Rail-Pillar Net:A 3D Detection Network for Railway Foreign Object Based on LiDAR

Aiming at the limitations of the existing railway foreign object detection methods based on two-dimensional(2D)images,such as short detection distance,strong influence of environment and lack of distance information,we propose Rail-PillarNet,a three-dimensional(3D)LIDAR(Light Detection and Ranging)railway foreign object detection method based on the improvement of PointPillars.Firstly,the parallel attention pillar encoder(PAPE)is designed to fully extract the features of the pillars and alleviate the problem of local fine-grained information loss in PointPillars pillars encoder.Secondly,a fine backbone network is designed to improve the feature extraction capability of the network by combining the coding characteristics of LIDAR point cloud feature and residual structure.Finally,the initial weight parameters of the model were optimised by the transfer learning training method to further improve accuracy.The experimental results on the OSDaR23 dataset show that the average accuracy of Rail-PillarNet reaches 58.51%,which is higher than most mainstream models,and the number of parameters is 5.49 M.Compared with PointPillars,the accuracy of each target is improved by 10.94%,3.53%,16.96%and 19.90%,respectively,and the number of parameters only increases by 0.64M,which achieves a balance between the number of parameters and accuracy.

Volumetric assessment of hepatic grafts using a light detection and ranging system for 3D scanning:Preliminary data

BACKGROUND Liver transplantation has evolved into a safe life-saving operation and remains the golden standard in the treatment of end stage liver disease.The main limiting factor in the application of liver transplantation is graft shortage.Many strategies have been developed in order to alleviate graft shortage,such as living donor partial liver transplantation and split liver transplantation for adult and pediatric patients.In these strategies,liver volume assessment is of paramount importance,as size mismatch can have severe consequences in the success of liver transplantation.AIM To evaluate the safety,feasibility,and accuracy of light detection and ranging(LIDAR)3D photography in the prediction of whole liver graft volume and mass.METHODS Seven liver grafts procured for orthotopic liver transplantation from brain deceased donors were prospectively measured with an LIDAR handheld camera and their mass was calculated and compared to their actual weight.RESULTS The mean error of all measurements was 17.03 g(range 3.56-59.33 g).Statistical analysis of the data yielded a Pearson correlation coefficient index of 0.9968,indicating a strong correlation between the values and a Student’s t-test P value of 0.26.Mean accuracy of the measurements was calculated at 97.88%.CONCLUSION Our preliminary data indicate that LIDAR scanning of liver grafts is a safe,cost-effective,and feasible method of ex vivo determination of whole liver volume and mass.More data are needed to determine the precision and accuracy of this method.

林区机载LiDAR点云的多分辨率层次布料模拟滤波

针对现有机载LiDAR(light detection and ranging)点云滤波方法在地形起伏剧烈的林区适用性不足的问题,提出一种多分辨率层次布料模拟滤波方法。首先,通过多尺度形态学开运算选择大量种子地面点;然后,基于种子地面点,使用布料模拟法由低至高逐层构建参考地形,以快速获取高分辨率参考地形;最后,基于点至参考地形的高差区分地面点和非地面点。利用国际摄影测量和遥感学会提供的数据集和参考方法,评估该方法性能。利用在中国、美国多个代表性林区的点云数据,评估该方法的可推广性。结果表明,该方法的Kappa系数和运行时间是83.72%和34.11 s,精度和效率较经典布料模拟滤波方法提高10.49%和52.17%。相比8种参考方法,该方法能够获得更高精度,并且具有稳定的可推广性。

Development of vehicle-recognition method on water surfaces using LiDAR data:SPD^(2)(spherically stratified point projection with diameter and distance)

