Cited by 1,336Advanced Neural Network ApplicationsVideo Surveillance and Tracking MethodsVisual Attention and Saliency Detection

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 access

Zhu, Pengfei, Wen, Longyin, Du, Dawei, Bian, Xiao, Fan, Heng, Hu, Qinghua, Ling, Haibin·2022·IEEE Transactions on Pattern Analysis and Machine Intelligence

DOI 10.1109/tpami.2021.3119563

Open this paper in CoChat — free account Open PDF

Abstract

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.

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

Crossref · 10.1109/tpami.2021.3119563

Cites

167 of 167 references verified0 citation errors · 0 not checkable

Statistics

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

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
Untitled work
(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
Mask R-CNN
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
Fast R-CNN
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

Show all 169 references

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

Sorted by citations

Items 601–700 of 1,000

CVTOT: A Cross-Video Tiny Object Tracking Method and Dataset for Earth Observation Videos
Li, Z., Yue, P., & Zhang, C. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3630601 · Cited by 3
Drone DETR: enhancing real-time detection transformer for drone small object imagery
Wang, W., Liu, Y., Cui, Z., Shan, L., Zheng, B., Wei, Z., Zhang, X., & Yamane, S. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-08048-2 · Cited by 3
Entropy-Guided Search Space Optimization for Efficient Neural Network Pruning
Qiu, Y., Niu, L., Sha, F., Cheng, Z., & Yanai‬, ‪. (2025) · Algorithms · doi:10.3390/a18120736 · Cited by 3
BBD-YOLO: An Improved Object Detection Algorithm for UAV Images Based on Dynamic Sparse Attention and Multi-Scale Feature Fusion
Wang, W., Liang, S., Xie, M., Ying, L., & Wang, Z. (2025) · IEEE Access · doi:10.1109/access.2025.3637213 · Cited by 3
Dynamic Adaptive Label Assignment for Tiny Object Detection in Remote Sensing Images
Shi, S., Fang, Q., & Xu, X. (2025) · CAAI Transactions on Intelligence Technology · doi:10.1049/cit2.70083 · Cited by 4
WMFA-AT: Adaptive Teacher with Weighted Multi-Layer Feature Alignment for Cross-Domain UAV Object Detection
Cheng, G., Yang, H., Tian, Y. T., Xie, M., Dang, C., Ding, Q. W., & Feng, X. (2025) · Remote Sensing · doi:10.3390/rs17233854 · Cited by 3
OF-DETR: an efficient end-to-end detector for tiny traffic targets in aerial images
Hu, J., Huang, H., Zhao, F., Tang, Y., He, S., Chen, X., Guo, Q., & Zhang, M. (2025) · Multimedia Systems · doi:10.1007/s00530-025-02060-5 · Cited by 3
An Enhanced YOLOv11 Model for Small Object Detection in UAV Aerial Images
Zhou, X., Liu, Q., & Huang, H. (2025) · SAE technical papers on CD-ROM/SAE technical paper series · doi:10.4271/2025-99-0449 · Cited by 3
TPD–YOLO: real-time traffic participant detection based on an improved feature distillation strategy
An, F., Zhong, M., Zhang, Y., Yuan, B., Tan, J., & Fan, K. (2025) · Journal of Real-Time Image Processing · doi:10.1007/s11554-025-01829-1 · Cited by 3
PV3M-YOLO: A triple attention-enhanced model for detecting pedestrians and vehicles in UAV-enabled smart transport networks
Tahir, N. U. A., Kuang, L., Sinishaw, M. L., & Asim, M. (2026) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2025.104701 · Cited by 4
FlyAwareV2: A multimodal cross-domain UAV dataset for urban scene understanding
