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
Research works printed but not linked: 1
- 10.1007/978-3-030-58548-8_28 · DOI · Paper page
Cited by
Sorted by citations
Items 501–600 of 1,000
A Lightweight Aerial Image Object Detector Based on Mask Information Enhancement
Zhou, L., Zhao, S., Liu, Z., Zhang, W., Qiao, B., & Liu, Y. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3555732 · Cited by 5
Joint Optimization Loss Function for Tiny Object Detection in Remote Sensing Images
Shi, S., Fang, Q., & Xu, X. (2025) · Remote Sensing · doi:10.3390/rs17081476 · Cited by 5
ViEdge: Video Analytics on Distributed Edge
Hou, X., Guan, Y., & Han, T. (2025) · ACM Transactions on Internet of Things · doi:10.1145/3742794 · Cited by 5
Gong, L., Liu, L., Liu, G., Bai, H., Wang, D., & Wang, H. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108358 · Cited by 5
AUSPEX: An integrated open-source decision-making framework for UAVs in rescue missions
Döschl, B., Sommer, K., & Kiam, J. J. (2025) · Frontiers in Robotics and AI · doi:10.3389/frobt.2025.1583479 · Cited by 7
CCANet: A Cross-scale Context Aggregation Network for UAV object detection
Shang, L., He, Q., Lei, H., & Yang, W. (2025) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2025.104472 · Cited by 5
Improved YOLOv3-tiny Object Detector with Dilated CNN for Drone-Captured Images
Kumar, N., Jilani, A. K., Kumar, P., & Nikiforova, A. (2022) · International Conference on Intelligent Data Science Technologies and Applications (IDSTA) · doi:10.1109/idsta55301.2022.9923041 · Cited by 5
Analysis and Comparison of Image-Based UAV Detection and Identification
Dubey, N., Nithin, N. M. S., & Tripathi, S. (2022) · IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) · doi:10.1109/upcon56432.2022.9986447 · Cited by 5
Research on UAV Ground Target Detection Based on Improved YOLOv7
Zhang, X., & Huang, D. (2023) · International Conference on Computer, Control and Robotics (ICCCR) · doi:10.1109/icccr56747.2023.10193961 · Cited by 5
Small Objects Recognition by Exploiting an Improved YOLOv5 Algorithm on the UAV Platform
Han, D.-J., Zhang, H., Wang, S., Chai, W., Zhou, H., & Zhou, F. (2023) · International Conference on Ubiquitous Communication (Ucom) · doi:10.1109/ucom59132.2023.10257606 · Cited by 5
Progressive Domain-style Translation for Nighttime Tracking
Zhang, J., Li, Z., Wei, R., & Wang, Y. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3612305 · Cited by 5
Aerial Data Exploration: An in-Depth Study From Horizontal to Oriented Viewpoint
Vo, N. D., Ngo, H.-G., Le, T. T., Nguyen, Q. H., Doan, D.-D., Nguyen, B. P., Le-Huu, D., Le-Duy, N., Tran, H., Nguyen, K.-D., et al. (2024) · IEEE Access · doi:10.1109/access.2024.3371514 · Cited by 5
Improved YOLOv8 Network for Small Objects Detection
Krasnov, D. I., Yarishev, S. N., Ryzhova, V. A., & Djamiykov, T. S. (2024) · International Scientific Conference Electronics (ET) · doi:10.1109/et63133.2024.10721517 · Cited by 5
YOLO-S3DT: A Small Target Detection Model for UAV Images Based on YOLOv8
Gao, P., & Li, Z. (2025) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2025.060873 · Cited by 5
Bounding Boxes and Probabilistic Graphical Models: Video Anomaly Detection Simplified
Siemon, M. S. N., Moeslund, T. B., Norton, B., & Nasrollahi, K. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-85187-2_16 · Cited by 5
Do, N.-T., Nguyen, N. N.-Y., Nguyen, D.-P., & Do, T.-H. (2024) · International Conference on Knowledge and Systems Engineering/Proceedings · doi:10.1109/kse63888.2024.11063545 · Cited by 5
Building an Ensemble of Convolutional Neural Networks for Classifying Panoramic Images
Архипов, П. О., & Philippskih, S. L. (2022) · Pattern Recognition and Image Analysis · doi:10.1134/s1054661822030051 · Cited by 5
Vehicle State Classification from Drone Image
