论文标题
快速，准确的LIDAR对象检测的尖柱骨干型选择
PointPillars Backbone Type Selection For Fast and Accurate LiDAR Object Detection
论文作者
论文摘要
在自动驾驶汽车和无人机的背景下，LIDAR传感器数据中的3D对象检测是一个重要主题。在本文中，我们介绍了对深卷神经网络对检测准确性和计算速度的骨架选择的影响的实验结果。我们选择了Pointpillars网络，该网络的特征是简单的体系结构，高速和模块化，可以轻松扩展。在实验过程中，我们特别注意检测效率的变化（通过MAP度量测量）以及处理一个点云所需的乘积操作总数。我们测试了10种不同的卷积神经网络体系结构，这些结构广泛用于基于图像的检测问题。对于像MobilenetV1这样的主干，我们获得了几乎4倍的加速，而MAP的成本减少了1.13％。另一方面，对于CSPDARKNET，我们的加速度超过1.5倍，地图增加了0.33％。因此，我们已经证明，可以在LiDAR点云中显着加速3D对象检测器，检测效率较小。当在包括SOC FPGA在内的嵌入式系统中实现指数或类似的算法时，可以使用此结果。该代码可在https://github.com/vision-agh/pointpillars \ _backbone上找到。
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 https://github.com/vision-agh/pointpillars\_backbone.
