The high bandwidth and low latency of 6G network technology enable the successful application of monocular 3D object detection on vehicle platforms.Monocular 3D-object-detection-based Pseudo-LiDAR is a low-cost,lowpow...The high bandwidth and low latency of 6G network technology enable the successful application of monocular 3D object detection on vehicle platforms.Monocular 3D-object-detection-based Pseudo-LiDAR is a low-cost,lowpower solution compared to LiDAR solutions in the field of autonomous driving.However,this technique has some problems,i.e.,(1)the poor quality of generated Pseudo-LiDAR point clouds resulting from the nonlinear error distribution of monocular depth estimation and(2)the weak representation capability of point cloud features due to the neglected global geometric structure features of point clouds existing in LiDAR-based 3D detection networks.Therefore,we proposed a Pseudo-LiDAR confidence sampling strategy and a hierarchical geometric feature extraction module for monocular 3D object detection.We first designed a point cloud confidence sampling strategy based on a 3D Gaussian distribution to assign small confidence to the points with great error in depth estimation and filter them out according to the confidence.Then,we present a hierarchical geometric feature extraction module by aggregating the local neighborhood features and a dual transformer to capture the global geometric features in the point cloud.Finally,our detection framework is based on Point-Voxel-RCNN(PV-RCNN)with high-quality Pseudo-LiDAR and enriched geometric features as input.From the experimental results,our method achieves satisfactory results in monocular 3D object detection.展开更多
目的针对激光雷达点云稀疏性导致小目标检测精度下降的问题,提出一种伪激光点云增强技术,利用图像与点云融合,对稀疏的小目标几何信息进行补充,提升道路场景下三维目标检测性能。方法首先,使用深度估计网络获取双目图像的深度图,利用激...目的针对激光雷达点云稀疏性导致小目标检测精度下降的问题,提出一种伪激光点云增强技术,利用图像与点云融合,对稀疏的小目标几何信息进行补充,提升道路场景下三维目标检测性能。方法首先,使用深度估计网络获取双目图像的深度图,利用激光点云对深度图进行深度校正,减少深度估计误差;其次,采用语义分割的方法获取图像的前景区域,仅将前景区域对应的深度图映射到三维空间中生成伪激光点云,提升伪激光点云中前景点的数量占比;最后,根据不同的观测距离对伪激光点云进行不同线数的下采样,并与原始激光点云进行融合作为最终的输入点云数据。结果在KITTI(Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago)数据集上的实验结果表明,该方法能够提升多个最新网络框架的小目标检测精度,以典型网络SECOND(sparsely embedded convolutional detection)、MVX-Net(multimodal voxelnet for 3D object detection)、Voxel-RCNN为例,在困难等级下,三维目标检测精度分别获得8.65%、7.32%和6.29%的大幅提升。结论该方法适用于所有以点云为输入的目标检测网络,并显著提升了多个目标检测网络在道路场景下的小目标检测性能。该方法具备有效性与通用性。展开更多
目的生物气溶胶激光遥测系统按光源配置参数主要分为3类,即传统激光雷达、微脉冲激光雷达、伪随机调制激光雷达。其系统光源参数严重影响系统的危险性和探测灵敏度,需要进行优化计算。方法参考美国关于激光产品使用的安全标准,并建立对...目的生物气溶胶激光遥测系统按光源配置参数主要分为3类,即传统激光雷达、微脉冲激光雷达、伪随机调制激光雷达。其系统光源参数严重影响系统的危险性和探测灵敏度,需要进行优化计算。方法参考美国关于激光产品使用的安全标准,并建立对应的激光雷达的数学模型,对比3种激光雷达光源配置方式的信噪比(SNR)和安全性,计算重复频率、脉冲能量、发散角、危险距离等因素的影响。结果计算结果表明,在保证人眼安全的前提下,使用微脉冲雷达的光源激发方式,脉冲频率设置约为55 k Hz时系统探测的SNR最高。结论人眼安全是前提,对于激光遥测系统的光源激发方式影响较大,该文计算出最优的光源配置方法,阐明了较为安全的应用方式。展开更多
基金supported by the National Key Research and Development Program of China(2020YFB1807500)the National Natural Science Foundation of China(62072360,62001357,62172438,61901367)+4 种基金the key research and development plan of Shaanxi province(2021ZDLGY02-09,2023-GHZD-44,2023-ZDLGY-54)the Natural Science Foundation of Guangdong Province of China(2022A1515010988)Key Project on Artificial Intelligence of Xi'an Science and Technology Plan(2022JH-RGZN-0003,2022JH-RGZN-0103,2022JH-CLCJ-0053)Xi'an Science and Technology Plan(20RGZN0005)the Proof-ofconcept fund from Hangzhou Research Institute of Xidian University(GNYZ2023QC0201).
文摘The high bandwidth and low latency of 6G network technology enable the successful application of monocular 3D object detection on vehicle platforms.Monocular 3D-object-detection-based Pseudo-LiDAR is a low-cost,lowpower solution compared to LiDAR solutions in the field of autonomous driving.However,this technique has some problems,i.e.,(1)the poor quality of generated Pseudo-LiDAR point clouds resulting from the nonlinear error distribution of monocular depth estimation and(2)the weak representation capability of point cloud features due to the neglected global geometric structure features of point clouds existing in LiDAR-based 3D detection networks.Therefore,we proposed a Pseudo-LiDAR confidence sampling strategy and a hierarchical geometric feature extraction module for monocular 3D object detection.We first designed a point cloud confidence sampling strategy based on a 3D Gaussian distribution to assign small confidence to the points with great error in depth estimation and filter them out according to the confidence.Then,we present a hierarchical geometric feature extraction module by aggregating the local neighborhood features and a dual transformer to capture the global geometric features in the point cloud.Finally,our detection framework is based on Point-Voxel-RCNN(PV-RCNN)with high-quality Pseudo-LiDAR and enriched geometric features as input.From the experimental results,our method achieves satisfactory results in monocular 3D object detection.
文摘目的针对激光雷达点云稀疏性导致小目标检测精度下降的问题,提出一种伪激光点云增强技术,利用图像与点云融合,对稀疏的小目标几何信息进行补充,提升道路场景下三维目标检测性能。方法首先,使用深度估计网络获取双目图像的深度图,利用激光点云对深度图进行深度校正,减少深度估计误差;其次,采用语义分割的方法获取图像的前景区域,仅将前景区域对应的深度图映射到三维空间中生成伪激光点云,提升伪激光点云中前景点的数量占比;最后,根据不同的观测距离对伪激光点云进行不同线数的下采样,并与原始激光点云进行融合作为最终的输入点云数据。结果在KITTI(Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago)数据集上的实验结果表明,该方法能够提升多个最新网络框架的小目标检测精度,以典型网络SECOND(sparsely embedded convolutional detection)、MVX-Net(multimodal voxelnet for 3D object detection)、Voxel-RCNN为例,在困难等级下,三维目标检测精度分别获得8.65%、7.32%和6.29%的大幅提升。结论该方法适用于所有以点云为输入的目标检测网络,并显著提升了多个目标检测网络在道路场景下的小目标检测性能。该方法具备有效性与通用性。
文摘目的生物气溶胶激光遥测系统按光源配置参数主要分为3类,即传统激光雷达、微脉冲激光雷达、伪随机调制激光雷达。其系统光源参数严重影响系统的危险性和探测灵敏度,需要进行优化计算。方法参考美国关于激光产品使用的安全标准,并建立对应的激光雷达的数学模型,对比3种激光雷达光源配置方式的信噪比(SNR)和安全性,计算重复频率、脉冲能量、发散角、危险距离等因素的影响。结果计算结果表明,在保证人眼安全的前提下,使用微脉冲雷达的光源激发方式,脉冲频率设置约为55 k Hz时系统探测的SNR最高。结论人眼安全是前提,对于激光遥测系统的光源激发方式影响较大,该文计算出最优的光源配置方法,阐明了较为安全的应用方式。