Lee, Y., Kim, S., Choi, J., & Jo, K.-H. (2023) · IEEE International Conference on Industrial Technology (ICIT) · doi:10.1109/icit58465.2023.10143168 · Cited by 5
Transformer-based End-to-End Object Detection in Aerial Images
Vo, N. D., Le, N., Ngo, G. H., Doan, D., Le, D., & Nguyen, K. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.01410113 · Cited by 5
YOLOv5 with Combination of Coordinate Attention and CBAM for Object Detection on Drone
An, J., Putro, M. D., Priadana, A., Lee, Y., Kim, J., & Jo, K.-H. (2023) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon51785.2023.10312634 · Cited by 5
Bag of Tricks for Building an Accurate and Slim Object Detector for Embedded Applications
Du, Y., Chen, Z., Jia, C., Li, X., & Jiang, Y. (2021) · International Conference on Multimedia Retrieval · doi:10.1145/3460426.3463659 · Cited by 5
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.v1 · Cited by 5
HPS-YOLOv7: A High Precision Small Object Detection AlgorithmPreprint
Sun, T., Chen, H., Liu, H., Lou, H., & Duan, X. (2023) · Research Square · doi:10.21203/rs.3.rs-2813484/v1 · Cited by 6
Improved RT-DETR Algorithm for Small Object Detection in Drone Aerial Imagery
Li, M., Chen, J., Deng, Z., Huang, L., Wan, K., Liu, W., Qiu, W., & Chen, K. (2025) · International Symposium on Autonomous Systems (ISAS) · doi:10.1109/icaisisas64483.2025.11051433 · Cited by 4
PointSR: Self-Regularized Point Supervision for Drone-View Object Detection
Li, W., Xi, Y., Jia, W., Zhang, Z., Li, F., Liu, X., & Miao, Q. (2025) · 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/cvpr52734.2025.01093 · Cited by 6
ESCVehicle: A Drone-Based Visible–Infrared Vehicle Benchmark With Extensive Scene Coverage
Song, J., Zhang, N., Wang, Z., & Tian, T. (2026) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2026.3655959 · Cited by 4
Yu, C., & Shin, Y. (2026) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2026.3657379 · Cited by 4
A Dynamic Visual SLAM System Incorporating Object Tracking for UAVs
Li, M., Li, J., Cao, Y., & Chen, G. (2024) · Drones · doi:10.3390/drones8060222 · Cited by 4
A Heatmap-Supplemented R-CNN Trained Using an Inflated IoU for Small Object Detection
Butler, J. B., & Leung, H. (2024) · Remote Sensing · doi:10.3390/rs16214065 · Cited by 5
Zhang, X., Yang, D., Song, T., Ye, Y., Song, Y., Zhou, J. N., & Chen, J. (2024) · The Visual Computer · doi:10.1007/s00371-024-03749-w · Cited by 4
An Enhanced End-to-End Object Detector for Drone Aerial Imagery
Yu, Q., Tong, Q., Miao, L., Qi, L., & Liu, X. (2025) · IEEE Access · doi:10.1109/access.2025.3533037 · Cited by 4
Hartmann, N., Rüter, J., & Jünger, F. (2025) · AIAA SciTech Forum · doi:10.2514/6.2025-2512 · Cited by 4
DGC-Net: Dynamic Graph Contrastive Network for Video Object Detection
Qi, Q., Wang, H., Yan, Y., & Li, X. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3551158 · Cited by 5
Aerial Mirage: Unmasking Hallucinations in Large Vision Language Models
Basak, D., Bhatt, S., Kanduri, S., & Desarkar, M. S. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00537 · Cited by 4
Edge-Guided DETR Model for Intelligent Sensing of Tomato Ripeness Under Complex Environments
Yao, J., Zhou, J., Nie, Y., Xue, J., Lin, K., & Tan, L. (2025) · Mathematics · doi:10.3390/math13132095 · Cited by 4
Focal Spotter: Small-Object Detection via Inner Scale and Interscale Feature Focalization
Duan, R., Du, Q., Tian, L., Liu, H., & Huo, S. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3584812 · Cited by 4
Small target detection algorithm based on CSF-YOLOv8
Guo, X., Li, D., Lan, C., & Han, C. (2025) · Signal Image and Video Processing · doi:10.1007/s11760-025-04505-w · Cited by 4
Alrayes, F. S., Ahmad, N., Alshuhail, A., Alshammeri, M., Alqazzaz, A., Alkhiri, H., Alqurni, J. S., & Said, Y. (2025) · Scientific Reports · doi:10.1038/s41598-025-09652-1 · Cited by 4
DE-YOLO: Detail-Enhanced Maritime Object Detection Algorithm Based on YOLOv8