Barbato, F., Caligiuri, M., & Zanuttigh, P. (2026) · Signal Processing Image Communication · doi:10.1016/j.image.2026.117483 · Cited by 3
An improved UAV image object detection algorithm combining multi-scale feature fusion and receptive-field attention-based convolution
Dong, F., Gui, B., Wang, W., Fan, W., & Liu, Q. (2026) · Scientific Reports · doi:10.1038/s41598-025-34711-y · Cited by 3
Brain‐Inspired CMOS‐Compatible ZnSnO Synaptic Array with Ultra‐High PPF for Versatile Neuromorphic Computing
Xie, L., Liu, Y., Hao, S., Zhao, Y., Li, D., Li, H., Mavlonov, G. K., Исамов, С. Б., Li, J., & Zhang, J. (2026) · Advanced Functional Materials · doi:10.1002/adfm.202530738 · Cited by 3
Reliable-Teacher: Uncertainty-Guided Collaborative Learning for Nighttime Object Detection
Luo, H., Xi, Y., Jia, W., Xiao, J., Ren, J., Yuan, D., Miao, Q., & He, X. (2026) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2026.3665871 · Cited by 3
CoreKD: A Context-Aware Local Region Structural Contrastive Knowledge Distillation Framework for Object Detection
Yi, J., Mao, J., Wang, Y., Liu, T., Li, M., Zeng, K., Zhang, H., & Chang, X. (2026) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2026.3672967 · Cited by 3
Enhanced YOLO11 for tiny object detection based on multi-scale information interaction and fusion in UAV aerial images
Gao, P., Li, H., & Wang, F. (2026) · Journal of Computational Design and Engineering · doi:10.1093/jcde/qwag028 · Cited by 3
AD-YOLO: A Unified Method for Traffic-Dense and Small Object Detection in UAV Images
Deng, Y., Hu, Y., Ye, Y., Chen, T., & Xu, P. (2026) · Drones · doi:10.3390/drones10050338 · Cited by 4
RSS-YOLO: An efficient UAV object detector with reciprocal spatial-semantic fusion
Dai, P., Chen, L., Xie, L., & Zhou, L. (2026) · Multimedia Systems · doi:10.1007/s00530-026-02518-0 · Cited by 3
A Shallow Information Enhanced Efficient Small Object Detector Based on YOLOv5
Yang, M., & Bai, H. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44195-0_1 · Cited by 3
Multi‐scale feature aggregation network for single‐image dehazing
Zhao, D. Z., Mo, B., & Xiang, Z. (2024) · IET Image Processing · doi:10.1049/ipr2.13146 · Cited by 3
Improved STNNet, A benchmark for detection, tracking, and counting crowds using Drones
Nazeer, M., Sharma, K., Sathappan, S., Srilatha, P., & Mohammed, A. A. K. (2024) · MethodsX · doi:10.1016/j.mex.2024.102820 · Cited by 3
Hstr-net: reference based video super-resolution with dual cameras
Suluhan, H. U., Doruk, A. E., Ates, H. F., & Gunturk, B. K. (2025) · Machine Vision and Applications · doi:10.1007/s00138-025-01685-w · Cited by 3
Para-YOLO: An Efficient High-Parameter Low-Computation Algorithm Based on YOLO11n for Remote Sensing Object Detection
Chen, H., Cao, Q., Wang, Y., Wang, S., Fu, H., Chen, Z., & Liang, F. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3576221 · Cited by 6
LSTT: Long short-term feature enhancement transformer for video small object detection
Xiao, J., Liu, W., Chen, R., Yan, Y., & Yang, W. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.129631 · Cited by 3
Long-Range Autonomy for Collaborative Aerial Search and Target Revisit
Cui, W., Poojari, S. S., Abdi, S. S., Nogar, S., & Paley, D. A. (2025) · Journal of Guidance Control and Dynamics · doi:10.2514/1.g008897 · Cited by 3
A Text-Guided Graph Neural Network for Predicting Long-Range Trajectories of Sea-Surface Objects
Zhao, Y., Li, W., Song, X., Zhang, X., Rao, J., Gao, Y., & Ding, J.-G. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3613964 · Cited by 3
