技术领域Technical Field
本申请涉及智能交通技术领域，尤其是涉及一种基于三维激光雷达数据识别服务区内车辆的方法。The present application relates to the field of intelligent transportation technology, and in particular to a method for identifying vehicles in a service area based on three-dimensional laser radar data.
背景技术Background Art
与服务区相比，其他车辆识别技术应用场景如停车场和自动驾驶在任务和技术要求上具有显著差异。停车场的车辆识别主要聚焦于进出管理与车位引导，重点在于车辆身份的验证与停车位的分配，对车辆位置精度要求较低，但对车辆身份识别的准确性要求较高。自动驾驶的车辆识别则侧重于行驶状态的实时监控，强调高精度的轨迹识别与即时反应，以确保车辆的自主决策和安全行驶。Compared with service areas, other vehicle identification technology application scenarios such as parking lots and autonomous driving have significant differences in tasks and technical requirements. Vehicle identification in parking lots mainly focuses on access management and parking guidance, with the emphasis on vehicle identity verification and parking space allocation. It has lower requirements for vehicle location accuracy, but higher requirements for vehicle identity recognition accuracy. Vehicle identification for autonomous driving focuses on real-time monitoring of driving status, emphasizing high-precision trajectory recognition and immediate response to ensure autonomous decision-making and safe driving of vehicles.
然而，服务区的车辆管理不仅涉及到车辆身份的识别，还需对车辆在复杂环境中的行为进行实时监控与分析。服务区内的车辆行驶状态更加复杂，包括减速、停车、启动等多种状态，并且车辆行驶路线并不固定，缺乏像自动驾驶场景中那样的规律性。此外，服务区内环境复杂，人员和设施众多，这对雷达技术提出了更高的抗干扰要求，尤其是在区分车辆与其他物体时，技术难度较大。传统的车辆监控技术依赖于有限的摄像头或人工监控，无法全面覆盖服务区内的各种动态情况，且在高流量、高密度的环境中效率低下。However, vehicle management in service areas not only involves the identification of vehicle identities, but also requires real-time monitoring and analysis of vehicle behavior in complex environments. The driving status of vehicles in service areas is more complicated, including deceleration, parking, starting and other states, and the vehicle driving routes are not fixed, lacking the regularity like in autonomous driving scenarios. In addition, the environment in the service area is complex, with a large number of people and facilities, which puts higher anti-interference requirements on radar technology, especially when distinguishing vehicles from other objects. The technical difficulty is relatively high. Traditional vehicle monitoring technology relies on limited cameras or manual monitoring, which cannot fully cover various dynamic situations in the service area, and is inefficient in high-traffic, high-density environments.
发明内容Summary of the invention
本申请的其中一个目的在于提供一种能够解决上述背景技术中至少一个缺陷的基于三维激光雷达数据识别服务区内车辆的方法。One of the objects of the present application is to provide a method for identifying vehicles in a service area based on three-dimensional lidar data, which can solve at least one of the defects in the above-mentioned background technology.
为达到上述的至少一个目的，本申请采用的技术方案为：一种基于三维激光雷达数据识别服务区内车辆的方法，包括如下步骤：In order to achieve at least one of the above purposes, the technical solution adopted by the present application is: a method for identifying vehicles in a service area based on three-dimensional laser radar data, comprising the following steps:
S100：采集服务区环境信息，构建包括服务区关键地面特征的有色底图线条模型；S100: Collect service area environmental information and construct a colored base map line model including key ground features of the service area;
S200：构建本地坐标系和世界坐标系；其中，本地坐标系用于进行车辆驾驶行为数据的汇总和分析，世界坐标系用于跨系统的数据整合和信息共享；S200: constructing a local coordinate system and a world coordinate system; wherein the local coordinate system is used to aggregate and analyze vehicle driving behavior data, and the world coordinate system is used for cross-system data integration and information sharing;
S300：对有色底图线条模型进行区域划分；基于车辆识别位置的优先级，在有色底图线条模型的安全区域位置进行激光雷达的布置，并根据逼近原则实现服务区全覆盖的情况下采用最少数量的激光雷达；S300: Divide the colored base map line model into regions; arrange laser radars in safe area positions of the colored base map line model based on the priority of the vehicle identification position, and use the minimum number of laser radars to achieve full coverage of the service area according to the approximation principle;
S400：基于布置的激光雷达对服务区内车辆进行分类和实时状态识别，并将采集的数据实时上传至服务器端。S400: Classify and identify the real-time status of vehicles in the service area based on the deployed laser radar, and upload the collected data to the server in real time.
优选的，步骤S100包括如下具体过程：Preferably, step S100 includes the following specific processes:
S110：在服务区内均匀布设多个十字靶标，采用GNSS设备测量十字靶标的三维坐标；S110: Multiple cross targets are evenly distributed in the service area, and the 3D coordinates of the cross targets are measured using GNSS equipment;
S120：使用无人机对服务区进行大范围多角度的拍摄，并根据采集的图像数据构建服务区的有色三维点云模型；S120: Use drones to take photos of the service area in a wide range and at multiple angles, and construct a colored 3D point cloud model of the service area based on the collected image data;
S130：对获得的三维点云模型进行去噪，使用去噪后的三维点云模型进行有色底图线条模型的构建。S130: De-noising the obtained three-dimensional point cloud model, and using the de-noised three-dimensional point cloud model to construct a colored base map line model.