Swarm robot systems are an important application of autonomous unmanned surface vehicles on water surfaces.For monitoring natural environments and conducting security activities within a certain range using a surface vehicle,the swarm robot system is more efficient than the operation of a single object as the former can reduce cost and save time.It is necessary to detect adjacent surface obstacles robustly to operate a cluster of unmanned surface vehicles.For this purpose,a LiDAR(light detection and ranging)sensor is used as it can simultaneously obtain 3D information for all directions,relatively robustly and accurately,irrespective of the surrounding environmental conditions.Although the GPS(global-positioning-system)error range exists,obtaining measurements of the surface-vessel position can still ensure stability during platoon maneuvering.In this study,a three-layer convolutional neural network is applied to classify types of surface vehicles.The aim of this approach is to redefine the sparse 3D point cloud data as 2D image data with a connotative meaning and subsequently utilize this transformed data for object classification purposes.Hence,we have proposed a descriptor that converts the 3D point cloud data into 2D image data.To use this descriptor effectively,it is necessary to perform a clustering operation that separates the point clouds for each object.We developed voxel-based clustering for the point cloud clustering.Furthermore,using the descriptor,3D point cloud data can be converted into a 2D feature image,and the converted 2D image is provided as an input value to the network.We intend to verify the validity of the proposed 3D point cloud feature descriptor by using experimental data in the simulator.Furthermore,we explore the feasibility of real-time object classification within this framework.

Three-channel CMOS transimpedance amplifier for LiDAR sensor receiver

For time-of-flight(TOF)light detection and ranging(LiDAR),a three-channel high-performance transimpedance amplifier(TIA)with high immunity to input load capacitance is presented.A regulated cascade(RGC)as the input stage is at the core of the complementary metal oxide semiconductor(CMOS)circuit chip,giving it more immunity to input photodiode detectors.A simple smart output interface acting as a feedback structure,which is rarely found in other designs,reduces the chip size and power consumption simultaneously.The circuit is designed using a 0.5μm CMOS process technology to achieve low cost.The device delivers a 33.87 dB?transimpedance gain at 350 MHz.With a higher input load capacitance,it shows a-3 dB bandwidth of 461 MHz,indicating a better detector tolerance at the front end of the system.Under a 3.3 V supply voltage,the device consumes 5.2 mW,and the total chip area with three channels is 402.8×597.0μm2(including the test pads).

A review of ToF-based LiDAR

In recent years,propelled by the rapid iterative advancements in digital imaging technology and the semiconductor industry,encompassing microelectronic design,manufacturing,packaging,and testing,time-of-flight(ToF)-based imaging systems for acquiring depth information have garnered considerable attention from both academia and industry.This technology has emerged as a focal point of research within the realm of 3D imaging.Owing to its relatively straightforward principles and exceptional performance,ToF technology finds extensive applications across various domains including human−computer interaction,autonomous driving,industrial inspection,medical and healthcare,augmented reality,smart homes,and 3D reconstruction,among others.Notably,the increasing maturity of ToF-based LiDAR systems is evident in current developments.This paper comprehensively reviews the fundamental principles of ToF technology and LiDAR systems,alongside recent research advancements.It elucidates the innovative aspects and technical challenges encountered in both transmitter(TX)and receiver(RX),providing detailed discussions on corresponding solutions.Furthermore,the paper explores prospective avenues for future research,offering valuable insights for subsequent investigations.

基于无人机载LiDAR点云数据的电力线提取方法

机载激光雷达(LiDAR)点云电力线提取过程中存在杆塔形状复杂、噪声影响大等问题,导致电力线点云提取精度低,本文提出一种基于点云分块处理、格网划分的曲面拟合滤波、自适应密度聚类算法的电力线点云提取与重建方法。首先,根据电力线走向,对整体点云进行分块处理;其次,在曲面拟合算法的基础上,引入格网划分思想,提出一种改进曲面拟合滤波算法并进行点云滤波;最后,通过给出自适应密度聚类解决方案精确提取电力线点云。借助点云库(PCL)、libLAS库与Visual Studio 2017 C++开发环境实现本文算法,基于实测点云数据对本文方法进行测试与精度评定。结果表明:电力线提取精确率为97.82%、召回率为99.76%、F1值为98.78%,一次便可实现电力线的成功提取,在保证提取精度的同时提升了提取效率,本文研究能够为电力线智能巡检提供良好的工程应用价值。

基于无人机LiDAR技术的森林资源精细化调查与监测

森林资源对全球生态平衡和生物多样性维护至关重要。为准确了解森林资源状况,利用无人机LiDAR技术,针对中国东北针叶林进行精细化调查与长期监测。通过多旋翼无人机和高精度LiDAR传感器,获取高精度三维点云数据,提取森林结构参数并建立生物量估算模型。结果表明:无人机LiDAR技术高效、精确、可靠,能为森林资源管理提供新技术手段。长期监测数据显示,树高、冠层密度和生物量均呈增加趋势,证明该技术在森林资源调查和监测中的重要应用价值。

