清洁:知识驱动的压缩框架,用于有效的3D对象检测
概括
这项研究引入了用于基于LiDAR的3D物体检测中使用的深度神经网络压缩的新框架. 该方法有效地转移知识并优化学生模型,从而为自动驾驶带来更准确,更紧的探测器.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 深度神经网络 (DNN) 在基于LiDAR的3D对象检测 (LiDAR-3DOD) 中表现出色,但却面临着巨大的计算成本和参数大小的问题.
- 知识蒸 (KD) 为压缩 LiDAR-3DOD 中的 DNN 提供了一个解决方案,但由于知识传输不足和学生架构不足,现有方法显示的收益有限.
- 不同质的知识传输,特别是从两阶段到一阶段探测器,仍然是当前KD方法的挑战.
研究的目的:
- 为基于LiDAR的3D探测器开发一个高效的压缩框架.
- 克服异质探测器类型 (例如,两阶段到一阶段) 之间的知识转移的局限性.
- 优化学生网络架构,以提高LiDAR-3DOD的效率和准确性.
主要方法:
- 提出了一个基于知识的类别压缩框架,使用基于知识的类别KD (CaKD) 进行异质的教师-学生对.
- 实施了面具类别知识驱动的结构化修剪方案,以识别和删除基于类别预测影响的不太重要的过器.
- 引入了修改后的IOU意识冗余消除模块,以减少假阳性样本并提高探测器的准确性.
主要成果:
- 在KITTI数据集中,压缩单阶段探测器在效率和准确性方面表现出高于双阶段探测器的性能.
- 在WOD-mini数据集上实现了对CenterPoint的5.2×内存占地面积的减少.
- 在WOD-mini数据集上,L2 mAPH提高了0.55%,显示了显著的压缩收益.
结论:
- 拟议的类别知识驱动压缩框架有效地提高了基于LiDAR的3D物体探测器的效率和准确性.
- 在异质检测器设置中,CaKD和结构化修剪能够有效地转移知识和优化架构.
- 该方法为在资源有限的自动驾驶系统中部署紧而强大的DNN提供了可行的解决方案.
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