Refining small object detection in aerial images with PF-DETR: a progressive fusion approach
Liu, J., Cao, Y., Dong, C., Zhang, X., Liang, Y., & Liang, Y. (2026) · PeerJ Computer Science · doi:10.7717/peerj-cs.3470 · Cited by 3
Enhanced Image Segmentation by a Novel Test Time Augmentation and Super-Resolution
García‐Aguilar, I., García-González, J., Luque‐Baena, R. M., López‐Rubio, E., & Domínguez, E. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-06527-9_15 · Cited by 3
Methodology for Image Analysis in Airborne Search and Rescue Operations
Ciccone, F., Bacciaglia, A., & Ceruti, A. (2022) · Lecture notes in mechanical engineering · doi:10.1007/978-3-031-15928-2_71 · Cited by 3
Apple Tree Health Recognition Through the Application of Transfer Learning for UAV Imagery
Dolgaia, L., Illarionova, S., Nesteruk, S., Криволапов, И. П., Baldycheva, A. V., Somov, A., & Shadrin, D. (2023) · IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) · doi:10.1109/etfa54631.2023.10275369 · Cited by 4
Neural network-based small cursor detection for embedded assistive technology
Darmawan, J. T., Sigalingging, X. K., Faisal, M. A., Leu, J., & Ratnasari, N. R. P. (2024) · The Visual Computer · doi:10.1007/s00371-023-03246-6 · Cited by 3
MRG-T: Mask-Relation-Guided Transformer for Remote Vision-Based Pedestrian Attribute Recognition in Aerial Imagery
Zhang, S., Li, Y., Wu, X., Chu, Z., & Li, L. (2024) · Remote Sensing · doi:10.3390/rs16071216 · Cited by 3
Reliability Assessment of Target Recognition Task Based on Unmanned Aerial Vehicle Simulation System
Wan, W., Lin, Y., Chen, M., Xu, P., Li, X., & Shang, B. (2024) · International Conference on Advanced Algorithms and Control Engineering (ICAACE) · doi:10.1109/icaace61206.2024.10548822 · Cited by 3
Exploring the Potential of Synthetic Data to Replace Real Data
Lee, H., Zhang, Y., Kwon, H., & Bhattacharyya, S. S. (2024) · Proceedings - International Conference on Image Processing · doi:10.1109/icip51287.2024.10647508 · Cited by 5
The AAV Tracking: Fusion and Adversarial of Global and Local Information
Zhao, Y., Yang, R., Guo, C., & Hao, Y. (2024) · IEEE Signal Processing Letters · doi:10.1109/lsp.2024.3496816 · Cited by 3
V10Drone: Improved YOLOv10 with KAN for Small Object Detection in Aerial Images
Li, Z., Chen, H., Wang, Z., Wang, Y., Li, J., & Zhu, H. (2024) · International Symposium on Computer Science and Intelligent Control (ISCSIC) · doi:10.1109/iscsic64297.2024.00039 · Cited by 3
Trending Machine Learning Methods for Vehicle, Pedestrian, and Traffic for Detection and Tracking Task in the Post-Covid Era: A Literature Review
Porto, J. V. D. A., Szemes, P. T., Pistori, H., & Menyhárt, J. (2025) · IEEE Access · doi:10.1109/access.2025.3565901 · Cited by 3
HiCAL: Hierarchical Consistency-Based Active Learning for Drone-View Object Detection
Zhang, X., Liu, Y., Li, A., Han, K., Zhao, Q., & Wu, J. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3576980 · Cited by 3
Deep learning classification applied to traffic accidents prediction
Coll-Josifov, R., Masip-Alvarez, A., & Lavèrnia-Ferrer, D. (2022) · Servizo de Publicacións da UDC eBooks · doi:10.17979/spudc.9788497498418.0964 · Cited by 3
High-Performance Siamese Network for Real-Time Tracking
Du, G., Zhou, P., Abudurexiti, R., Mahpirat, M., Aysa, A., & Ubul, K. (2022) · Sensors · doi:10.3390/s22228953 · Cited by 3
红外—可见光跨模态的行人检测综述
Bie, Q., Wang, X., Xu, X., Zhao, Q., Wang, Z., Chen, J., & Hu, R. (2023) · Journal of Image and Graphics · doi:10.11834/jig.220670 · Cited by 3
Facilitating UAV Application for CAV Deployment
