., Yi, J., Li, N., Ji, C., Yu, C., & Cheng, L. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110748 · Cited by 10
SuperMOT: Decoupling Motion and Fusing Temporal Pyramid Features for UAV Multiobject Tracking
Ren, L., Yin, W., Diao, W., Fu, K., & Sun, X. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3563060 · Cited by 9
CARLA Drone: Monocular 3D Object Detection from a Different Perspective
Meier, J., Scalerandi, L., Dhaouadi, O., Kaiser, J., Araslanov, N., & Cremers, D. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-85187-2_9 · Cited by 9
Adversarial Machine Learning for Robust and Secure UAV Detection in Consumer Applications
Din, I. U., Almogren, A., & Rodrigues, J. J. P. C. (2025) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2025.3572255 · Cited by 9
Context-Aware Enhanced Feature Refinement for small object detection with Deformable DETR
Shi, D., Zhao, C., Shao, J., Feng, M., Luo, L., Ouyang, B., & Huang, J. M. (2025) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2025.1588565 · Cited by 9
FRD-YOLO: a faster real-time object detector for aerial imagery
Rouighi, I. B., Chtioui, H., Jegham, I., Alouani, I., & Khalifa, A. B. (2025) · Journal of Real-Time Image Processing · doi:10.1007/s11554-025-01750-7 · Cited by 9
Semi-LLIE: Semi-supervised contrastive learning with Mamba-based low-light enhancement
Li, G., Zhang, K., Wang, T., Li, M., Zhao, B. C., & Li, X. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.108010 · Cited by 10
Oriented Tiny Object Detection: A Dataset, Benchmark, and Dynamic Unbiased Learning
Xu, C., Zhang, R., Yang, W. T., Zhu, H., Xu, F., Ding, J., & Xia, G.-S. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3634161 · Cited by 11
Adaptive image zoom-in with bounding box transformation for UAV object detection
Wang, T., Lin, C.-Y., Tang, C., Zhou, J., Xiong, D., Li, J., Zhao, J., & Lv, J. (2026) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2026.01.036 · Cited by 11
The Weaknesses of Adversarial Camouflage in Overhead Imagery
Etten, A. V. (2022) · IEEE Applied Imagery Pattern Recognition Workshop · doi:10.1109/aipr57179.2022.10092201 · Cited by 9
Retrieving Object Motions From Coded Shutter Snapshot in Dark Environment
Dong, K., Guo, Y., Yang, R., Cheng, Y., Suo, J., & Dai, Q. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3280010 · Cited by 9
Zhou, Y. Z., Xia, L., Zhao, J., Yao, R., & Liu, B. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15608-2 · Cited by 11
Ameen, M., & Stone, R. (2023) · International Journal of Network Security & Its Applications · doi:10.5121/ijnsa.2023.15307 · Cited by 9
The MONET dataset: Multimodal drone thermal dataset recorded in rural scenarios
Riz, L., Caraffa, A., Bortolon, M., Mekhalfi, M. L., Boscaini, D., Moura, A., Antunes, J., Dias, A. M. P., Silva, H. M., Leonidou, A., et al. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00253 · Cited by 9
Duan, S., Cheng, P., Wang, Z., Wang, Z., Chen, K., Sun, X., & Fu, K. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3353192 · Cited by 9
Area in circle: A novel evaluation metric for object detection
Zhang, X., Razavi‐Far, R., Isah, H., David, A., Higgins, G., Lu, R., & Ghorbani, A. A. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111684 · Cited by 9
TAFormer: A Unified Target-Aware Transformer for Video and Motion Joint Prediction in Aerial Scenes
Xu, L., Lu, W., Yu, H., Mao, Y., Bi, H., Liu, C., Sun, X., & Fu, K. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3398374 · Cited by 9
Multiview Aerial Visual Recognition (MAVREC): Can Multi-View Improve Aerial Visual Perception?