优选的，在步骤S130中，对于三维点云模型的去噪包括如下具体过程：Preferably, in step S130, denoising the three-dimensional point cloud model includes the following specific processes:
S131：通过RANSAC算法对点云的地面进行判断；S131: judging the ground of the point cloud by using the RANSAC algorithm;
S132：对点云数据中各点至地面的高度进行判断，若点距离地面的高度在设定的第一范围内，认为该点属于低程点并进行保留，否则进行下一步骤；S132: judging the height of each point from the ground in the point cloud data, if the height of the point from the ground is within a set first range, the point is considered to be a low-range point and is retained, otherwise proceeding to the next step;
S133：判断该点距离地面的高度是否位于第二范围内，若位于则进行下一步骤，否则认定该点为噪声点并进行去除；S133: Determine whether the height of the point from the ground is within the second range, if so, proceed to the next step, otherwise, determine that the point is a noise point and remove it;
S134：以该点作为球心进行设定半径距离的范围搜索，若该球形搜索范围内存在其他点的数量大于设定的阈值数，认为该点为低程点并进行保留，否则为噪声点并进行去除。S134: Use the point as the center of the sphere to search within a range of a set radius. If the number of other points within the spherical search range is greater than the set threshold, the point is considered a low-level point and is retained; otherwise, it is considered a noise point and is removed.
优选的，在步骤S130中，对于有色底图线条模型的构建过程如下：Preferably, in step S130, the construction process of the colored base map line model is as follows:
S135：基于去噪后的点云数据，去除所有离地面超过第一范围的点；S135: Based on the denoised point cloud data, remove all points that are above a first range from the ground;
S136：将剩余的点云数据投影至地面，生成地面的有色底图模型；S136: Project the remaining point cloud data onto the ground to generate a colored base map model of the ground;
S137：通过计算机视觉技术从步骤S136生成的有色底图模型中提取出服务区内不同区域的空间布局和车道信息，得到服务区的有色底图线条模型。S137: Using computer vision technology, the spatial layout and lane information of different areas in the service area are extracted from the colored base map model generated in step S136 to obtain a colored base map line model of the service area.
优选的，在步骤S300中，对于有色底图线条模型的区域划分包括如下过程：Preferably, in step S300, the region division of the colored base map line model includes the following process:
S310：根据有色底图线条模型将服务区划分为三个区域，包括需要被识别区域、不可移动物体区域和可架设雷达区域；S310: Divide the service area into three areas according to the colored base map line model, including an area that needs to be identified, an area for immovable objects, and an area where radar can be set up;
S320：对整个服务区进行网格划分并对划分的网格进行横纵方向的编号；S320: Divide the entire service area into grids and number the divided grids in horizontal and vertical directions;
S330：根据划分的网格结合有色底图线条模型获取需要被识别区域的编号位置，以及每个网格四个角点的坐标。S330: Obtain the numbered positions of the areas to be identified and the coordinates of the four corner points of each grid according to the divided grids combined with the colored base map line model.
优选的，服务区出入口位置的车辆识别优先级大于其他位置；则在步骤S300中对于服务区出入口位置的激光雷达布置包括如下过程：Preferably, the vehicle identification priority at the entrance and exit of the service area is higher than that at other locations; then the laser radar arrangement at the entrance and exit of the service area in step S300 includes the following process:
S340：于划分的网格中定位服务区出入口位置的中点坐标(x1，y1)，以及服务区的中心点坐标(x2，y2)；S340: Locate the midpoint coordinates (x1 , y1 ) of the entrance and exit of the service area in the divided grid, and the center point coordinates (x2 , y2 ) of the service area;
S350：根据获得的坐标计算激光雷达的放置向量v=( x2-x1，y2-y1)；S350: Calculate the placement vector v=(x2 -x1 ,y2 -y1 ) of the laser radar according to the obtained coordinates;
S360：根据放置向量v的方向，确定雷达放置的候选区域；S360: Determine a candidate area for radar placement according to the direction of the placement vector v;
S370：遍历候选区域内的每个网格以进行激光雷达的识别范围计算，选择局部最优的网格并通过逼近原则确定激光雷达的精确布置位置。S370: Traverse each grid in the candidate area to calculate the recognition range of the laser radar, select the local optimal grid and determine the precise layout position of the laser radar through the approximation principle.
优选的，对于服务区除出入口位置的其他区域的激光雷达布置过程如下：Preferably, the laser radar arrangement process for other areas of the service area except the entrance and exit positions is as follows:
S381：确定服务区出入口位置激光雷达的覆盖范围边缘对应的网格，进而得到服务区未被完全识别的网格区域编号；S381: Determine the grid corresponding to the edge of the coverage range of the laser radar at the entrance and exit of the service area, and then obtain the grid area number that is not fully recognized in the service area;
S382：沿横向或纵向方向遍历每个空的网格区域并模拟激光雷达的放置；S382: traverse each empty grid area in the horizontal or vertical direction and simulate the placement of the laser radar;
S383：对激光雷达在每个放置位置所覆盖的空网格区域以及边缘位置进行记录；S383: Recording the empty grid area and edge position covered by the laser radar at each placement position;
S384：选择覆盖空网格区域最多，同时与服务区出入口位置激光雷达所占网格区域的交叉网格数最多的放置点作为最佳放置网格区域；S384: Selecting a placement point that covers the largest number of empty grid areas and has the largest number of intersecting grids with the grid area occupied by the laser radar at the entrance and exit of the service area as the optimal placement grid area;
S385：于最佳放置网格区域内通过逼近原则确定激光雷达精确布置位置。S385: Determine the precise placement position of the laser radar in the optimal placement grid area by using the approximation principle.
优选的，基于逼近原则确定激光雷达精确布置位置的过程如下：Preferably, the process of determining the precise placement position of the laser radar based on the approximation principle is as follows:
S301：在对应的局部最优网格中遍历全部可以放置激光雷达的布置区域；S301: traversing all layout areas where laser radars can be placed in the corresponding local optimal grid;
S302：对布置区域按照设定间隔距离划分多个放置点；S302: Divide the layout area into a plurality of placement points according to a set interval distance;
S303：对每个放置点进行激光雷达的假设放置并判断激光雷达的覆盖范围，选择其中具有最佳覆盖范围的放置点作为激光雷达的精确布置位置。S303: Perform a hypothetical placement of the laser radar for each placement point and determine the coverage range of the laser radar, and select the placement point with the best coverage range as the precise layout position of the laser radar.
优选的，步骤S400包括如下过程：Preferably, step S400 includes the following process:
S410：对激光雷达采集的三维点云进行目标标注，包括小轿车、箱式货车和大卡车三类目标，其他类型被标注为负样本；S410: Target annotation is performed on the 3D point cloud collected by the LiDAR, including three types of targets: cars, vans, and trucks. Other types are annotated as negative samples.