Mao, Z., Li, B., Zhang, R., & Fei, Q. (2025) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc65474.2025.11090244 · Cited by 4
IECA-YOLOv7: A Lightweight Model with Enhanced Attention and Loss for Aerial Wildlife Detection
Ke, W., Liu, T., & Cui, X. (2025) · Animals · doi:10.3390/ani15182743 · Cited by 4
Khan, A. S., Abbass, M. J., & Khan, A. (2025) · Sensors · doi:10.3390/s25195992 · Cited by 5
Optimized Implementation of YOLOv3-Tiny for Real-Time Image and Video Recognition on FPGA
Calì, R., Falaschetti, L., & Biagetti, G. (2025) · Electronics · doi:10.3390/electronics14203993 · Cited by 5
Memory-Efficient Annotation Techniques for Autonomous Drone Navigation
Hegde, P. V., Ahmed, M. R., & Nalband, A. H. (2025) · IEEE Access · doi:10.1109/access.2025.3590402 · Cited by 4
Sun, J., Liu, Z., Xie, M., Wang, Y., Yuan, R., Wu, J., Duan, S., & Wang, L. (2025) · Optics & Laser Technology · doi:10.1016/j.optlastec.2025.113519 · Cited by 4
Wang, Q., Sun, Y., Shen, T., Al-Antary, M. T., Alasmary, H., & Waqas, M. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3647051 · Cited by 4
Zhou, Y., Chen, J., Zhang, Z., Huang, P., Ding, R., Zou, Z., Gao, P., Wei, Y., Li, K., Yang, X., et al. (2026) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2026.01.005 · Cited by 9
DFGOVD: A New Dataset and Benchmark for Drone Fine-Grained Oriented Vehicle Detection in the Wild
Zheng, J., Feng, Z., Xing, M., Su, Y. R., Peng, W., & Zhang, Y. (2026) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2026.3660958 · Cited by 5
Shi, H., Wu, Y., Xu, Y., Mu, X., Hou, M., Shi, B., & Zhang, L. (2026) · Scientific Reports · doi:10.1038/s41598-026-38378-x · Cited by 4
Li, X., Bi, S., & Zhang, Y. A. (2026) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2026.3672623 · Cited by 5
Liu, X., Zou, H., Li, J., Pan, L., He, S., Cao, X., Zhang, Y., Li, J., & Chen, W. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.115788 · Cited by 4
MicroDETR: DETR with frequency-spatial aware and cross-scale fusion for tiny object detection
Lv, Y., 文, 冬., & Song, X. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.113747 · Cited by 5
A Novel Technique for Converting Images from 2D to 3D using Deep Neural Networks
Kulkarni, V. S., Trivedi, R. B., & Goyal, S. R. (2023) · International Conference on Innovative Data Communication Technologies and Application (ICIDCA) · doi:10.1109/icidca56705.2023.10100306 · Cited by 4
YOLOv5s-DSD: An Improved Aerial Image Detection Algorithm Based on YOLOv5s
Sun, C., Chen, Y., Xiao, C., You, L., & Li, R. (2023) · Sensors · doi:10.3390/s23156905 · Cited by 4
Vega: Drone-based Multi-Altitude Target Detection for Autonomous Surveillance
Bandarupalli, A., Jain, S., Melachuri, A., Pappas, J., & Chaterji, S. (2023) · International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT) · doi:10.1109/dcoss-iot58021.2023.00044 · Cited by 4
YOLOv5 with Dual Attention Network for Object Detection on Drone
An, J., Putro, M. D., Priadana, A., Lee, Y., Kim, J., & Jo, K.-H. (2023) · International Workshop on Intelligent Systems (IWIS) · doi:10.1109/iwis58789.2023.10284592 · Cited by 4
Tan, Q., Yang, X., Qiu, C. J., Jiang, Y., He, J., Liu, J., & Wu, Y. (2024) · Remote Sensing · doi:10.3390/rs16061031 · Cited by 4
Efficient Transferability Assessment for Selection of Pre-trained Detectors
Zhao, W., Li, A., Li, Z., & Dou, Q. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00170 · Cited by 4
Masked Feature Compression for Object Detection
Dai, C., Song, T., Jin, Y., Ren, Y., Yang, B., & Song, G. (2024) · Mathematics · doi:10.3390/math12121848 · Cited by 4
Enhancing Post-Disaster Survivor Detection Using UAV Imagery and Transfer Learning Strategies
Ahmed, N., & Al-Maadeed, S. A. (2024) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc61514.2024.10592327 · Cited by 4
