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.

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.

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。

基于改进PointPillars的点云车辆目标检测

针对自动驾驶领域中,激光雷达获取的点云数据存在稀疏性以及边缘噪点误检等问题,提出一种基于改进PointPil-lars的点云车辆目标检测方法。首先,基于SimAM注意力机制改进体素化特征输入,使得网络特征提取阶段能更加关注关键信息,提高特征学习的全局性。其次,基于卷积块注意力模块(CBAM)改进骨干网络结构,提出全新的轻量化通道注意力模块Tiny-CAM和可变形空间注意力模块Deformable-SAM,构建Multi-CBAM骨干网络,提升网络特征提取及特征融合能力。在KITTI数据集以及非公开车库点云数据集上进行验证,实验结果表明,与原网络相比,改进PointPillars方法具有更高的检测精度,平均检测精度提升2.98%,针对遮挡小于30%的点云车辆目标检测精度提升6.51%,证明了该方法的有效性。

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.

Pedestrian detection algorithm based on video sequences and laser point cloud

Pedestrian detection is a critical problem in the field of computer vision. Although most existing algorithms are able to detect pedestrians well in controlled environ- ments, it is often difficult to achieve accurate pedestrian de- tection from video sequences alone, especially in pedestrian- intensive scenes wherein pedestrians may cause mutual oc- clusion and thus incomplete detection. To surmount these dif- ficulties, this paper presents pedestrian detection algorithm based on video sequences and laser point cloud. First, laser point cloud is interpreted and classified to separate pedes- trian data and vehicle data. Then a fusion of video image data and laser point cloud data is achieved by calibration. The re- gion of interest after fusion is determined using feature in- formation contained in video image and three-dimensional information of laser point cloud to remove false detection of pedestrian and thus to achieve pedestrian detection in inten- sive scenes. Experimental verification and analysis in video sequences demonstrate that fusion of two data improves the performance of pedestrian detection and has better detection results.

Decoupling of the position and angular errors in laser pointing with a neural network method

In laser-pointing-related applications,when only the centroid of a laser spot is considered,then the position and angular errors of the laser beam are often coupled together.In this study,the decoupling of the position and angular errors is achieved from one single spot image by utilizing a neural network technique.In particular,the successful application of the neural network technique relies on novel experimental procedures,including using an appropriate small-focal-length lens and tilting the detector,to physically enlarge the contrast of different spots.This technique,with the corresponding new system design,may prove to be instructive in the future design of laser-pointing-related systems.

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。

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。

基于视觉图像与激光点云融合的交通标志快速识别方法

交通标志对车辆交通起到重要作用和意义,而智能交通中交通标志识别由于标志特征提取效果差,导致识别率低、识别时间长,因此,提出一种新的基于视觉图像与激光点云融合的交通标志快速识别方法。采用双边滤波方法预处理原始激光点云数据;通过归一化处理得到视觉图像激光点云融合的目标空间激光点云位置测距数值;通过测距值获取目标图像位置,归一化处理交通标志视觉图像,引入k均值聚类算法二聚类处理图像,采用制作的切割模板切割图像感兴趣区域,提取交通标志图像的深度特征,结合卷积神经网络二次过滤特征,重新标定二次过滤后的特征,最终利用卷积神经网络模型实现交通标志快速识别;经实验对比证明,采用所提方法提取各个类型交通标志特征的提取效果较好,并且识别率达到89.74%,识别时间仅为13.1 s,干扰下识别时间最高仅为15.1 s,验证了该方法可以快速且准确识别各个类型的交通标志。

基于三维激光点云的序列运动图像点目标快速跟踪方法

由于现有方法未充分考虑小目标和遮挡目标检测的问题,导致目标跟踪效果不佳,由此针对点目标跟踪较为困难的问题,提出基于三维激光点云的序列运动图像点目标快速跟踪方法。首先下采样序列运动图像三维激光点云并去除其中的地面数据,通过动态阈值改进欧式聚类方法,分割图像为目标和背景,然后改进SECOND算法,在目标检测阶段融入自适应空间特征融合模块和二维卷积神经网络,同时采用3D DIoU替代Smooth L1作为损失函数,提升SECOND算法的目标检测性能,最后在激光雷达坐标系中构建各点目标的三维中心,利用3D卡尔曼滤波器连续跟踪目标,以交并比和欧式距离为度量标准,通过贪婪算法匹配最近邻目标,实现序列运动图像点目标快速跟踪。实验结果表明,所提方法IoU值更高,可达到0.971,且对目标的跟踪更理想。

基于激光点云的电力杆塔倾斜角度计算方法

本文旨在提出一种基于无人机激光点云数据的电力杆塔倾斜角度自动精准计算方法,用于解决电力杆塔人工巡检验收环节中倾斜度计算效率低下、过程繁琐、难度较高的问题。本方法利用无人机激光雷达获取复杂环境下的电力杆塔点云,采用分层密度统计与塔身结构提取方法,生成电力杆塔中轴方向线,并计算其与杆塔所处位置铅垂线的夹角,得到电力杆塔倾斜角度。为验证本方法的有效性,选取了不同地点4个待验收的杆塔进行实验,通过模拟仿真与影响因素分析,探索了电力杆塔倾斜角度计算的精度。结果表明,本方法对电力杆塔倾斜角度计算的平均绝对误差为0.017 4?,验证了其在电力杆塔人工巡检验收环节中倾斜度计算的有效性与可靠性。

利用激光点云的智能网联汽车自主换道横向避障

通过智能网联汽车自主换道横向避障设计,提高车辆自主驾驶安全性能,提出基于激光点云的智能网联汽车自主换道横向避障方法,构建基于全激光雷达的无人驾驶系统的汽车行驶环境激光点云数据采集模型,通过激光雷达激光点云图像检测方法实现对智能网汽车行驶环境感知。结合障碍物检测、车道线检测、可行驶区域检测的方法,分析智能网联下汽车换道障碍物和车道的相关性特征量,通过激光点云的自主寻优方法寻找边界点直到搜索路径达到阈值,再结合直线道路模型和车道线连续性原则实现对汽车自主换道横向避障规划设计。仿真结果表明,采用该方法进行汽车自主环控避障,在5 s时达到收敛状态,障碍物坐标识别误差距离低于0.7,并且该方法的识别精度达到了0.970以上,提高了车道信息的感知能力,准确高效地估计可行驶区域,提高车辆避障能力。

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。

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。

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。