无人机LiDAR免控制测量技术在1∶10000 DEM更新中的应用

在安徽省1∶10000基础地理信息数据更新工程DEM局部更新工作中,针对外业中控制点布设工作量大、难度高的特点,研究无人机LiDAR测量技术生产DEM的免控制技术方法,利用激光点云分类结果中提取的可靠地面点,建立高程拟合模型,拟合转换点云高程,生产DEM。并与传统通过实测控制点进行点云高程拟合生产的DEM成果精度比较。研究结果表明:利用免控制技术方法生产的DEM精度可以满足安徽省1∶10000 DEM更新要求。

基于多元GMM的机载多光谱LiDAR点云空谱联合分类

针对传统机载多光谱激光雷达(multispectral light detection and ranging,MS-LiDAR)土地覆盖分类方法空谱信息协同利用能力不足或多类型特征联合利用时特征维数过高的缺陷,提出一种基于多元高斯混合模型(Gaussian mixture model,GMM)的机载MS-LiDAR点云空谱联合分割算法。该算法首先对原始多波段独立点云进行辐射校正、异常剔除及融合,形成同时表达空间位置及其对应多波段光谱信息的多光谱点云;然后,提取各激光点的多光谱、高程等特征构建空谱特征矢量,并通过特征标准化及离散化消除不同类型特征间的单位和尺度差异;再次,构建多元GMM建模目标在空谱特征空间呈现的多峰分布,获取激光点属于各类目标的响应度并按照最大响应度原则确定类属;最后,设计3D多数投票法优化分割结果。实验基于实测的Optech Titan MS-LiDAR数据验证提出算法的有效性和可行性。实验结果表明:联合多波段强度特征及高程特征的多元GMM的分割总体精度可达93.57%,Kappa系数可达0.912,仅联合四维特征即可实现MS-LiDAR点云的高精度分割。该项研究可为综合利用MS-LiDAR数据的多光谱及空间信息提供新途径。

高精度LiDAR技术在活动断层断错地貌研究中的应用——以沂沭断裂带莒县至郯城段为例

郯庐断裂带是我国东部地区最为重要的大型活动断裂之一,具有较强的发震能力,断裂带沿线多处穿过人类活动及聚集的城市。为了研究郯庐断裂带的几何展布及活动性特征,选取郯庐断裂带的沂沭断裂莒县至郯城范围为研究对象,考虑东部地区植被覆盖,采用机载激光雷达和地基激光雷达手段采集了断裂带沿线左山(一步涧)段、钟华山段、岌山段、马陵山段以及蒋家岭等地高精度地形数据,对断裂的滑动分布特征进行分析。通过对测量得到的点云数据进行点云匹配、镶嵌、植物滤除以及不规则三角网建模处理,得到了0.1 m空间分辨率的数字高程模型(Digital Elevation Model,DEM),实现了对断裂微地貌形态的高清晰度三维再现,提取了断裂断错冲沟右旋水平位错量和断层陡坎垂直位错量,结合地质填图和探槽揭露的古地震事件进行验证和对比,对郯庐断裂带莒县至郯城段的断层活动特征进行了定量分析。结果表明,沿断裂带左山(一步涧)段、钟华山段、岌山段和马陵山段的水平和垂直位错量具有分级特征,晚第四纪以来可能发生过多期(3~5次)活动和多次古地震事件。研究成果为进一步研究郯庐断裂运动学和几何学提供了重要依据,同时也说明地基激光雷达和机载激光雷达技术在活动断层研究中有着广泛的应用前景。

