RV-DroneEye: Unity-Based Framework for a Synthetic Dataset for Robust UAV Recognition

The rapid proliferation of unmanned aerial vehicles (UAVs) has created an urgent need for robust detection & identification systems to ensure airspace security and safety. However, the scarcity of large-scale, diverse, and accurately annotated real-world datasets hinders the development of effective UAV detection & identification algorithms. To address this challenge, we present the RV-DroneEye (Realistic Virtual DroneEye) Dataset, a comprehensive framework for generating synthetic datasets utilizing the Unity 3D simulation engine to enhance UAV detection training. Additionally, we leverage photorealistic augmentation using Flux.1 diffusion-based model as a base, diverse environmental conditions, and physically accurate flight dynamics to generate large-scale annotated datasets that capture the visual complexity of real-world UAV detection scenarios. Then we evaluate our dataset’s generalization on various benchmarks and train multiple object-detection models. The RV-DroneEye dataset includes diverse UAV models, environments (urban, forest, lake), weather, and lighting conditions. We assess its effectiveness by training state-of-the-art object detection models and evaluating them on real-world test sets, including a multiclass task for more than 20 UAV types. Results show that models trained on RV-DroneEye achieve comparable or better UAV pattern generalization than those using limited real-world data, with marked gains in challenging scenarios such as low-light conditions, extreme angles, and cluttered backgrounds.