Zong, S., Li, Y., Chen, S., Mao, S., & Labi, S. (2023) · doi:10.5703/1288284317729 · Cited by 3
Study of the dependence of accuracy in vehicles search on the size of the object using UAV images
Tsekhmystro, R., Rubel, O. S., & Лукин, В. В. (2024) · Aerospace technic and technology · doi:10.32620/aktt.2024.3.08 · Cited by 2
UAV Mission Computer Operation Mode Optimization Focusing on Computational Energy Efficiency and System Responsiveness
Любімов, О., Туркін, І. Б., Cheranovskiy, V., & Volobuieva, L. (2024) · Computation · doi:10.3390/computation12120235 · Cited by 2
Deep interactive query design and progressive search for end-to-end detection of tiny object in aerial images
Jin, C., Zheng, A., Wu, Z., & Tong, C. (2025) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-025-02582-4 · Cited by 2
STSODNet: Scale Transformer Small Object Detection Network
Li, J., Yang, L., & Wang, P. S. P. (2025) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001425550043 · Cited by 2
Adaptive Observer-Based Neural Network Control for Multi-UAV Systems with Predefined-Time Stability
Zhang, Y., Sha, H., Peng, R., Li, N., Miao, Z., He, C., & Zhou, J. (2025) · Drones · doi:10.3390/drones9030222 · Cited by 2
Ringmo-SenseV2: Remote Sensing Foundation Model for Spatiotemporal Prediction Based on Multisource Heterogeneous Time-Series Data
Xu, L., Lu, W., Hu, L., Yang, H., Jiang, Y., Liu, C., Yu, H., Deng, C., Sun, X., & Fu, K. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3565203 · Cited by 2
Fundamentals of Drone Navigation and the Role of Computer Vision
Sindiramutty, S. R., Jhanjhi, N. Z., & Goh, W. W. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-8497-8.ch006 · Cited by 2
Star-PMFI: Star-attention and pyramid multi-scale feature integration network for small object detection in drone imagery
Yang, W., Li, Z., & He, Q. (2025) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2025.104479 · Cited by 2
HiDRA-DCDNet: Dynamic Hierarchical Attention and Multi-Scale Context Fusion for Real-Time Remote Sensing Small-Target Detection
Wang, J., Bai, Z., Zhang, X., Qiu, Y., Bu, F., & Shao, Y. (2025) · Remote Sensing · doi:10.3390/rs17132195 · Cited by 2
USVTrack: A Benchmark for Multi-Object Tracking in Complex Water Surface Scenes
Xue, B., Cheng, Y., Ding, K., Pan, C., & Xiang, S. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3595760 · Cited by 2
Dilated Strip-Wise Spatial Feature Pyramid: An Efficient Network for Object Detection
Sundaralingam, H., Suresh, T., Akilan, T., & Ahmed, S. B. (2025) · IEEE International Symposium on Industrial Electronics (ISIE) · doi:10.1109/isie62713.2025.11124807 · Cited by 2
Occlusion-robust multi-object tracking against viewpoint changing for UAV
Zhai, Y., Lu, D., Li, J., & Wang, Y. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.132440 · Cited by 2
Segment and Recover: Defending Object Detectors Against Adversarial Patch Attacks
Gu, H., & Jafarnejadsani, H. (2025) · Journal of Imaging · doi:10.3390/jimaging11090316 · Cited by 2
ARES: Adaptive robust object detection framework for enhancing real-time performance in autonomous vehicle systems
Park, S., Baek, H., & Lee, J. (2025) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2025.103574 · Cited by 2
MCRO-YOLO: An evolved Version of YOLO for Small Object Detection
Li, J., Shi, Q., Yang, F. O., Fu, S., & Hou, F. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11227312 · Cited by 2
MMKLTrack: Illumination-robust UAV tracking with progressive localization
