基于激光雷达点云的车辆目标检测算法改进研究

本文提出了一种基于PointRCNN的改进目标检测算法。该方法针对原始PointRCNN对远距离处的车辆检测效果较差的问题进行了优化，并提高了算法目标检测的平均精度值。改进算法第1阶段先将激光雷达点云进行伪图像处理，降维至二维，然后利用Point-Focus结构对其进行处理并还原至三维点云。再将其送入PointNet++主干网络中进行特征提取，得到点的分类与回归结果并进行第1阶段的3D框生成。第2阶段对3D框进行优化选择，引入Point-CSPNet结构进一步提升网络学习能力和鲁棒性。本文合理借鉴了YOLO系列算法中的Focus、CSPNet结构，充分提取了原始点云中的有效信息，有效整合了网络运算过程中的特征及梯度变化，提高网络的检测准确率。本文的改进算法在KITTI数据集的3D场景下平均精度值从81.10%提升至81.74%;BEV场景下平均精度值从86.87%提升至88.20%,可视化效果中远距离处的车辆目标检测效果也得到了一定程度的优化，对无人驾驶技术进一步优化和完善具有一定的积极意义。

Research on improving vehicle target detection algorithm based on lidar point cloud

This paper presents a target detection algorithm based on PointRCNN. This method is aimed at vehicle targets. Aiming at the problem that the original PointRCNN is poor in vehicle detection at a distance, the method is optimized and the average accuracy of target detection is improved. In the first stage, the lidar point cloud is processed by pseudo-image structure and dimensionality reduction to 2D, and then processed by Point-Focus structure and restored to 3D point cloud. Then it will be sent into the backbone of PointNet++ for feature extraction, classification and regression. In the second stage, 3D frame is optimized and selected, and Point-CSPNet structure is introduced to further improve network learning ability and robustness. In this paper, the Focus and CSPNet structures of YOLO series algorithms are used for reference. The effective information in the original point cloud is fully extracted and the feature, gradient changes in the network operation are effectively integrated to improve the detection accuracy of the network. The average accuracy of the improved algorithm is improved from 81.10% to 81.74% in 3D scenes of KITTI dataset; and it is improved from 86.87% to 88.20% in BEV scenes of KITTI dataset, and the detection effect of vehicle targets in the far distance of visual effect has also been optimized to a certain extent, which has certain positive significance for further optimization and improvement of unmanned driving technology.