SynDroneVision: A Synthetic Dataset for Image-Based Drone Detection
This addresses data scarcity for drone detection in surveillance, but it is incremental as it applies an existing synthetic data method to a new domain.
The authors tackled the problem of limited annotated training data for drone detection by creating SynDroneVision, a synthetic dataset, and found it enhances model performance and reduces costs for real-world data acquisition.
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 will be publicly released upon paper acceptance.