Yin, K., Feng, J., Dong, S., & Long, Y. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.130207 · Cited by 2
D-Know: Disentangled Domain Knowledge-Aided Learning for Open-Domain Continual Object Detection
He, B., Yan, C., Kou, Y., Wang, Y., Lv, X., Du, H., & Xie, Y. (2025) · Applied Sciences · doi:10.3390/app152312723 · Cited by 2
DSENet++: A Coarse-to-Fine Framework for Enhanced Sub-Region Detection in Aerial Images
Wang, X., Jiang, H., Chen, L., Zhang, J., Zhang, J., Ge, S., & Zeng, D. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3604895 · Cited by 2
RSNet-Lite: A Lightweight Perception Subnetwork for Remote Sensing Object Detection
Li, H., Ma, J., Guo, W., Li, X., Qin, X., & Ma, Z. (2025) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2025.3631871 · Cited by 2
Performance Comparison of Yolo Versions for Small Object Detection on UAV Images
Nivashini, V., & Rajesh, D. G. (2025) · International Research Journal on Advanced Engineering Hub (IRJAEH) · doi:10.47392/irjaeh.2025.0646 · Cited by 2
MSGP-DETR: A multi-species stored-grain insect pest detection transformer integrating frequency-domain enhancement and state space models
Guan, Z., Sun, F., Lyu, Z., Xin, Y., & Zhao, Z. (2025) · Journal of Stored Products Research · doi:10.1016/j.jspr.2025.102941 · Cited by 2
Vision transformer with salience self-attention for underwater and aerial object recognition and tracking
Zhou, S., Liu, M., Zhou, J., & Zheng, R. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.132631 · Cited by 2
Cross-scale feature alignment and feature enhancement for small object detection
Wu, B., Wang, Y., Huang, X., & Xie, G. (2026) · Pattern Analysis and Applications · doi:10.1007/s10044-025-01601-y · Cited by 2
Enhancing UAV small target detection: A balanced accuracy-efficiency algorithm with tiered feature focus
Guo, H., & Liu, S. (2026) · Image and Vision Computing · doi:10.1016/j.imavis.2026.105897 · Cited by 2
MSFFNet: Multiscale Feature Fusion Network for Small Target Detection in Remote Sensing Images
Zong, H., Wang, C., Wang, H., Cheng, X., Gao, S., & Yan, Y. (2026) · CAAI Transactions on Intelligence Technology · doi:10.1049/cit2.70097 · Cited by 2
ADD-Net: Adaptive dynamic architecture with knowledge distillation for UAV small object detection
Wang, H., Li, Y., Zhou, W., & Zhang, H. (2026) · Digital Signal Processing · doi:10.1016/j.dsp.2026.105990 · Cited by 2
Tiny object detection: An in-depth survey of techniques, challenges, and future directions
Nautiyal, R., & Deshmukh, M. (2026) · Digital Signal Processing · doi:10.1016/j.dsp.2026.105995 · Cited by 2
Optimizing long-range UAV detection on YOLOv8: Breaking-point distance analysis and combining adaptive tiling with AdamW optimizer
Ngon, N. V., Nhan, D. T., Long, C. H., & Phạm, T. Đ. (2026) · Journal of Military Science and Technology · doi:10.54939/1859-1043.j.mst.109.2026.154-163 · Cited by 2
HF-DETR: High-Frequency Feature Enhancement for Small Object Detection
Zhu, J., & Zhang, Y. (2026) · IEEE Signal Processing Letters · doi:10.1109/lsp.2026.3670742 · Cited by 2
Night UAV Vehicle Detection Based on Infrared-Visible Fusion and Improved YOLO11
Su, H. (2026) · Journal of Computer Science and Digital Technology · doi:10.71204/ew979106 · Cited by 2
UAV small object detection based on density map prediction and segmentation
Ni, J., Huang, J., Tang, G., Shi, P., & Gu, Y. (2026) · Measurement Science and Technology · doi:10.1088/1361-6501/ae5379 · Cited by 2
