Intelligent extraction of road cracks based on vehicle laser point cloud and panoramic sequence images

In light of the limited efficacy of conventional methods for identifying pavement cracks and the absence of comprehensive depth and location data in two-dimensional photographs,this study presents an intelligent strategy for extracting road cracks.This methodology involves the integration of laser point cloud data obtained from a vehicle-mounted system and a panoramic sequence of images.The study employs a vehicle-mounted LiDAR measurement system to acquire laser point cloud and panoramic sequence image data simultaneously.A convolutional neural network is utilized to extract cracks from the panoramic sequence image.The extracted sequence image is then aligned with the laser point cloud,enabling the assignment of RGB information to the vehicle-mounted three dimensional(3D)point cloud and location information to the two dimensional(2D)panoramic image.Additionally,a threshold value is set based on the crack elevation change to extract the aligned roadway point cloud.The three-dimensional data pertaining to the cracks can be acquired.The experimental findings demonstrate that the use of convolutional neural networks has yielded noteworthy outcomes in the extraction of road cracks.The utilization of point cloud and image alignment techniques enables the extraction of precise location data pertaining to road cracks.This approach exhibits superior accuracy when compared to conventional methods.Moreover,it facilitates rapid and accurate identification and localization of road cracks,thereby playing a crucial role in ensuring road maintenance and traffic safety.Consequently,this technique finds extensive application in the domains of intelligent transportation and urbanization development.The technology exhibits significant promise for use in the domains of intelligent transportation and city development.

基于知识蒸馏和定位引导的Pointpillars点云检测网络

激光雷达数据由于其几何特性,被广泛应用于三维目标检测任务中。由于点云数据的稀疏性和不规则性,难以实现特征提取的质量和推理速度间的平衡。本文提出一种基于体柱特征编码的三维目标检测算法,以Pointpillars网络为基础,设计Teacher-Student模型框架对回归框尺度进行蒸馏,增加蒸馏损失,优化训练网络模型,提升特征提取的质量。为进一步提高模型检测效果,设计定位引导分类项,增加分类预测和回归预测之间的相关性,提高物体识别准确率。本网络所做改进没有引入额外的网络嵌入。算法在KITTI数据集上的实验结果表明,相比于基准网络,在三维模式下的平均精度值从60.65%提升到了64.69%,鸟瞰图模式下的平均精度值从67.74%提升到70.24%。模型推理速度为45 FPS,在提升检测精度的同时满足了实时性要求。

Building Facade Point Clouds Segmentation Based on Optimal Dual-Scale Feature Descriptors

To address the current issues of inaccurate segmentation and the limited applicability of segmentation methods for building facades in point clouds, we propose a facade segmentation algorithm based on optimal dual-scale feature descriptors. First, we select the optimal dual-scale descriptors from a range of feature descriptors. Next, we segment the facade according to the threshold value of the chosen optimal dual-scale descriptors. Finally, we use RANSAC (Random Sample Consensus) to fit the segmented surface and optimize the fitting result. Experimental results show that, compared to commonly used facade segmentation algorithms, the proposed method yields more accurate segmentation results, providing a robust data foundation for subsequent 3D model reconstruction of buildings.

Landslide data mosaicking based on an airborne laser point cloud and multi-beam sonar images

Landslides are one of the most disastrous geological hazards in southwestern China.Once a landslide becomes unstable,it threatens the lives and safety of local residents.However,empirical studies on landslides have predominantly focused on landslides that occur on land.To this end,we aim to investigate ashore and underwater landslide data synchronously.This study proposes an optimized mosaicking method for ashore and underwater landslide data.This method fuses an airborne laser point cloud with multi-beam depth sounder images.Owing to their relatively high efficiency and large coverage area,airborne laser measurement systems are suitable for emergency investigations of landslides.Based on the airborne laser point cloud,the traversal of the point with the lowest elevation value in the point set can be used to perform rapid extraction of the crude channel boundaries.Further meticulous extraction of the channel boundaries is then implemented using the probability mean value optimization method.In addition,synthesis of the integrated ashore and underwater landslide data angle is realized using the spatial guide line between the channel boundaries and the underwater multibeam sonar images.A landslide located on the right bank of the middle reaches of the Yalong River is selected as a case study to demonstrate that the proposed method has higher precision thantraditional methods.The experimental results show that the mosaicking method in this study can meet the basic needs of landslide modeling and provide a basis for qualitative and quantitative analysis and stability prediction of landslides.

