SynDroneVision: A Synthetic Dataset for Image-Based Drone Detection

Abstract

Developing robust drone detection systems is often constrained by the limited availability of large-scale annotated training data and the high costs associated with real-world data collection. However leveraging synthetic data generated via game engine-based simulations provides a promising and cost-effective solution to overcome this issue. Therefore we present SynDroneVision a synthetic dataset specifically designed for RGB-based drone detection in surveillance applications. Featuring diverse backgrounds lighting conditions and drone models SynDroneVision offers a comprehensive training foundation for deep learning algorithms. To evaluate the dataset's effectiveness we perform a comparative analysis across a selection of recent YOLO detection models. Our findings demonstrate that SynDroneVision is a valuable resource for real-world data enrichment achieving notable enhancements in model performance and robustness while significantly reducing the time and costs of real-world data acquisition. SynDroneVision can be accessed at https://zenodo.org/records/13360116.