Efficient transformer-based UAV image object detection network based on convolutional self-attention and matchability-adaptive classification
Di, J., Wang, Q., & Yang, Y. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-00621-w · Cited by 2
AUV-Net: An adaptive feature fusion network for small object detection in unmanned aerial vehicle scenes
Qu, Z., Liu, H., Kong, W., Gu, J., Wang, C., Deng, L., & Liu, L. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.115285 · Cited by 2
ClaraVid: A Holistic Scene Reconstruction Benchmark from Aerial Perspective with Delentropy-Based Complexity Profiling
Beche, R., & Nedevschi, S. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.02413 · Cited by 2
Real-Time Drone-Car Collaboration for Future Mobility: Vision and Challenge
Tamboli, S., Davuluri, K., Mottakin, K., & Song, Z. (2026) · ACM SIGAPP Applied Computing Review · doi:10.1145/3807408.3807409 · Cited by 2
GSR-Net: enhancing real-time UAV remote sensing object detection via a lightweight transformer model
Ma, P., Fei, H., Zhang, Y., Lian, N., Liu, F., & Zhou, J. (2026) · The Visual Computer · doi:10.1007/s00371-026-04483-1 · Cited by 2
From Generic to Adaptive: Similarity-Adaptive Receptive-Field Cross DETR for Remote-Sensing Object Detection
Lin, C., Fu, Y., Xu, H., Teng, X., & Wang, T. (2026) · Remote Sensing · doi:10.3390/rs18101670 · Cited by 2
TSC-DETR: DETR With Transformer-Semantics to CNN Guidance for Small Object Detection
Yu, Z., Guo, L., Huo, H., Xu, Q., Ren, F., & Cui, Q. (2026) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2026.3699672 · Cited by 2
Csf-yolo: clean features and stable fusion for UAV small-object detection
Deng, L., Wang, J., Chen, J., Zhang, Y., Shi, D., & Liu, L. (2026) · Journal of Real-Time Image Processing · doi:10.1007/s11554-026-01902-3 · Cited by 2
SO-DETR: Small Object Detection Transformer via State Space Duality Encoder and Multigroup Rank Supervision
Fan, Z., Wang, C., Yu, Q., Min, L., Wang, S., & Wang, B. (2026) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2026.3703859 · Cited by 2
Enhancing foggy weather object detection via frequency decoupling, content-adaptive feature fusion and efficient spatial pyramid pooling in YOLOv11
Zhu, J. (2026) · The Journal of Supercomputing · doi:10.1007/s11227-026-08714-z · Cited by 2
MDCF-YOLO: a lightweight multi-scale context-aware and feature fusion network for small object detection in remote sensing images
Dai, P., Chen, L., Fan, T., Zheng, H., & Hu, Y. (2026) · Cluster Computing · doi:10.1007/s10586-026-06483-1 · Cited by 2
CDF-DETR: Cross-Stage Attention and Dual-Scale Feature Calibration for Small-Object Detection in UAV Remote Sensing Imagery
Zou, R., Guo, J., Liang, J., Che, K., Deng, Y., & Chen, B. (2026) · Remote Sensing · doi:10.3390/rs18162793 · Cited by 2
A Hierarchical Context-Fusion and Synergistic Alignment Network for small object detection
Zhang, B., Pan, W., Xu, B., & Dai, S. (2026) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2026.08.034 · Cited by 2
DyCC-Net: Dynamic Context Collection Network for Input-Aware Drone-View Object Detection
Xi, Y., Jia, W., Miao, Q., Liu, X., Fan, X., & Lou, J. (2022) · Remote Sensing · doi:10.3390/rs14246313 · Cited by 2
Research on Motion Trend Enhanced 2D Detection on Drones
Wu, H. (2023) · International Conference on Computer Communication and Artificial Intelligence (CCAI) · doi:10.1109/ccai57533.2023.10201284 · Cited by 2
PanopticUAV: Panoptic Segmentation of UAV Images for Marine Environment Monitoring