改进PointNet++模型在道路杆状物提取中的应用

针对现有道路杆状物提取大多需要针对数据类型人工设计特征、泛用性差、自动化程度低等问题,本文提出一种基于改进PointNet++深度学习网络的道路杆状物语义分割方法,实现了对道路杆状物的提取。首先对原网络模型的感受野、分块大小等参数进行调整,使得该模型更适合道路点云数据;然后针对点云数据不平衡的问题,采用焦点损失函数作为模型的损失函数,使占比较少的类别得到充分训练;最后针对PointNet++网络提取特征时没有考虑邻域内各点特征影响关系的问题,采用邻域特征聚合模块融合邻域信息,提升该网络模型对点云特征的学习能力。为验证所提方法的有效性,使用改进后的网络模型在自建的道路点云组成的数据集上进行了试验,相对于经典PointNet++网络,杆状物类的分割精度明显提升,在简单道路和复杂道路上的交并比(IoU)分别提升了8.44%、15.25%,达到了98.88%、92.50%。

TREATMENT OF BRONCHIAL ASTHMA WITH CO_2 LASER IRRADIATING POINTS

Therapeutic effect of bronchisl asthma treated With CO2 laser irradiating points was ob-served.Ninety-six cases of bronchial asthma,that were considered cold and insufficiency type accord-ing to TCM,were divided into two groups:one treated with CO2 laser irradiation at Tiantu（CV 22）,Danzhong（CV 17）,Feishu（BL13）and Dingchuan（EX-BI）（n=48）,and the other with warmmoxibustion at same points（n=48）.The symptoms,physical signs and pulmonary ventilation（PEF.VC,FEV1.0,FEV1％and MMEF）were taken as parameter.xhe results showed that thetherapeutic effect In laser irradiation group was better than that in moxibustion group.Moreover,laser irradiation could obviously improve pulmonary ventilation in patients.Therefore,CO2 laser irra-diation is an effective therapy for bronchial asthma.

Laser therapy on points of acupuncture on nerve repair

Paresthesia is the name given to a temporary or permanent sensory loss caused by several surgical procedures that affected the peripheral sensory nerve.In dentistry,common iatrogenic procedures that can lead to sensory loss include third molar removal,

THE TREATMENT OF CERVICAL SPONDYLOPATHY BY LASER ACUPUNCTURE AT THE CERVICAL JIAJI POINTS: AN ANALYSIS OF THERAPEUTIC EFFECT OF 102 CASES

Laser acupuncture therapy was employed in the treatment of spondylopathy.Thetherapeutic effect of the three types of the spondylopathy,nerve root type,vertebral artery type andspinal cord type were stuied and compared.The results indicate a significant difference between thethree types of spondylopathy.The best therapeutic effect was obtained in nerve root type with a remarkable effective rate of 98.5％;the effective rates for the vertebral artery type and the spinal cordtype were 92.3％and 25％,respectively.Combining laser irradiatlon with acupuncture,the therapyposseses the functions of acupuncture,heat effect and mild moxibustion action.Therefore,it showedeffect quickly and had the advantages of short treatment course and high therapeutic effect.Clinicalobservation has confirmed that laser acupuncture is more effective than single laser irradiation.Thistherapy offers another simple but effective method for the treatment of spondylopathy.

基于多线激光雷达的井下斜坡道无人矿卡定位与建图方法

井下斜坡道的定位与建图是实现井下斜坡道无人驾驶的关键技术之一,矿山井下斜坡道区域为典型非结构化环境特征,且道路具有一定倾斜角度,采用传统SLAM算法无法获得精确里程计信息,导致定位与建图精度难以满足无人矿卡行驶需求。针对上述问题,通过研究激光SLAM(Simultaneous Localization And Mapping)算法LeGO-LOAM,笔者提出一种适用于矿山井下斜坡道环境的定位与建图方法。首先,针对井下斜坡道口两侧均为光滑水泥墙壁,特征点稀少问题,设计了基于人工路标的辅助增强定位方法,有效增加点云特征数量,从而优化位姿估计结果,避免建图漂移现象;然后在特征预处理阶段,提出了一种基于激光点云高度差与坡度信息融合的提取地面点高效算法,通过改善地面地点的选取策略,针对倾斜坑洼路面仍能有效识别地面点,解决了井下斜坡道定位与建图倾斜角度大、误差大等问题;其次,基于CVC(Curved-Voxel Clustering)聚类算法设计了一种斜坡道点云曲率体素聚类算法,采用曲率体素和基于哈希的数据结构对点云进行分割,大幅提高在井下稀疏、噪声环境下点云聚类的鲁棒性;最后,运用Scan-To-Map进行点云匹配,同时兼顾点云配准的性能与速度。在中钢集团山东某井下斜坡道的现场实验证明:与原算法相比精度提升13.15%,Z轴误差降低22.3%,地图质量明显提升,能有效解决井下无人驾驶建图及定位的难题。

