3+1D雷达点云的深度细分,用于实时路边交通用户检测
Savankumar Bhanderi1, Shiva Agrawal2, Gordon Elger3,2
1Institute of Innovative Mobility (IIMo), Research Group Sensor Technology and Data Fusion for Environmental Perception, Technische Hochschule Ingolstadt, Ingolstadt, 85049, Germany. savankumar.bhanderi@thi.de.
Scientific reports
|November 4, 2025
概括
本研究介绍了用于3D雷达点云集群的深度学习方法,以提高智能城市道路安全. 新方法增强了对象检测和细分,用于实时的交通感知.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 智能基础设施 智能基础设施
背景情况:
- 智慧城市需要强大的道路安全和交通管理感知系统.
- 汽车雷达在恶劣条件下提供可靠的性能,但在物体检测分辨率方面面临挑战.
- 传统的集群方法与易受伤害的道路使用者和隔离附近物体作斗争.
研究的目的:
- 为智能基础设施开发基于深度学习的3D雷达点云集群方法.
- 使用雷达数据提高交通参与者检测的准确性和效率.
- 为了实现智能交通系统的实时感知.
主要方法:
- 一种深度学习方法,结合了3D雷达点云的语义和实例细分.
- 使用深度神经网络进行点云处理和对象集群.
- 为基于智能基础设施的传感器设置量身定制的系统的开发.
主要成果:
- 在语义细分方面获得了95.35%的F1-宏分数.
- 在0.5 IoU时获得了91.03%的平均平均精度 (mAP),例如细分.
- 实时管道在边缘设备上以 43.61 FPS 运行,内存占有率低 (< 0.7 MB).
结论:
- 拟议的深度学习方法显著改善了智能城市应用的3D雷达点云集群.
- 该系统在语义和实例细分方面表现出高精度,优于传统方法.
- 实时性能和低资源需求使其适合在智能基础设施中进行边缘部署.
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