A comprehensive collection of high-quality datasets for training computer vision models for drone applications, including object detection, tracking, and surveillance.
Read Full ReportA large-scale benchmark with 288 video clips and 10,209 static images for drone-based detection and tracking across 14 different cities in China.
Multiple datasets available for drone detection with easy integration through Python API and various export formats for different frameworks.
Over 4,000 amateur drone pictures with negative samples for better discrimination, specifically designed for training YOLO models.
Real-world object detection dataset for quadcopter UAV with 51,446 training and 5,375 test images in PASCAL VOC format.
Datasets with flying drones captured with multiple synchronized cameras and accurate 3D trajectory ground truth for advanced tracking.
Large-scale UAV detection and tracking benchmark with ~80,000 frames from urban environments focusing on vehicle detection and tracking.
Benchmark for low altitude UAV target tracking with 113,476 images and 10 different object classes for single-object tracking applications.
Datasets specifically curated for defense industry applications including counter-drone systems, surveillance, and target tracking.
View the full research report on drone datasets with detailed analysis, comparisons, and implementation strategies for various applications.
Selecting the right dataset is crucial for developing effective drone-based computer vision systems. Our research provides detailed implementation strategies for various applications:
Combine VisDrone (primary) with UAVDT (supplementary) for comprehensive object detection capabilities.
Use DroneDetectionDataset with Kaggle Drone Dataset for robust drone detection systems.
UAV123 provides excellent single-object tracking data, while VisDrone excels at multi-object tracking.