用合成数据训练的检测模型在真实城市街景上表现更好
Boundless: Generating Photorealistic Synthetic Data for Object Detection in Urban Streetscapes
- 基于UE5构建可配置的逼真街景合成系统
- 合成数据训练模型在真实数据上达7.8点mAP提升
- 适合需要大规模标注数据的城市自动驾驶研究
我们提出Boundless，一个用于密集城市街景中物体检测的逼真合成数据生成系统。该系统可替代大规模真实数据采集和人工标注，实现自动化、可配置的数据生成。Boundless基于Unreal Engine 5（UE5）城市样例项目，并通过改进实现了不同光照与场景变化条件下3D边界框的精准获取。我们在中高空相机采集的真实世界数据集上评估了由Boundless生成数据训练的检测模型性能。结果表明，相较于在CARLA上训练的模型，其在真实数据上的检测性能提升了7.8 mAP。这些结果支持了合成数据生成是训练可扩展城市场景物体检测模型的一种可信方法这一前提。
原文摘要 · Abstract (English)
We introduce Boundless, a photo-realistic synthetic data generation system for enabling highly accurate object detection in dense urban streetscapes. Boundless can replace massive real-world data collection and manual ground-truth object annotation (labeling) with an automated and configurable process. Boundless is based on the Unreal Engine 5 (UE5) City Sample project with improvements enabling accurate collection of 3D bounding boxes across different lighting and scene variability conditions. We evaluate the performance of object detection models trained on the dataset generated by Boundless when used for inference on a real-world dataset acquired from medium-altitude cameras. We compare the performance of the Boundless-trained model against the CARLA-trained model and observe an improvement of 7.8 mAP. The results we achieved support the premise that synthetic data generation is a credible methodology for training/fine-tuning scalable object detection models for urban scenes.
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