Datactive: Data Fault Localization for Object Detection Systems
Yin, Y., Feng, Y., Weng, S., Yao, Y., Liu, J., & Zhao, Z. (2024) · ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) · doi:10.1145/3650212.3680329 · Cited by 4
Bridging the gap between object detection in close-up and high-resolution wide shots
Li, W., Guo, Y., Zheng, J., Lin, H., Ma, C., Fang, L., & Yang, X. (2024) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2024.104181 · Cited by 5
Liu, X., Li, Q., Hu, Y., Pan, J., Zhao, H., Yuan, D., & Li, J.-B. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.111078 · Cited by 4
Rouighi, I. B., Chtioui, H., Jegham, I., & Khalifa, A. B. (2024) · International Conference on Control, Decision and Information Technologies (CoDIT) · doi:10.1109/codit62066.2024.10708153 · Cited by 4
TrafficNight: An Aerial Multimodal Benchmark for Nighttime Vehicle Surveillance
Zhang, G., Liu, Y., Yang, X., Huang, H., & Huang, C. B. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73650-6_3 · Cited by 4
Alasiry, A., & Qayyum, M. S. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-024-06753-y · Cited by 4
Efficient Feature Extraction Model for Surveillance Systems Using Drone Imagery
Choi, J., Lee, Y., & Jo, K.-H. (2024) · IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) · doi:10.1109/cyberscitech64112.2024.00077 · Cited by 4
Cao, Z., Li, J., Niu, J., & Zhou, M. C. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10801539 · Cited by 4
Spatio-Temporal Feature Aware Vision Transformers for Real-Time Unmanned Aerial Vehicle Tracking
Zhang, H., Ye, H., Guo, X., Zhang, X., Rong, Y., & Li, S. (2025) · Drones · doi:10.3390/drones9010068 · Cited by 4
DHQ-DETR: Distributed and High-Quality Object Query for Enhanced Dense Detection in Remote Sensing
Li, C., Zhang, J., Huo, B., & Xue, Y. (2025) · Remote Sensing · doi:10.3390/rs17030514 · Cited by 4
Two‐Stage Approach to Small‐Object Detection
Yu, M., & Leung, H. (2025) · Systems Engineering · doi:10.1002/sys.21800 · Cited by 5
RNAF-YOLO: a model for highlighting difficult-to-detect targets in remote sensing images
Wang, M., Gao, W., Fang, Y., Liu, X., & Jin, X. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/adb207 · Cited by 4
MASNet: a novel deep learning approach for enhanced detection of small targets in complex scenarios
Zhang, Z., Yang, Y., & Jian, X. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/adbccb · Cited by 4
Low-Light Aerial Imaging With Color and Monochrome Cameras
Yuan, P., Lin, L., Lin, J., Liao, Y., & Zhao, T. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3557565 · Cited by 4
UEVAVD: A Dataset for Developing UAV's Eye View Active Object Detection
Jiang, X., Liu, T., Liu, L., Liu, Z., & Liu, Y. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3566604 · Cited by 4
A global object-oriented dynamic network for low-altitude remote sensing object detection
Tang, D., Tang, S., Wang, Y., Guan, S., & Jin, Y. (2025) · Scientific Reports · doi:10.1038/s41598-025-02194-6 · Cited by 4
RL-Net: a rapid and lightweight network for detecting tiny vehicle targets in remote sensing images
Du, Y., Chen, L., & Hao, X. (2025) · Complex & Intelligent Systems · doi:10.1007/s40747-025-01956-z · Cited by 4
Diversifying Human Pose In Synthetic Data For Aerial-View Human Detection
Shen, Y., Lee, H., Kwon, H., & Bhattacharyya, S. S. (2025) · Proceedings - International Conference on Image Processing · doi:10.1109/icip55913.2025.11084750 · Cited by 4
MVA 2025 Small Multi-Object Tracking for Spotting Birds Challenge: Dataset, Methods, and Results
Kondo, Y., Ukita, N., Kanayama, R., Yoshida, Y., Yamaguchi, T., Yu, X., Liang, G., Liu, X., Wang, G.-Z., Chu, W.-T., et al. (2025) · doi:10.23919/mva65244.2025.11175061 · Cited by 4
An, J., Putro, M. D., Priadana, A., Lee, Y., Kim, J., & Jo, K.-H. (2023) · IEEE International Symposium on Industrial Electronics (ISIE) · doi:10.1109/isie51358.2023.10227913 · Cited by 4