S420：于服务器端构建车辆识别模型并进行模型训练；S420: Building a vehicle recognition model on the server and performing model training;
S430：对激光雷达的坐标系进行标定，确保激光雷达采集的点云数据的坐标统一；S430: Calibrate the coordinate system of the laser radar to ensure that the coordinates of the point cloud data collected by the laser radar are unified;
S440：将激光雷达采集的点云数据上传至服务器端，进而结合训练好的车辆识别模型进行检测，根据检测的结果对车辆进行追踪和行为识别。S440: The point cloud data collected by the laser radar is uploaded to the server, and then detected in combination with the trained vehicle recognition model, and the vehicle is tracked and the behavior is recognized according to the detection results.
优选的，步骤S420中通过PointPillars模型进行模型训练的过程如下：Preferably, the process of model training using the PointPillars model in step S420 is as follows:
S421：将三维点云数据划分为多个柱状体；若单一柱状体内包含的点数超过阈值，则计算该柱状体的特征；若单一柱状体内点数不足阈值，则填充为零；S421: Divide the three-dimensional point cloud data into multiple cylinders; if the number of points contained in a single cylinder exceeds a threshold, calculate the features of the cylinder; if the number of points in a single cylinder is less than the threshold, fill it with zero;
S422：将每个柱状体内的点进行聚合，计算该柱状体的均值特征并压缩为固定长度的特征向量；将所有柱状体的特征向量进行拼接形成二维特征图；S422: Aggregate the points in each column, calculate the mean feature of the column and compress it into a feature vector of fixed length; splice the feature vectors of all columns to form a two-dimensional feature map;
S423：通过主干网络对二维特征图进行处理，逐步捕捉区域形状、纹理及不同区域间的空间关系，从而提取出二维特征图中的高级空间特征；S423: Processing the two-dimensional feature map through the backbone network, gradually capturing the regional shape, texture and spatial relationship between different regions, thereby extracting high-level spatial features in the two-dimensional feature map;
S424：通过Feature Pyramid Networks网络结构对提取的高级空间特征进行融合并传递至head模块，进而head模块对最终的检测结果进行生成，包括目标类别、位置及置信度。S424: The extracted high-level spatial features are fused through the Feature Pyramid Networks network structure and passed to the head module, and then the head module generates the final detection results, including target category, location and confidence.
与现有技术相比，本申请的有益效果在于：Compared with the prior art, the beneficial effects of this application are:
（1）本申请通过采用无人机摄影测量技术生成服务区点云。与传统人工测量或高精度地面激光扫描相比，不仅大幅度降低了设备投入和人工成本，还能迅速完成大范围区域的三维建模，尤其适用于大规模服务区的基础设施规划与管理。(1) This application generates service area point clouds by using drone photogrammetry technology. Compared with traditional manual measurement or high-precision ground laser scanning, it not only greatly reduces equipment investment and labor costs, but also can quickly complete three-dimensional modeling of large areas, which is particularly suitable for infrastructure planning and management of large-scale service areas.
（2）本申请根据服务区的实际情况与激光雷达的覆盖范围，能够智能优化激光雷达布设点，最大限度减少激光雷达的数量，在降低成本的同时，确保服务区各关键区域的激光雷达覆盖无死角。(2) This application can intelligently optimize the LiDAR deployment points based on the actual situation of the service area and the coverage of the LiDAR, minimize the number of LiDARs, and ensure that the LiDAR coverage of key areas in the service area is complete while reducing costs.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
图1为本申请的整体工作流程示意图。FIG1 is a schematic diagram of the overall workflow of this application.
图2为本申请对服务区有色底图线条模型进行网格划分的示意图。FIG2 is a schematic diagram of the grid division of the colored base map line model of the service area in this application.
图3为本申请中对服务区出入口位置布置激光雷达的示意图。FIG3 is a schematic diagram of the arrangement of laser radars at the entrance and exit of a service area in this application.
图4为本申请中服务区出入口位置激光雷达的覆盖范围示意图。FIG4 is a schematic diagram of the coverage range of the laser radar at the entrance and exit of the service area in this application.
图5为本申请完成激光雷达布置后的示意图。FIG5 is a schematic diagram of the laser radar arrangement after completion of the present application.
具体实施方式DETAILED DESCRIPTION
下面，结合具体实施方式，对本申请做进一步描述，需要说明的是，在本说明书的描述中，参考术语“一个实施例”、“一些实施例”、“示例”、“具体示例”、或“一些示例”等的描述意指结合该实施例或示例描述的具体特征、结构、材料或者特点包含于本发明的至少一个实施例或示例中。在本说明书中，对上述术语的示意性表述不应理解为必须针对的是相同的实施例或示例。而且，描述的具体特征、结构、材料或者特点可以在任何的一个或多个实施例或示例中以合适的方式结合。此外，本领域的技术人员可以将本说明书中描述的不同实施例或示例进行结合和组合。Below, the present application is further described in conjunction with specific implementation methods. It should be noted that in the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms should not be understood as necessarily being directed to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification.
在本申请的描述中，需要说明的是，对于方位词，如有术语“中心”、 “横向”、“纵向”、“长度”、“宽度”、“厚度”、“上”、“下”、 “前”、“后”、“左”、“右”、“竖直”、“水平”、“顶”、“底”、“内”、“外”、“顺时针”、“逆时针”等指示方位和位置关系为基于附图所示的方位或位置关系，仅是为了便于叙述本申请和简化描述，而不是指示或暗示所指的装置或元件必须具有特定的方位、以特定方位构造和操作，不能理解为限制本申请的具体保护范围。In the description of the present application, it should be noted that directional words, such as the terms "center", "lateral", "longitudinal", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", etc., indicating directions and positional relationships are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of narrating the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and cannot be understood as limiting the specific scope of protection of the present application.