Dou, Y., Yao, F., Wang, X., Qu, L., Chen, L., Xu, Z., Ding, L., Bullock, L. B., Zhong, G., & Wang, S. (2023) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2023.027764 · Cited by 2
An airborne object detection and location system based on deep inference
Hu, X. H., Pan, S., Li, D., Feng, L., & Zhao, Y. (2023) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2632/1/012019 · Cited by 2
Improving Safety in the Maintenance of Infrastructures: Design of a UAV-Based System for Work zone Monitoring
Martínez-Sánchez, J., Piñeiro-Monteagudo, H., Balado, J., Soilán, M., & Arias, P. (2023) · Transportation research procedia · doi:10.1016/j.trpro.2023.11.758 · Cited by 2
Reliability Analysis of Petri Nets For Unmanned Aerial Vehicles
Kumari, L., C, N. A., Bhavitha, N., Naik, S. M., Das, M., & Mohan, B. R. (2024) · IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) · doi:10.1109/iatmsi60426.2024.10503471 · Cited by 2
Multi-Scale Adversarial Cross-Domain Vehicle Detection from UAV to Satellite Imagery
Chen, Y., Ye, S., Li, F., Han, T., & Meng, X. (2024) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss53475.2024.10640616 · Cited by 2
Improving Few-Shot and Cross-Domain Object Detection on Aerial Images with a Diffusion-Based Detector
Jeune, P. L., Talaoubrid, H., & Mokraoui, A. (2024) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss53475.2024.10642125 · Cited by 2
Real-Time Object Detection and Depth Estimation in Quadcopters through Intelligent Image Processing with YOLOv8
Mahdavi, A., Haghighi, M. M., & Khankalantary, S. (2024) · doi:10.1109/icee63041.2024.10668382 · Cited by 2
Application of UAV Target Detection and Tracking System Based on Intelligent Image Recognition
Zheng, D., Sun, Y., He, D. J., Wang, J., Ju, H., & Zhao, H. (2024) · World Conference on Communication & Computing (WCONF) · doi:10.1109/wconf61366.2024.10692181 · Cited by 2
Benchmarking the Robustness of UAV Tracking Against Common Corruptions
Liu, X., Feng, Y., Hu, S., Yuan, X., & Fan, H. (2024) · IEEE International Conference on Multimedia Information Processing and Retrieval (MIPR) · doi:10.1109/mipr62202.2024.00079 · Cited by 6
Self-citation
Detecting Indoor Tiny Autonomous Malicious Drones within Critical Infrastructures: An Innovative Algorithm based on Harmonic Radar-Equipped Mini-Drones
Skraparlis, A. N., Ntalianis, K., Ntaliani, M. S., Ntalianis, F., & Mastorakis, N. E. (2024) · WSEAS Transactions on Information Science and Applications archive · doi:10.37394/23209.2024.21.42 · Cited by 2
A Method for Constructing an Aerial Image Dataset of Civil Aircraft “Black Box” Using AIGC-Generated Samples
Luo, Q., Li, W., Zhuge, J., & Shao, J. (2024) · International Conference on Machine Learning and Computer Application (ICMLCA) · doi:10.1109/icmlca63499.2024.10754398 · Cited by 2
Surface Multiple Object Tracking: An Accurate HAT-YOLOv8-ADT Tracking Model
Lin, N., Zhang, L., Wu, T., Hawbani, A., Zhou, H., & Zhao, L. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3539852 · Cited by 2

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)
ORCID: Zhu, Pengfei
Wen, Longyin
Du, Dawei
Bian, Xiao
Fan, Heng
Hu, Qinghua
Ling, Haibin
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2022-11-01
Volume: 44
Issue: 11
Pages: 7380-7399
ISSN: 0162-8828 (ISSN-L)
2160-9292
1939-3539
DOI: 10.1109/tpami.2021.3119563
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: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
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

Detection and Tracking Meet Drones Challenge

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks