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
针对现有基于伪点云的3D目标检测算法精度远低于基于真实激光雷达(Light Detection and ranging,LiDar)点云的3D目标检测,本文研究伪点云重构,并提出适合伪点云的3D目标检测网络.考虑到由图像深度转换得到的伪点云稠密且随深度增大逐渐...针对现有基于伪点云的3D目标检测算法精度远低于基于真实激光雷达(Light Detection and ranging,LiDar)点云的3D目标检测,本文研究伪点云重构,并提出适合伪点云的3D目标检测网络.考虑到由图像深度转换得到的伪点云稠密且随深度增大逐渐稀疏,本文提出深度相关伪点云稀疏化方法,在减少后续计算量的同时保留中远距离更多的有效伪点云,实现伪点云重构.本文提出LiDar点云指导下特征分布趋同与语义关联的3D目标检测网络,在网络训练时引入LiDar点云分支来指导伪点云目标特征的生成,使生成的伪点云特征分布趋同于LiDar点云特征分布,从而降低数据源不一致造成的检测性能损失;针对RPN(Region Proposal Network)网络获取的3D候选框内的伪点云间语义关联不足的问题,设计注意力感知模块,在伪点云特征表示中通过注意力机制嵌入点间的语义关联关系,提升3D目标检测精度.在KITTI 3D目标检测数据集上的实验结果表明:现有的3D目标检测网络采用重构后的伪点云,检测精度提升了2.61%;提出的特征分布趋同与语义关联的3D目标检测网络,将基于伪点云的3D目标检测精度再提升0.57%,相比其他优秀的3D目标检测方法在检测精度上也有提升.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。