需要说明的是，本申请的说明书和权利要求书中的术语“第一”、“第二”等是用于区别类似的对象，而不必用于描述特定的顺序或先后次序。It should be noted that the terms "first", "second", etc. in the description and claims of the present application are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.
在本申请中，除非另有明确的规定和限定，术语“安装”、“相连”、“连接”、“固定”等术语应做广义理解，例如，可以是连接，也可以是可拆卸连接，或成一体；可以是机械连接，也可以是电连接；可以是直接相连，也可以通过中间媒介间接相连，可以是两个元件内部的连通或两个元件的相互作用关系。对于本领域的普通技术人员而言，可以根据具体情况理解上述术语在本发明中的具体含义。In this application, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be connected, detachably connected, or integrated; it can be mechanically connected or electrically connected; it can be directly connected or indirectly connected through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.
在本申请中，除非另有明确的规定和限定，第一特征在第二特征之“上”或之“下”可以包括第一和第二特征直接接触，也可以包括第一和第二特征不是直接接触而是通过它们之间的另外的特征接触。而且，第一特征在第二特征“之上”、“上方”和“上面”包括第一特征在第二特征正上方和斜上方，或仅仅表示第一特征水平高度高于第二特征。第一特征在第二特征“之下”、“下方”和“下面”包括第一特征在第二特征正下方和斜下方，或仅仅表示第一特征水平高度小于第二特征。In the present application, unless otherwise clearly specified and limited, a first feature being "above" or "below" a second feature may include that the first and second features are in direct contact, or may include that the first and second features are not in direct contact but are in contact through another feature between them. Moreover, a first feature being "above", "above" and "above" a second feature includes that the first feature is directly above and obliquely above the second feature, or simply indicates that the first feature is higher in level than the second feature. A first feature being "below", "below" and "below" a second feature includes that the first feature is directly below and obliquely below the second feature, or simply indicates that the first feature is lower in level than the second feature.
本申请的说明书和权利要求书中的术语“包括”和“具有”以及它们的任何变形，意图在于覆盖不排他的包含，例如，包含了一系列步骤或单元的过程、方法、系统、产品或设备不必限于清楚地列出的那些步骤或单元，而是可包括没有清楚地列出的或对于这些过程、方法、产品或设备固有的其它步骤或单元。The terms "including" and "having" and any variations thereof in the specification and claims of the present application are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products or apparatuses.
本申请的其中一个优选的实施例，如图1所示，一种基于三维激光雷达数据识别服务区内车辆的方法，包括如下步骤：One of the preferred embodiments of the present application, as shown in FIG1 , is a method for identifying vehicles in a service area based on three-dimensional laser radar data, comprising the following steps:
S100：采集服务区环境信息，构建包括服务区关键地面特征的有色底图线条模型。S100: Collect service area environmental information and construct a colored base map line model including key ground features of the service area.
S200：构建本地坐标系和世界坐标系；其中，本地坐标系用于进行车辆驾驶行为数据的汇总和分析，世界坐标系用于跨系统的数据整合和信息共享。S200: constructing a local coordinate system and a world coordinate system; wherein the local coordinate system is used to aggregate and analyze vehicle driving behavior data, and the world coordinate system is used for cross-system data integration and information sharing.
S300：对有色底图线条模型进行区域划分；基于车辆识别位置的优先级，在有色底图线条模型的安全区域位置进行激光雷达的布置，并根据逼近原则实现服务区全覆盖的情况下采用最少数量的激光雷达。S300: Divide the colored base map line model into regions; arrange laser radars in safe area positions of the colored base map line model based on the priority of vehicle identification positions, and use the minimum number of laser radars to achieve full coverage of the service area according to the approximation principle.
S400：基于布置的激光雷达对服务区内车辆进行分类和实时状态识别，并将采集的数据实时上传至服务器端。S400: Classify and identify the real-time status of vehicles in the service area based on the deployed laser radar, and upload the collected data to the server in real time.
可以理解的是，为了更精准的对服务区内的车辆进行识别，需要对服务区内的环境信息进行充分的采集，同时为了方便后续激光雷达的布置，可以将服务区的地图信息通过底图的形式进行呈现，进而通过在底图上进行激光雷达布置方式的遍历，可以有效的提高激光雷达的布置效率。It is understandable that in order to more accurately identify vehicles in the service area, it is necessary to fully collect environmental information in the service area. At the same time, in order to facilitate the subsequent layout of lidar, the map information of the service area can be presented in the form of a base map. Then, by traversing the layout of lidar on the base map, the layout efficiency of lidar can be effectively improved.
具体的说，本申请设计的服务区底图涵盖了服务区的停车位、车道和禁停区域等关键信息。通过与后续的两套坐标系的结合，可以是对底图信息进行精确标定，进而为后续的车辆管理提供全面的空间数据支持。Specifically, the service area base map designed in this application covers key information such as parking spaces, lanes and no-parking areas in the service area. By combining with the subsequent two sets of coordinate systems, the base map information can be accurately calibrated, thereby providing comprehensive spatial data support for subsequent vehicle management.
对于坐标系的构建中，本地坐标系进行车辆驾驶行为数据的汇总和分析包括车流、车速和停车信息等数据，可以方便服务区内部的管理和运行。世界坐标系主要是为了实现激光雷达识别车辆与全球定位系统的融合，从而为跨系统的数据整合和信息共享提供支持。In the construction of the coordinate system, the local coordinate system summarizes and analyzes vehicle driving behavior data, including traffic flow, speed, parking information, etc., which can facilitate the management and operation of the service area. The world coordinate system is mainly used to achieve the integration of lidar vehicle recognition and global positioning system, so as to provide support for cross-system data integration and information sharing.
对于激光雷达的布置，由于激光雷达的造价相对高昂，为了降低成本，本申请设计一套即可以覆盖服务区全域，又能够减少激光雷达布控数量的方案。本方案可以在减少激光雷达设备数量的前提下，最大限度的实现对服务区的激光雷达全覆盖，从而有效降低设备成本并提高管理效率。As for the layout of LiDAR, since LiDAR is relatively expensive, in order to reduce the cost, this application designs a solution that can cover the entire service area and reduce the number of LiDAR deployments. This solution can achieve full LiDAR coverage of the service area to the maximum extent while reducing the number of LiDAR devices, thereby effectively reducing equipment costs and improving management efficiency.
