Synthetic Dataset for Vision-Based Air-to-Air Object Detection

The increasing deployment of Uncrewed Aerial Vehicles (UAVs) across various industries has heightened the need for robust airborne object detection to ensure safe airspace operations. Vision-based deep learning models offer effective solutions but require large, diverse, and well-annotated datasets for optimal performance. Existing datasets are limited in diversity, expensive to acquire, and manually annotated, restricting their scalability. This work presents a scalable synthetic dataset for vision-based airborne object detection, generated using AirSim integrated with Unreal Engine. Our dataset includes nine UAV models, multiple airborne object classes (birds, helicopters, balloons, aircraft), and diverse environmental conditions. Unlike existing synthetic datasets, our framework eliminates the need for photogrammetry or chroma-keying, making it fully automated and cost-effective. We evaluate our dataset by training detection models and benchmarking them against existing real-world datasets, demonstrating that synthetic data can improve model generalization while significantly reducing acquisition costs. The dataset is available at: www.kaggle.com/datasets/avldevelopment/air-to-air-object-detection-dataset