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在拥挤的行人身上实时3D物体检测
Bin Lu1,2, Qing Li1, Yanju Liang1,2
1Chinese Academy of Sciences Institute of Microelectronics, Beijing 100029, China.
Sensors (Basel, Switzerland)
|November 14, 2023
概括
这项研究介绍了RTCP,这是一种实时检测模型,用于使用盲填充LiDAR对拥挤的行人进行检测. 它通过提高挑战自动驾驶场景的准确性和效率来增强感知.
科学领域:
- 自主驾驶系统 自主驾驶系统
- 计算机视觉 计算机视觉
- 激光雷达的感知方式
背景情况:
- 对象检测对于自动驾驶的感知至关重要.
- 填补盲点的激光雷达虽然可以减少盲点,但由于分辨率低和点云稀疏性,它们存在挑战.
- 现有的方法在使用稀疏数据时,难以检测拥挤的行人.
研究的目的:
- 开发一个实时检测模型,用于拥挤的行人目标在自动驾驶.
- 解决低分辨率LiDAR数据的局限性,提高检测精度和效率.
- 在拥挤的场景中增强对阻塞的强度.
主要方法:
- 一种基于注意力的点采样方法,以减少点云冗余.
- 量子化点云空间和邻近融合在极点坐标的特征提取.
- 一个对象对齐注意力 (OAA) 模块与热图引导的训练分支,以提高目标焦点和强度.
主要成果:
- 拟议的RTCP模型证明了计算效率的提高.
- 对于拥挤的行人目标而言,增强了对阻塞的强度.
- 在多个数据集 (KITTI,JRDB,定制) 上实现了检测准确度和运行效率之间的卓越权衡.
结论:
- 对于拥挤的行人检测,RTCP有效地处理来自盲人填充LiDAR的稀疏点云.
- 该模型为改善自动驾驶汽车感知提供了一个实际的解决方案.
- RTCP为现实应用提供了速度和准确性的最先进平衡.
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