对于服务区内的车辆识别，本申请可以基于激光雷达的点云数据对车辆进行识别以及行为分析。即通过先进的算法对服务区内的车辆进行分类，包括小轿车、箱式卡车和大卡车等，并实现对每辆车的实时追踪和行为判断。通过对车辆超速、违规停放、逆行等行为的自动检测，系统可实时上传行为数据至服务器端，供服务区管理人员进行快速反应与决策，从而提高服务区的安全性和管理效率。For vehicle identification in the service area, this application can identify vehicles and analyze their behavior based on the point cloud data of the LiDAR. That is, through advanced algorithms, vehicles in the service area are classified, including cars, box trucks, and large trucks, and real-time tracking and behavior judgment of each vehicle are achieved. Through automatic detection of vehicle speeding, illegal parking, and reverse driving, the system can upload behavior data to the server in real time for service area managers to respond and make decisions quickly, thereby improving the safety and management efficiency of the service area.
为了方便理解，下面将对各步骤进行详细的描述。For ease of understanding, each step will be described in detail below.
本实施例中，步骤S100包括如下具体过程：In this embodiment, step S100 includes the following specific processes:
S110：在服务区内均匀布设多个十字靶标，采用GNSS设备测量十字靶标的三维坐标。S110: Multiple cross targets are evenly distributed in the service area, and the three-dimensional coordinates of the cross targets are measured using GNSS equipment.
具体的，十字靶标的数量可以根据实际需要自行进行设置，一般可以设置5-10个十字靶标。十字靶标可以将标记区域分为四个扇形区域，相邻两个扇形部分的颜色进行不同设置，例如分别为黑色和白色。这些十字靶标朝向天空以便于在测量过程中无人机能够精准清晰捕捉到这些十字靶标的位置。十字靶标的选择应当确保覆盖整个服务区，以保证标定精度。在完成十字靶标的布置后，可以使用GNSS设备，如北斗定位系统、GPS和Galileo等，精测测量单个十字靶标的三维坐标。Specifically, the number of cross targets can be set according to actual needs, and generally 5-10 cross targets can be set. The cross target can divide the marking area into four sector areas, and the colors of the two adjacent sector parts are set differently, for example, black and white respectively. These cross targets are facing the sky so that the drone can accurately and clearly capture the positions of these cross targets during the measurement process. The selection of cross targets should ensure that the entire service area is covered to ensure calibration accuracy. After completing the arrangement of the cross targets, GNSS equipment, such as Beidou Positioning System, GPS and Galileo, can be used to accurately measure the three-dimensional coordinates of a single cross target.
S120：使用无人机对服务区进行大范围多角度的拍摄，并根据采集的图像数据构建服务区的有色三维点云模型。S120: Use drones to take photos of the service area over a wide range and from multiple angles, and construct a colored 3D point cloud model of the service area based on the collected image data.
具体的，无人机的图像数据采集需要保证有足够的图像数据，以便于后续的三维重建。对于有色三维点云模型的构建，优选采用结构从运动算法（SfM），通过使用结构从运动算法（SfM）推算出无人机携带的相机位置和朝向，以及场景中物体的三维坐标。SfM算法不仅需要识别图片中的特征点，还能利用相机之间的相对位置关系对场景进行三维重建。该算法的具体步骤如下：Specifically, the image data collection of the drone needs to ensure that there is enough image data to facilitate subsequent 3D reconstruction. For the construction of colored 3D point cloud models, the structure from motion algorithm (SfM) is preferably used. The position and orientation of the camera carried by the drone and the 3D coordinates of the objects in the scene are inferred by using the structure from motion algorithm (SfM). The SfM algorithm not only needs to identify the feature points in the image, but also can use the relative position relationship between the cameras to reconstruct the scene in 3D. The specific steps of the algorithm are as follows:
从每一张图像中提取中关键特征点匹配不同图像之间的特征点。根据已知的特征点匹配结果，通过基础矩阵或本质矩阵来估算相机之间的相对位置和姿态，这个过程本质上是解一个线性系统，从而确定每个相机的内外参。利用之前求得的相机位置和匹配的二维特征点，算法通过共线性方程求解空间点。共线性方程是摄影测量中的关键数学工具，描述了图像空间中的点，相机坐标系和物理坐标系中点之间的几何关系。共线性方程通过建立图像坐标和物体坐标系之间的关系，允许从二维图像坐标中反推物体在三维空间中的位置；共线性方程形式如下：Extract key feature points from each image and match the feature points between different images. Based on the known feature point matching results, the relative position and posture between the cameras are estimated through the basic matrix or essential matrix. This process is essentially to solve a linear system to determine the internal and external parameters of each camera. Using the previously obtained camera position and matched two-dimensional feature points, the algorithm solves the spatial points through collinearity equations. Collinearity equations are key mathematical tools in photogrammetry, describing the geometric relationship between points in image space, camera coordinate systems, and physical coordinate systems. Collinearity equations allow the position of an object in three-dimensional space to be inferred from two-dimensional image coordinates by establishing a relationship between image coordinates and object coordinate systems; the collinearity equation is in the following form:
。 .
。 .
其中，(x，y)表示图像坐标，(x0，y0)表示主点坐标，c表示相机的焦距，(X，Y，Z)表示三维空间中的物体坐标，(X0，Y0，Z0) 表示相机投影中心在物方坐标系中的坐标，Rij表示相机坐标系到物方坐标系的旋转变换。Among them, (x, y) represents the image coordinates, (x0 ,y0 ) represents the principal point coordinates, c represents the focal length of the camera, (X, Y, Z) represents the object coordinates in three-dimensional space, (X0 ,Y0 ,Z0 ) represents the coordinates of the camera projection center in the object coordinate system, andRij represents the rotation transformation from the camera coordinate system to the object coordinate system.
