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.

翻译：开发鲁棒的无人机检测系统常受限于大规模标注训练数据的稀缺性以及真实世界数据采集的高昂成本。然而，利用基于游戏引擎仿真生成的合成数据，为克服这一问题提供了一种前景广阔且经济高效的解决方案。为此，我们提出了SynDroneVision，这是一个专门为监控应用中基于RGB的无人机检测而设计的合成数据集。该数据集包含多样化的背景、光照条件和无人机模型，为深度学习算法提供了全面的训练基础。为评估数据集的有效性，我们对一系列最新的YOLO检测模型进行了对比分析。研究结果表明，SynDroneVision是用于真实世界数据增强的宝贵资源，能够显著提升模型性能与鲁棒性，同时大幅减少真实世界数据采集的时间与成本。SynDroneVision将在论文录用后公开发布。