46 CASES OF INSOMNIA TREATED BY SEMICONDUCTOR LASER IRRADIATION ON AURICULAR POINTS

Insomnia is a commonly encounteredsymptom in clinical practice. The author hastreated it by using semiconductor laserirradiation on auricular points with satisfactorytherapeutic results. A report is presented asfollows.General DataIn this series, there were 46 cases

基于LiDAR数据在水利DLG生产中的应用

以工程实例为依托,介绍了LiDAR数据制作DLG的主要流程,论证了由DOM及点云滤波生成的DEM制作DLG成果完全满足1∶1 000比例尺精度要求。而且影像数据使用激光雷达的高精度惯导及GPS数据生产的POS,可完全实现免相控。激光点云滤波的DEM与航摄影像DEM对比发现,对植被茂密区域及较窄的水利渠道,点云滤波DEM数据可准确的获取高程信息,而航摄影像DEM数据则无法获得。

隔震支座骨架板平面度检测及误差补偿

针对机械加工行业隔震支座骨架板平面度检测损伤表面并且不能多个连续检测的问题,提出一种基于激光传感器和振动误差补偿的非接触平面度检测系统。首先利用激光传感器对骨架板表面进行扫描,并通过滤波和精简算法来优化骨架板点云;然后将运动平台的偏移量写入每个点云坐标,形成精确的骨架板点云数据;最后将传统最小二乘法升维,计算各测量点到拟合平面的偏距值得到平面度。实验结果表明:系统能够有效检测骨架板的平面度,解决了传统检测效率低并且含振动误差的问题,能够更好地适应自动化检测环境。

基于激光雷达扫描的料仓原料储料量测量方法

为提高仓内原料储料量测量自动化和智能化水平,设计了一种基于二维激光雷达扫描的储料量测量装置与系统。本系统采用RPLIDARS1型二维激光雷达扫描获取不同储料量物料的原始点云数据,通过坐标变换、重叠点提取、滤波、分割等方法对原始点云进行预处理,采用贪婪投影三角化算法将预处理后的点云进行三维重建,获得仓内原料的三维模型,结合物料三维模型和物料的容重获得仓内原料的储料量,从而实现储料量的自动测量。以玉米为试验对象,测量小型储料塔内玉米储料量并对玉米不同储料量进行扫描测量,验证模型的准确性,结果显示:测量结果的平均绝对误差为8.05kg,平均相对误差为1.52%。研究结果表明,基于二维激光雷达扫描的储料量测量方法是可行的,具有较好的稳定性和测量精度,能够满足实际生产需求。

点云加权迭代线拟合室内平面图自动生成方法

提出了一种基于三维激光点云加权迭代线拟合的室内平面图自动生成方法,根据线特征对三维点云沿高程方向的投影点云进行分割,采用加权迭代直线拟合方法进行分段直线拟合,并进一步对拟合直线进行融合和轮廓补全,实现室内平面图的自动生成。基于手持SLAM系统获取的典型室内场景激光点云自动生成室内平面图,结果表明,该方法能有效抑制室内点云噪声,结构提取完整性可达94.4%,制图精度优于3 cm,可快速生成室内平面结构图。

多源点云优化的城市三维模型构建

简易模型与传统复杂的单体模型相比,对存储、可视化、传输和后续应用技术要求低,敏感信息承载量少,且能满足大部分应用需求,具有更高的应用价值。考虑应用需求与技术现状,自然资源部将城市三维模型(L0D1.3级)纳入实景三维中国建设任务中。目前相关生产以半自动化采集为主,工程化应用存在技术流程烦琐、效率较低且对数据量限制较大等问题。本文立足山东实际,提出了一种基于多源点云优化的城市三维模型快速构建技术,在充分利用现有数据基础上有效提升了数据处理效率,大幅减少了生产成本,并在实景三维山东建设过程中得到了有效验证,具有较好的社会经济效益与推广价值。