在初步完成三维重建后，将通过束调整（Bundle Adjustment）进一步优化。束调整是通过最小化图像特征点在各视角下的重投影误差，来优化所有相机的参数和三维点的坐标。通过最小二乘法求解，调整相机和三维点的位置，直到误差达到最小。After the initial 3D reconstruction is completed, it will be further optimized through bundle adjustment. Bundle adjustment is to optimize the parameters of all cameras and the coordinates of 3D points by minimizing the reprojection error of image feature points at each viewing angle. The least squares method is used to adjust the positions of the cameras and 3D points until the error is minimized.
上述三维重建并优化后得到的三维点云模型依旧是稀疏模型。在稀疏重建的基础上，使用多视图立体匹配（Multi-View Stereo，MVS）技术生成稠密点云。稠密点云通过增加更多的三维点来更精细的表示场景的几何结构。通过以上步骤，可以生成稠密的服务区的有色三维点云模型。通过点云中的十字靶标坐标与预先测量好的十字靶标坐标，通过四参数转换公式，可以将点云转换到世界坐标系下；具体的转换过程为本领域技术人员公知，故不在此进行详细的阐述。The 3D point cloud model obtained after the above 3D reconstruction and optimization is still a sparse model. Based on the sparse reconstruction, the Multi-View Stereo (MVS) technology is used to generate a dense point cloud. The dense point cloud represents the geometric structure of the scene more finely by adding more 3D points. Through the above steps, a dense colored 3D point cloud model of the service area can be generated. The point cloud can be converted to the world coordinate system through the four-parameter conversion formula by comparing the cross target coordinates in the point cloud with the pre-measured cross target coordinates; the specific conversion process is well known to those skilled in the art, so it will not be elaborated here.
S130：对获得的三维点云模型进行去噪，使用去噪后的三维点云模型进行有色底图线条模型的构建。S130: De-noising the obtained three-dimensional point cloud model, and using the de-noised three-dimensional point cloud model to construct a colored base map line model.
可以理解的是，通过前述步骤获得的有色三维点云中可能存在大量的噪点，所以需要对点云进行去噪操作。具体的，对于三维点云模型的去噪包括如下具体过程：It is understandable that there may be a large number of noise points in the colored 3D point cloud obtained through the above steps, so it is necessary to perform denoising operation on the point cloud. Specifically, the denoising of the 3D point cloud model includes the following specific processes:
S131：通过RANSAC算法对点云的地面进行判断。S131: Determine the ground of the point cloud using the RANSAC algorithm.
需要注意的是，为了方便进行后续的步骤，可以先对点云空间进行分块，将点云数据在水平xy平面划分为多个网格区域，单个网格区域的大小可以根据实际需要自行进行设置，例如单个网格尺寸为3m×3m。每个网格区域都包含多个点，且每个网格的z值为该网格区域内所有点的高度信息。It should be noted that in order to facilitate the subsequent steps, the point cloud space can be divided into multiple grid areas in the horizontal xy plane. The size of a single grid area can be set according to actual needs. For example, the size of a single grid is 3m×3m. Each grid area contains multiple points, and the z value of each grid is the height information of all points in the grid area.
S132：对点云数据中各点至地面的高度进行判断，若点距离地面的高度在设定的第一范围内，认为该点属于低程点并进行保留，否则进行下一步骤。S132: Determine the height from each point in the point cloud data to the ground. If the height from the point to the ground is within a set first range, the point is considered to be a low-range point and is retained. Otherwise, proceed to the next step.
可以理解的是，第一范围的具体取值可以根据实际需要自行进行设定；一般来说，考虑到服务区内道路的坡度变化，可以优选将第一范围设定为±0.5m。It is understandable that the specific value of the first range can be set according to actual needs; generally speaking, considering the slope change of the road in the service area, it is preferred to set the first range to ±0.5m.
S133：判断该点距离地面的高度是否位于第二范围内，若位于则进行下一步骤，否则认定该点为噪声点并进行去除。S133: Determine whether the height of the point from the ground is within the second range, if so, proceed to the next step, otherwise, determine that the point is a noise point and remove it.
可以理解的是，第二范围的具体取值可以根据实际需要自行进行设定，考虑到一般车辆的高度都在3m以下，即3m以上的物体对车辆的正常行驶不会产生干涉；因此对于第二范围的取值优选为3m，即高度超过3m的点都看作是噪点进行删除。It can be understood that the specific value of the second range can be set according to actual needs, taking into account that the height of general vehicles is below 3m, that is, objects above 3m will not interfere with the normal driving of the vehicle; therefore, the value of the second range is preferably 3m, that is, points with a height exceeding 3m are regarded as noise points and deleted.
S134：对于高度超过第一范围但位于第二范围内的点，以该点作为球心进行设定半径距离的范围搜索，若该球形搜索范围内存在其他点的数量大于设定的阈值数，认为该点为低程点并进行保留，否则为噪声点并进行去除。S134: For a point whose height exceeds the first range but is within the second range, a range search with a set radius distance is performed with the point as the center of the sphere. If the number of other points within the spherical search range is greater than the set threshold, the point is considered to be a low-range point and is retained; otherwise, it is considered to be a noise point and is removed.
可以理解的是，对于高度超过第一范围但位于第二范围内的点，其可能属于建筑物或者路灯上的点，这些点在理论上属于建筑物或路灯的一部分，不能简单的当作噪点进行判断。即通过步骤S134保留建筑物和路灯上的高点，剔除掉高程的噪点；可以有效去除停车区高空部分的噪声点，从而得到更精准的点云模型，确保高程方向上的空旷区域不受噪声点影响。对于球形搜索范围半径的具体取值可以根据实际需要自行进行选择，例如可以取半径为0.5m。It is understandable that points whose height exceeds the first range but are within the second range may belong to points on buildings or street lamps. These points are theoretically part of buildings or street lamps and cannot be simply judged as noise points. That is, through step S134, the high points on buildings and street lamps are retained, and the noise points in the elevation are eliminated; the noise points in the high part of the parking area can be effectively removed, thereby obtaining a more accurate point cloud model, ensuring that the open area in the elevation direction is not affected by noise points. The specific value of the radius of the spherical search range can be selected according to actual needs, for example, the radius can be taken as 0.5m.