Dutta, A., Das, S., Nielsen, J., Chakraborty, R., & Shah, M. A. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.02140 · Cited by 13
TLSH-MOT: Drone-View Video Multiple Object Tracking via Transformer-Based Locally Sensitive Hash
Yuan, Y., Wu, Y., Zhao, L., Liu, Y., & Pang, Y. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3545081 · Cited by 9
FO-YOLO for small object detection in drone aerial imagery
Zhou, H., Yin, W., Sun, K., Wu, T., & Deng, B. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07688-8 · Cited by 9
Illumination-aware window transformer for RGBT modality fusion
Zhou, L., & Chen, Z. (2022) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2022.103725 · Cited by 9
Aerial Object Detection and Tracking using YOLOv4 and DeepSORT
Jadhav, R., Patil, R., Diwan, A., Rathod, S. M., & Inamdar, M. (2022) · International Conference on Industry 4.0 Technology (I4Tech) · doi:10.1109/i4tech55392.2022.9952705 · Cited by 9
Progressive Transformation Learning for Leveraging Virtual Images in Training
Shen, Y., Lee, H., Kwon, H., & Bhattacharyya, S. S. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00087 · Cited by 9
Small Object Detection using Prediction head and Attention
Kim, H. M., Kim, J. H., Park, K. R., & Moon, Y. S. (2022) · International Conference on Electronics, Information, and Communication (ICEIC) · doi:10.1109/iceic54506.2022.9748393 · Cited by 8
DB-Tracker: Multi-Object Tracking for Drone Aerial Video Based on Box-MeMBer and MB-OSNet
Yuan, Y., Wu, Y., Zhao, L., Chen, J., & Zhao, Q. (2023) · Drones · doi:10.3390/drones7100607 · Cited by 8
Explainable Attention-Based AAV Target Detection for Search and Rescue Scenarios
Liu, S., Yi, L., Xiong, X., Tolba, A. K. R., Ding, J., & Li, C. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3519158 · Cited by 8
Xi, H., Ru, L., Tian, J., Lu, B., Hu, S., Wang, W., & Luan, X. (2025) · Mathematics · doi:10.3390/math13040591 · Cited by 8
Chen, Y., Wang, J., Zhou, Q., & Hu, H. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3543018 · Cited by 8
SCASNet: Spatial Context-Aware Selection Network for Small Object Detection in Aerial Imagery
Wang, Z., Dong, X., & Xu, Y. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3555627 · Cited by 8
Drones in Defense: Real-Time Vision-Based Military Target Surveillance and Tracking
Chakrabarty, S., Chatterjee, R., Chakraborty, S., Shuvo, S. R., & Chowdhury, R. (2025) · International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC) · doi:10.1109/isacc65211.2025.10969335 · Cited by 8
Zhan, Z., Lai, D., Huang, C., Zhang, Z., Deng, Y., & Yang, J. L. (2025) · Sensors · doi:10.3390/s25092730 · Cited by 8
SOE-YOLO: A Small-Object Enhancement Detection Network
Zhang, C., Gu, C., Duan, Q., & Zhao, S. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3568844 · Cited by 9
DDH-YOLO: A dual-head YOLOv8 model for small object detection in UAV aerial images
Liu, Y., & Yang, Z. (2025) · Signal Image and Video Processing · doi:10.1007/s11760-025-04429-5 · Cited by 8
SD-YOLO: A Robust and Efficient Object Detector for Aerial Image Detection
Qi, S., Sun, Y., Song, X., Li, J., Shang, T., & Yu, L. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3591493 · Cited by 8
ASCDet: cross-space UAV object detection method guided by adaptive sparse convolution
Cheng, G., Feng, X., Tian, Y. T., Xie, M., Dang, C., Ding, Q. W., & Shao, Z. (2025) · International Journal of Digital Earth · doi:10.1080/17538947.2025.2528648 · Cited by 8
LCW-YOLO: An Explainable Computer Vision Model for Small Object Detection in Drone Images
Liao, D., Bi, R., Zheng, Y., Hua, C., Huang, L., Tian, X., & Liao, B. (2025) · Applied Sciences · doi:10.3390/app15179730 · Cited by 8
YOLO-UAVShip: An Effective Method and Dateset for Multi-View Ship Detection in UAV Images
Li, Y., Tian, Y., Yuan, C., Yu, K., Yin, K., Huang, H., Yang, G., Li, F., & Zhou, Z.-G. (2025) · Remote Sensing · doi:10.3390/rs17173119 · Cited by 8
Enhancing UAV object detection with an efficient multi-scale feature fusion framework
Lai, D., Kang, K., Xu, K., Ma, X., Zhang, Y., Huang, F., & Chen, J. (2025) · PLoS ONE · doi:10.1371/journal.pone.0332408 · Cited by 8
Nguyen, H., & Pham, T. V. (2025) · Cluster Computing · doi:10.1007/s10586-025-05848-2 · Cited by 9
Xie, H., Wang, M., Cao, R., Wang, J., Jiang, Y., Qiang, H., & Jiang, L. (2025) · Remote Sensing · doi:10.3390/rs17233823 · Cited by 8
AMSA-YOLO: Real-time object detection with adaptive multi-scale attention mechanism
Wang, C., Sun, P., Yang, C., Teng, X., & Wang, R. (2026) · Neural Networks · doi:10.1016/j.neunet.2026.108545 · Cited by 11
Metadata
- Authors
- Zhu, P., Wen, L., Du, D., Bian, X., Fan, H., Hu, Q., & Ling, H.
- Affiliations
- Tianjin University (CN)
- JD Finance America Corporation, Mountain View, CA, USA
- State University of New York (US)
- GE Global Research (United States) (US)
- University of North Texas (US)
- Stony Brook University (US)
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2022-11-01
- Volume
- 44
- Issue
- 11
- Pages
- 7380-7399
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W3205100603
- Cited by
- 1,336
- References
- 169
- Citations by year
- 2026: 422 · 2025: 409 · 2024: 257 · 2023: 173 · 2022: 73
- FWCI
- 45.41
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Key Research and Development Program of China (2019YFB2101904)
- National Natural Science Foundation of China (61732011, 61876127, 61925602)
- Applied Basic Research Program of Qinghai (2019-ZJ-7017)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- VisDrone dataset, multi-target tracking, single object tracking, video object detection, object detection, benchmark challenge
- MeSH terms
- Unmanned Aerial Devices, Algorithms, Photography
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
