3D object detection from LiDAR sensor data is an important topic in the
context of autonomous cars and drones. In this paper, we present the results of
experiments on the impact of backbone selection of a deep convolutional neural
network on detection accuracy and computation speed. We chose the PointPillars
network, which is characterised by a simple architecture, high speed, and
modularity that allows for easy expansion. During the experiments, we paid
particular attention to the change in detection efficiency (measured by the mAP
metric) and the total number of multiply-addition operations needed to process
one point cloud. We tested 10 different convolutional neural network
architectures that are widely used in image-based detection problems. For a
backbone like MobilenetV1, we obtained an almost 4x speedup at the cost of a
1.13% decrease in mAP. On the other hand, for CSPDarknet we got an acceleration
of more than 1.5x at an increase in mAP of 0.33%. We have thus demonstrated
that it is possible to significantly speed up a 3D object detector in LiDAR
point clouds with a small decrease in detection efficiency. This result can be
used when PointPillars or similar algorithms are implemented in embedded
systems, including SoC FPGAs. The code is available at
this https URL

本研究探讨了深度卷积神经网络背骨选择对于基于 LiDAR 传感器数据的 3D 目标检测精度和计算速度的影响，测试了 10 种不同的卷积神经网络架构，并在保证检测效率（以 mAP 度量）的前提下，显著提高 LiDAR 点云中 3D 物体检测速度。