不同森林覆盖类型对LiDAR生成DEM的精度影响分析

运用机载激光雷达(LiDAR)获取的高精度数字高程模型(DEM)是目前地学分析应用满足精细化要求的基础数据。目前对森林覆盖下DEM产品的精度影响分析依然有所欠缺。以实时动态载波相位差分技术(RTK)结合全站仪实测的高程碎部点与机载LiDAR数据生成的高精度DEM产品数值做差,进行单因素方差分析(ANOVA)检验以及独立样本T-检验。经多重比较分析(LSD)可知,以马尾松为代表的针叶林和以桉树、茶树、黄连木、荔枝、阔叶混合林为代表的阔叶林之间差值相差较大,说明针叶林和阔叶林两种不同森林覆盖类型的绝对精度存在差异。但Robust检验结果显示阔叶林地表DEM的精度均值相等性为1,说明阔叶林(以桉树、茶树、黄连木、荔枝、阔叶混合林林地为代表)地表DEM精度不受树种的影响。综上,阔叶林与针叶林的地表DEM精度差异显著,说明阔叶林和针叶林对机载LiDAR生成森林区域地表DEM存在一定的影响。

乌鲁木齐机场一次冷锋型低空风切变过程的LiDAR分析

为了研究低空风切变风场结构,针对乌鲁木齐机场2021-11-26发生的风切变不安全事件,采用FC-Ⅲ型激光测风雷达产品数据,配合美国国家环境预报中心再分析资料和常规气象观测资料进行分析和验证,取得了风切变演变过程的数据。结果表明,该次风切变过程发生在特定的地形风作用下,冷锋前小尺度冷空气造成显著的风向风速变化,东南风急流底部与西北风风带形成倾斜向上的垂直切变区,并引发冷锋型低空风切变;风切变发生前1 h,乌鲁木齐机场周边出现了风场转换;平面位置显示模式比航空器报告提前10 min监测到风切变,为东南大风风速骤减区,且风切变区随冷空气渗透西移;冷空气渗透过程东南大风层变薄西撤;07^(#)跑道附近,正侧风迅速减小且进近过程中伴有风向的大角度转变;冷空气由25^(#)跑道向07^(#)跑道楔形渗透,渗透过程发生在08:30~10:25期间;激光雷达捕捉到该次低空小尺度冷空气活动,分析出冷空气由东北侧进入呈后倾状态的演变过程和结构,并触发了中度风切变预警。这一结果对提高气象服务保障能力是有帮助的。

融合机载LiDAR和植被指数的自适应单木提取方法

机载激光数据(Light Detection And Ranging,LiDAR)难以区分地面和草地范围,可见光植被指数无法分离灌木和乔木层,针对上述问题,构建一种融合LiDAR点云与RGB植被指数的多波段信息图像。以激光点云生成精细冠层高度模型(Canopy Height Model,CHM),利用无人机影像数据生成高清数字正射影像,在比较不同植被指数精度后选择差异增强植被指数(Differential Enhanced Vegetation Index,DEVI),与CHM进行融合。形态学重建CHM+DEVI融合图像,去除不合理值;构建训练样本,采用分类回归树算法,分割地面范围并自适应提取植被为乔木、灌木和草地,乔木区域采用局部最大值算法探测树顶点,作为前景标记,非乔木区域赋为后景标记,进行分水岭变换得到分割结果。将该方法提取的植被信息与实测数据进行精度分析,结果表明:改进方法在4个样方中,总体查全率提高3.2%,查准率提高3.9%,准确度F1得分提高3.5%,树高精度分别提高1.7%,6.4%,1.8%和0.3%。验证了改进方法的有效性,同时区域内植被混杂程度越高改进算法的提取效果越好。

基于机载LiDAR点云的建筑物多细节层次建模方法

机载激光雷达点云(LiDAR)重建大范围建筑物模型一直是实景三维建模的热点问题,由于机载激光点云稀疏、建筑物立面数据缺失,给模型自动化重建带来了极大的挑战。为了解决该问题,本论文研究基于图优化理论重建建筑物多细节层次(LOD)模型。首先,对原始点云进行自动化滤波,分离地面点和非地面点,对地面点构建数字高程模型(DEM),对非地面点半自动提取单体化建筑物点云。然后,基于滚球法(Alpha shape)提取建筑物边界,利用通用图优化方法(G2O)对误差线进行全局一致性改正,获得规则化的二维边框。并基于建筑物屋顶三维高程及DEM高程值重建建筑物LOD1模型。其次,根据点云的高程差异生成高程栅格图,从高程栅格图提取建筑物轮廓线,对轮廓线进行简化、规则化、聚类,并将规则后的边界线拉伸获取建筑物立面结构,弥补建筑物立面数据缺失对建模的影响。最后,将屋顶平面相交、建筑物立面裁剪,对候选平面进行二元图割全局优化,选择最能表达建筑物结构的平面,以此重建建筑物LOD2模型。本论文选择北京市2017年机载激光点云进行实验,结果表明,本文提出的方法可以稳健地重建建筑物多细节层次模型,LOD2模型距离偏差为0.22 m,LOD1模型距离偏差为0.71 m,整体模型精确度高;针对1147个建筑物,LOD2和LOD1模型重建时间分别为1290s和1097s,具有较高的自动化程度和建模效率。

