用游戏引擎生成的合成数据集，提升无人机检测模型性能。
SynDroneVision: A Synthetic Dataset for Image-Based Drone Detection
- 基于游戏引擎构建多样场景的合成图像数据集。
- 在多个YOLO模型上验证，显著提升检测精度与鲁棒性。
- 适合需要低成本数据训练的安防与无人机监测场景。
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及真实数据采集的高昂成本。利用基于游戏引擎的仿真生成合成数据，为解决该问题提供了高效且经济的方案。为此，我们提出SynDroneVision，一个专为监控应用中基于RGB的无人机检测设计的合成数据集。该数据集包含多样的背景、光照条件和无人机型号，为深度学习算法提供了全面的训练基础。为评估其有效性，我们在若干近期YOLO检测模型上进行了对比分析。结果表明，SynDroneVision能有效增强真实数据，显著提升模型性能与鲁棒性，同时大幅降低真实数据采集的时间与成本。论文被接收后，SynDroneVision将公开发布。
原文摘要 · Abstract (English)
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.
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