Classification guided thick fog removal network for drone imaging: ClassifyCycle
Liu, Y., Qi, W., Huang, G., Zhu, F., & Xiao, Y. (2023) · Optics Express · doi:10.1364/oe.498444 · Cited by 4
COMPONENTS OF A SYSTEM FOR AUTOMATIC DETECTIONOF A ZONE OF INTEREST IN IMAGES OBTAINED FROM A UAV
Voytenko, V. P., Olofsson, B., Solodchuk, M., & Denisov, Y. (2023) · TECHNICAL SCIENCES AND TECHNOLOGIES · doi:10.25140/2411-5363-2023-2(32)-300-312 · Cited by 4
A 3D World Interpreter System for Safe Autonomous Crane Operation
ter Haar, F. B., Ruis, F., & van Manen, B. (2024) · Robotics · doi:10.3390/robotics13020023 · Cited by 4
OpenCV Implementation of Grid-based Vertical Safe Landing for UAV using YOLOv5
Shadakshri, H. C., Veena, M. B., & Dev, K. R. G. (2022) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2022.0130957 · Cited by 4
Tsekhmystro, R., Rubel, O. S., & Лукин, В. В. (2024) · RADIOELECTRONIC AND COMPUTER SYSTEMS · doi:10.32620/reks.2024.1.08 · Cited by 4
Kos, A., Majek, K., & Belter, D. (2025) · SoftwareX · doi:10.1016/j.softx.2025.102110 · Cited by 4
Архипов, П. О., Philippskih, S. L., & Владимирович, Ц. М. (2024) · Pattern Recognition and Image Analysis · doi:10.1134/s105466182470069x · Cited by 3
InstanceSR: Efficient Reconstructing Small Object With Differential Instance-Level Super-Resolution
Fan, Y., Liu, C., Tian, R., & Qian, X. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3496664 · Cited by 3
Du, J., Ge, C., Xue, C., Xu, C., Feng, Z., & Xie, Z. (2024) · Proceedings of the Institution of Mechanical Engineers Part L Journal of Materials Design and Applications · doi:10.1177/14644207241306977 · Cited by 3
Do, T. T., Nguyen, D. D., Ho, X. H., & Nguyen, V. D. (2025) · International Conference on Ubiquitous Information Management and Communication (IMCOM) · doi:10.1109/imcom64595.2025.10857535 · Cited by 3
Dual-Domain Feature-Guided Task Alignment for Enhanced Small Object Detection
Guo, F., Wu, J., & Zhang, Q. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888564 · Cited by 3
A Reinforcement Learning Agent Controlled Multi-branch Small Object Detection Framework
Hong, J., Long, Y., Luo, Y., Hua, L., Long, J., & Qi, Q. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10887941 · Cited by 3
On the Credibility of Backdoor Attacks Against Object Detectors in the Physical World
Doan, B. G., Nguyen, D. Q., Lindquist, C., Montague, P., Abraham, T., Vel, O. D., Camtepe, S. A., Kanhere, S. S., Abbasnejad, E., & Ranasinghe, D. C. (2024) · Annual Computer Security Applications Conference · doi:10.1109/acsac63791.2024.00079 · Cited by 5
Liu, Y., Ji, Y., Ren, Q.-D.-E.-J., Bao, S., Liu, N., Lu, M., & Wu, N. (2025) · Sensors · doi:10.3390/s25072117 · Cited by 3
A Large-Scale Dataset for Small and Tiny Object Detection from Aerial Imagery
Verma, S., Gupta, H., & Verma, O. P. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.04.672 · Cited by 3
Adaptive Modality Selection Drone-Based RGBT Detector for Tiny Targets
Tang, Y., Tong, X., He, Z., Yang, J., & Cao, Y. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3569003 · Cited by 3
He, Y., Zhu, J., Li, Y., Huang, Q., Wang, Z., & Yang, K. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-6972-1_29 · Cited by 3
Wang, K., He, G., & Li, X. (2025) · Sensors · doi:10.3390/s25144381 · Cited by 3
Chang, S., Deng, X., & Xue, D. (2025) · Internet Technology Letters · doi:10.1002/itl2.70118 · Cited by 3
Zhou, Y., Feng, L., Lan, M., Yang, X., Li, Q., Ke, Y., Jiang, X., & Zhang, W. (2025) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2025.09.007 · Cited by 3
Camouflaged Object Tracking: A Benchmark
Guo, X., Zhong, P., Zhang, H., Huang, D., Shao, H., Zhao, Q., & Li, S. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3758243 · Cited by 3
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