本实施例中，在完成点云数据的去噪后进行有色底图线条模型的构建包括如下过程：In this embodiment, after the point cloud data is denoised, the construction of the colored base map line model includes the following process:
S135：基于去噪后的点云数据，去除所有离地面超过第一范围的点。S135: Based on the denoised point cloud data, remove all points that are above a first range from the ground.
可以理解的是，有色底图线条模型主要是对服务区的地面信息进行记录，而在前述去噪后的点云数据中还包含了部分非地面物体的点云。例如一个半包围结构的遮雨棚等，雨棚下方并不影响通车，若在后一步骤中将这些点全部投影到地面，可能在提取数据的过程中会被当作地面结构点，从而导致后续底图建立的不准确。It is understandable that the colored base map line model mainly records the ground information of the service area, and the denoised point cloud data also contains some point clouds of non-ground objects. For example, a semi-enclosed awning, etc., does not affect traffic below the awning. If all these points are projected to the ground in the next step, they may be regarded as ground structure points during data extraction, resulting in inaccurate subsequent base map establishment.
S136：将剩余的点云数据投影至地面，生成地面的有色底图模型。S136: Project the remaining point cloud data onto the ground to generate a colored base map model of the ground.
应当知道的是，生成的有色底图线条模型代表了服务区的地面信息，包括停车线、斑马线和车辆标记等关键地面特征。It should be noted that the generated colored basemap line model represents the ground information of the service area, including key ground features such as parking lines, zebra crossings and vehicle markings.
S137：通过计算机视觉技术从步骤S136生成的有色底图模型中提取出服务区内不同区域的空间布局和车道信息，得到服务区的有色底图线条模型。S137: Using computer vision technology, the spatial layout and lane information of different areas in the service area are extracted from the colored base map model generated in step S136 to obtain a colored base map line model of the service area.
可以理解的是，步骤S137采用的计算机视觉技术可以有多种，如边缘检测方法和阈值分割方法等，具体的工作原理为本领域技术人员所公知，故不在此进行详细的阐述。通过计算机视觉技术可以从有色底图线条模型中提取车道线和停车线等线条特征，这些特征代表了服务区内不同区域的空间布局和车道信息。通过提取的车道线和其他标记，生成一个包含各部分位置、大小和几何形态的JSON文件。该文件包含车道线、停车位、禁停区域等要素的空间信息，并为未来的车道线提取与车辆位置识别提供数据支持。It is understandable that there may be multiple computer vision technologies used in step S137, such as edge detection methods and threshold segmentation methods. The specific working principles are well known to those skilled in the art, so they will not be elaborated on in detail here. Computer vision technology can be used to extract line features such as lane lines and parking lines from the colored base map line model. These features represent the spatial layout and lane information of different areas in the service area. A JSON file containing the position, size and geometry of each part is generated through the extracted lane lines and other markings. The file contains spatial information of elements such as lane lines, parking spaces, and no-parking areas, and provides data support for future lane line extraction and vehicle position recognition.
基于上述信息，生成一个准确的服务区有色底图线条模型。该模型通过三维坐标系与车道线等空间数据的融合，提供精确的地面布局信息，用于后续车辆识别与位置分析。利用该底图线条模型，结合未来的车辆位置识别技术，实现对服务区内车辆的智能管理与监控。此技术可为车道线管理、停车位利用、车流监控等提供精确的基础数据支持。Based on the above information, an accurate colored base map line model of the service area is generated. This model provides accurate ground layout information for subsequent vehicle identification and location analysis by integrating the three-dimensional coordinate system with spatial data such as lane lines. Using this base map line model, combined with future vehicle location recognition technology, intelligent management and monitoring of vehicles in the service area can be achieved. This technology can provide accurate basic data support for lane line management, parking space utilization, and traffic flow monitoring.
应当知道的是，本申请通过采用无人机摄影测量技术来进行服务区点云的生成，从而可以快速且低成本构建服务区的底图。相比较传统人工测量和高精度地面激光扫描的方式，本申请不仅大幅度降低了设备投入和人工成本，还能迅速完成大范围区域的三维建模，尤其适用于大规模服务区的基础设施规划与管理。通过本申请的技术方案，能够为服务区提供精确的底图，并保证后续基于三维模型的各类技术应用具有高质量的数据支持。It should be known that this application uses drone photogrammetry technology to generate service area point clouds, so that the base map of the service area can be constructed quickly and at low cost. Compared with traditional manual measurement and high-precision ground laser scanning methods, this application not only greatly reduces equipment investment and labor costs, but also can quickly complete three-dimensional modeling of large areas, which is especially suitable for infrastructure planning and management of large-scale service areas. Through the technical solution of this application, it is possible to provide an accurate base map for the service area and ensure that various subsequent technical applications based on three-dimensional models have high-quality data support.
本实施例中，为了实现精准的车辆监测和信息管理，步骤S200中设计了两套坐标系，分别为服务区的本地坐标系和世界坐标系；本地坐标系和世界坐标系的具体构建过程为本领域技术人员的公知技术；为了方便理解，下面将对两种坐标系进行简单的描述。In this embodiment, in order to achieve accurate vehicle monitoring and information management, two sets of coordinate systems are designed in step S200, namely the local coordinate system of the service area and the world coordinate system; the specific construction process of the local coordinate system and the world coordinate system is a well-known technology for technical personnel in this field; for the sake of ease of understanding, the two coordinate systems will be briefly described below.
一、世界坐标系。1. World coordinate system.
