Boundless：生成用于城市街景中目标检测的逼真合成数据
Boundless: Generating Photorealistic Synthetic Data for Object Detection in Urban Streetscapes
AI总结：
提出基于UE5的逼真合成数据生成系统Boundless，通过自动化流程替代真实数据收集与标注，用于训练高精度城市街景目标检测模型，相比CARLA训练模型在真实数据推理上提升了7.8 mAP。
摘要：
我们介绍了 Boundless，这是一个逼真的合成数据生成系统，旨在实现密集城市街景中高精度的目标检测。Boundless 能够用自动化且可配置的流程，取代大规模的真实世界数据收集和人工真值目标标注（标记）。Boundless 基于 Unreal Engine 5 (UE5) City Sample 项目，并进行了改进，使其能够在不同光照和场景变化条件下准确收集 3D 边界框。我们评估了在 Boundless 生成的数据集上训练的目标检测模型的性能，这些模型被用于对从中空相机获取的真实世界数据集进行推理。我们将 Boundless 训练模型的性能与 CARLA 训练模型进行了比较，观察到 7.8 mAP 的提升。我们取得的结果支持了这一前提，即合成数据生成是一种可靠的方法，可用于训练/微调适用于城市场景的可扩展目标检测模型。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