基于点云切片算法的铁路钢桁拱桥线形分析

铁路桥梁线形测量对于桥梁健康检测与确保铁路安全运营具有重要作用。为提高运营铁路钢桁拱桥线形测量效率,以某3跨钢桁拱桥为例,运用地面激光扫描(TLS)技术对桥梁杆件进行整体化扫描,从桥梁线形测量精度、扫描完整性、点云个数等3方面,分析出最佳的桥梁扫描测站数为10个。运用3DNDT点云配准算法将各测站一一配准,桥梁点云配准精度为2 mm,将桥梁点云投影至xoy平面,用半径滤波器进行噪声点的去除,得到完整“纯净”的桥梁点云模型。提出点云等距切片与点云平面切片算法提取桥梁线形,并将线形点云数据导出在AutoCAD中拾取坐标。将点云切片法提取的TLS测量值、全站仪法测量结果与原始成桥线形做比较分析,结果表明:在桥面线形分析中,两方法均在跨中处A5点测量出最大变形,分别为12.69 mm、10.29 mm,两方法的最大相互较差R为2.4 mm,相关系数优于99.93%;在拱轴线线形分析中,点云切片法与全站仪法在主桁上弦跨中B4点位测量的最大变形为6.2 mm、3.9 mm,在主桁下弦跨中B10点位测量的最大变形为5.9 mm、3.5 mm,两方法的最大互相较差R为3.2 mm,相关系数优于99.87%,验证了点云切片算法的有效性与TLS测量的高精度性。拱轴线横向线形未出现明显侧移,点云等距切片得到的19个吊杆垂直度保持良好,未发生扭转与偏移。该成果对于运营铁路钢桁拱桥的线形分析与点云处理方法可提供相应的思路和参考,具有重要的实用价值。

Numerical Simulation of External-Cavity Distributed Feedback Semiconductor Laser

We propose a theoretical model to describe external-cavity distributed feedback semiconductor lasers and investigate the impact of the number of external feedback points on linewidth and side-mode suppression ratio through numerical simulation. The simulation results demonstrate that the linewidth of external-cavity semiconductor lasers can be reduced by increasing the external cavity length and feedback ratio, and adding more external feedback points can further narrow the linewidth and enhance the side mode suppression ratio. This research provides insight into the external cavity distributed feedback mechanism and can guide the design of high-performance external cavity semiconductor lasers. .

基于三维激光点云的地铁隧道特征提取方法研究

针对地铁隧道的结构化特点,为实现轨道及月台平面的自动化巡查,本文提出一种基于几何约束的轨道及平面点云特征快速提取方法。对于隧道激光点云,通过PCA估计的方法确定其点云主轴方向并将其矫正至水平;在此基础上利用直通滤波提取隧道横截面点云,通过RANSAC拟合构建横截面数学模型;然后计算截面上各点到拟合圆心的距离,依据点心距的变化提取候选轨道点与平面点;根据两组截面的特征点优化求解直线与平面方程,计算点线、点面距离,完成直线、平面的特征提取。研究结果表明,轨道检测精度较好,检测准确率高。

地面激光扫描点云与无人机影像点云融合应用

通过建立高精度的桥梁三维点云模型,检查桥梁病害情况并拟合绘制出桥梁线形。首先以无人机近景摄影、环绕飞行、井字飞行获取某双线特大桥梁主体与细部纹理数据,然后将不同航线采集的数据在Context Capture软件里面进行三维重建,将桥梁主体与细部影像融合生成完整桥梁点云1。运用Trimble SX12仪器完成对桥梁一体化扫描,获得完整桥梁点云2。提出基于双向KD-tree优化的ICP(Iterative Closest Point)算法对无人机航摄桥梁点云1与地面激光扫描桥梁点云数据2进行配准融合,加密后的桥梁点云用于建立运营铁路双线特大桥精细化三维实景建模。提出基于KD-tree的PCA(Principal Component Analysis)算法完整提取出桥梁吊索点云,运用最小二乘法拟合出桥梁拱轴线线形、RANSAC算法拟合出桥面线形。通过与单一无人机、单一地面激光扫描精度及完整性对比分析,以验证融合建模的有效性。研究结果表明:融合建模的模型水平精度1.71 cm、垂直方向精度1.25 cm,较单一无人机建模精度在水平与竖直方向分别提升16.59%与20.89%;融合建模的完整性为98.17%,纹理效果更加真实,并检查出桥墩存在蜂窝麻面、渗水等病害,拱肋存在涂装锈蚀、破裂等病害。该研究可为桥梁三维点云模型应用研究提供思路参考,具有较好的应用前景。

Study on the LAI Single Tree Model Based on Terrestrial Laser Scanning

Leaf area index (LAI) is a key parameter for studying global terrestrial ecology and environment and has great ecological significance. How to accurately measure and calculate structural parameters of trees has become an urgent matter. This paper reports the use of terrestrial laser scanning (TLS) as a measurement tool to achieve accurate LAI estimation through point cloud preprocessing measures, the LeWos algorithm, and voxel methods. The accuracy and feasibility of this indirect measurement method were explored. It is found that the single wood structure parameters extracted from TLS have a good linear relationship with manual measurement, and the extraction errors meet the requirements of real-scene conversion. The study also found when the voxel size is consistent with the minimum distance of the point cloud set by TLS instrument, it has a strong correlation with the measured value of canopy analyser. These results lay the foundation for conveniently and quickly obtaining structural parameters of trees, tree growth state detection, and canopy ecological benefit assessment.