论文标题
对点云中3D对象检测的点孔网络的优化
Optimisation of the PointPillars network for 3D object detection in point clouds
论文作者
论文摘要
在本文中，我们介绍了有关在点云中进行3D对象检测的深神经网络优化的研究。使用了Brevitas和Pytorch工具中可用的定量和修剪等技术。我们对Pointpillars网络进行了实验，该实验在检测准确性和计算复杂性之间提供了合理的折衷。这项工作的目的是提出网络的变体，我们最终将在FPGA设备中实现。这将允许使用低能消耗的实时LIDAR数据处理。获得的结果表明，即使在算法的主要部分中从32位浮点到2位整数的显着定量也会导致检测准确性降低5％-9％，同时允许模型的大小减小近16倍。
In this paper we present our research on the optimisation of a deep neural network for 3D object detection in a point cloud. Techniques like quantisation and pruning available in the Brevitas and PyTorch tools were used. We performed the experiments for the PointPillars network, which offers a reasonable compromise between detection accuracy and calculation complexity. The aim of this work was to propose a variant of the network which we will ultimately implement in an FPGA device. This will allow for real-time LiDAR data processing with low energy consumption. The obtained results indicate that even a significant quantisation from 32-bit floating point to 2-bit integer in the main part of the algorithm, results in 5%-9% decrease of the detection accuracy, while allowing for almost a 16-fold reduction in size of the model.