倾斜摄影和LiDAR技术融合在大比例尺地形图测绘中的应用

地形图传统测量方法费时费力。本文融合倾斜摄影与激光探测及测距系统技术,以抚州市比亚迪厂区地形图测绘项目为例,非房屋区域采用机载激光雷达航测技术,房屋区域采用倾斜摄影测量航测技术成图。最后通过外业采集检查点与内业测图成果进行精度对比分析。结果表明:该技术测绘成果平面与高程均能满足大比例尺测图的精度要求,而且能节约地形图制作时间及成本。

基于机载LiDAR点云的电力线提取与三维重建

为了解决地形复杂、点云密度不均匀的输电线机载激光雷达(LiDAR)点云电力线提取精度低的问题,本文根据电力线点的空间分布特征设计与实现了一套电力线提取与三维重建方法。首先,使用改进曲面拟合滤波算法与形态学开运算实现地面点、低矮植被点等的滤除;其次,以滤波处理得到点云数据为数据源,利用电力线点维度特征实现电力线点粗提取并利用密度聚类算法进行单根电力线精提取;最后,基于单根电力线提取结果进行电力线三维重建。为了对本文提出电力线提取与重建方法进行检验,使用宁波市某高压交流输变电工程中部分实测机载LiDAR点云数据进行实验,结果表明,本文方法提取28根电力线结果误差率均在0.04%以内,验证了本文方法的可靠性与实用性。

机载LiDAR河流高程拟合算法

使用机载激光雷达(LiDAR)进行数字高程模型(DEM)制作过程中,河流数据缺失,需进行人工编辑,目前处理流程中难以既保证数据精度又保证整体平整美观,本文提出机载LiDAR河流高程拟合方案,通过半自动河流边线提取、河流中心线提取以及中心线高程拟合一系列技术流程,不仅解决当前复杂的人工编辑问题,还提升了拟合精度,文中详细地阐述河流高程拟合关键算法,并基于Micro station V8开发出应用实例,为机载LiDAR河流高程拟合提供新思路。

面向机载LiDAR点云数据的双线河水体DEM构建方法

机载激光雷达(LiDAR)系统可以快速获取高精度的地面点云数据,可以快速生成数字高程模型(DEM)。但是基于原始水体点云数据直接构建DEM效果较差,针对这个问题,论文提出了一种双线河水体点云填充方法,对缺失的河道内点云数据进行填充,并设计开发了河流优化填充模块,实现了双线河水体具有上下游特征的DEM构建效果。

Multi-Scale Feature Extraction for Joint Classification of Hyperspectral and LiDAR Data

With the development of sensors,the application of multi-source remote sensing data has been widely concerned.Since hyperspectral image(HSI)contains rich spectral information while light detection and ranging(LiDAR)data contains elevation information,joint use of them for ground object classification can yield positive results,especially by building deep networks.Fortu-nately,multi-scale deep networks allow to expand the receptive fields of convolution without causing the computational and training problems associated with simply adding more network layers.In this work,a multi-scale feature fusion network is proposed for the joint classification of HSI and LiDAR data.First,we design a multi-scale spatial feature extraction module with cross-channel connections,by which spatial information of HSI data and elevation information of LiDAR data are extracted and fused.In addition,a multi-scale spectral feature extraction module is employed to extract the multi-scale spectral features of HSI data.Finally,joint multi-scale features are obtained by weighting and concatenation operations and then fed into the classifier.To verify the effective-ness of the proposed network,experiments are carried out on the MUUFL Gulfport and Trento datasets.The experimental results demonstrate that the classification performance of the proposed method is superior to that of other state-of-the-art methods.