世界坐标系主要用于将服务区内的各个部分、以及识别到的车辆在全球范围内进行信息收集和融合。通过世界坐标系，可以将服务区内的各类信息与高速公路交通流数据进行整合。例如，通过世界坐标系可以实时监控服务区内车辆的位置、轨迹，并与外部系统（如交通控制中心）进行数据交换。The world coordinate system is mainly used to collect and integrate information about various parts of the service area and identified vehicles on a global scale. Through the world coordinate system, various types of information in the service area can be integrated with highway traffic flow data. For example, the world coordinate system can monitor the position and trajectory of vehicles in the service area in real time and exchange data with external systems (such as traffic control centers).
世界坐标系的构建依赖于全球导航卫星系统（GNSS）以及在服务区内布设的十字靶标。在服务区内均匀布设5-10个十字靶标，这些十字靶标应具有明确的全球定位信息，朝向天空并能被无人机或其他测量设备准确捕捉。通过在三维模型中标定这些十字靶标的位置，结合GNSS提供的坐标信息，可以将服务区的底图和三维点云模型转换至世界坐标系，实现精准的全球定位和信息融合。The construction of the world coordinate system relies on the global navigation satellite system (GNSS) and cross targets deployed in the service area. 5-10 cross targets are evenly deployed in the service area. These cross targets should have clear global positioning information, face the sky and be accurately captured by drones or other measuring equipment. By calibrating the positions of these cross targets in the 3D model and combining the coordinate information provided by GNSS, the base map and 3D point cloud model of the service area can be converted to the world coordinate system to achieve accurate global positioning and information fusion.
二、本地坐标系。2. Local coordinate system.
本地坐标系主要用于服务区内的车辆监测、异常检测和行为分析。相较于世界坐标系，本地坐标系以服务区的具体场景为基础，能够更加直观和简便的表示服务区内物体的位置和状态。通过本地坐标系，可以高效的进行车辆的进出识别、停车状态判断以及其他事件的实时监控。本地坐标系的使用使得系统能够在局部范围内实现快速响应，提高处理速度和精度。The local coordinate system is mainly used for vehicle monitoring, anomaly detection and behavior analysis in the service area. Compared with the world coordinate system, the local coordinate system is based on the specific scene of the service area and can more intuitively and simply represent the position and status of objects in the service area. Through the local coordinate system, vehicle entry and exit recognition, parking status judgment and other events can be efficiently monitored in real time. The use of the local coordinate system enables the system to achieve rapid response in a local area, improving processing speed and accuracy.
本地坐标系的建立则基于世界坐标系的转换原理。在获取了世界坐标系的坐标数据后，通过对服务区范围内的坐标进行去中心化处理，可以得到本地坐标系。具体而言，通过将服务区内所有关键物体（如停车位、车道线、交通标识等）的世界坐标系数据转换为相对坐标，即可构建本地坐标系。这一过程可以通过设定参考点或固定基准点来简化，并确保后续在本地坐标系下的数据处理与管理可以顺利进行。The establishment of the local coordinate system is based on the conversion principle of the world coordinate system. After obtaining the coordinate data of the world coordinate system, the local coordinate system can be obtained by decentralizing the coordinates within the service area. Specifically, the local coordinate system can be constructed by converting the world coordinate system data of all key objects in the service area (such as parking spaces, lane lines, traffic signs, etc.) into relative coordinates. This process can be simplified by setting reference points or fixed reference points, and ensure that subsequent data processing and management in the local coordinate system can proceed smoothly.
在后续的车辆监测和行为识别过程中，系统会使用本地坐标系对数据进行处理和判断。为了实现不同场景之间的无缝转换，本地坐标系与世界坐标系之间会提供接口，支持实时转换和数据更新。具体而言，当需要将本地坐标系下的识别结果与全球范围的交通数据进行关联时，系统将通过预设的转换算法将本地坐标系的坐标转换为世界坐标系，以便进行高效的数据共享与信息整合。通过两套坐标系的结合，使得本申请在提供本地管理与控制的同时，也能方便与更大范围的交通系统、物联网平台以及高速公路管理系统进行联动。通过使用世界坐标系，服务区的数据可以实现与周围交通流的无缝对接。In the subsequent vehicle monitoring and behavior recognition process, the system will use the local coordinate system to process and judge the data. In order to achieve seamless conversion between different scenes, an interface will be provided between the local coordinate system and the world coordinate system to support real-time conversion and data update. Specifically, when it is necessary to associate the recognition results in the local coordinate system with global traffic data, the system will convert the coordinates of the local coordinate system into the world coordinate system through a preset conversion algorithm to facilitate efficient data sharing and information integration. Through the combination of the two sets of coordinate systems, the present application can provide local management and control while also being convenient for linkage with a wider range of transportation systems, Internet of Things platforms, and highway management systems. By using the world coordinate system, the data in the service area can be seamlessly connected with the surrounding traffic flow.
本实施例中，在进行步骤S300时，基于使用最少数量的激光雷达对服务区全域进行覆盖的原则，在进行激光雷达的布设时首先需要确认每个激光雷达的架设高度。激光雷达的架设高度需要满足以下条件：满足对所有类型的车辆的识别，确保车辆在激光雷达斜下方通过时的识别有效性，即对于靠近激光雷达的车辆，激光雷达仍应能够正确识别其存在。In this embodiment, when performing step S300, based on the principle of using the least number of laser radars to cover the entire service area, it is necessary to first confirm the installation height of each laser radar when deploying the laser radar. The installation height of the laser radar needs to meet the following conditions: meet the recognition of all types of vehicles, ensure the effectiveness of recognition when the vehicle passes obliquely below the laser radar, that is, for vehicles close to the laser radar, the laser radar should still be able to correctly identify its existence.
应当知道的是，车辆的高度一般不得超过4.2m，车辆的长度一般不会超过18m。根据激光雷达的工作原理，激光雷达的扫描角度和有效扫描距离对识别效果至关重要。为了避免纵向夹角过小导致识别失败，通常要求激光雷达的扫描角度大于30度。由此，根据三角学原理，可以推导出激光雷达的最小架设高度为：9×sin30°+4.2=8.7m。为了留有冗余空间，本实施例中每个激光雷达的架设高度优选为9米。It should be known that the height of the vehicle shall generally not exceed 4.2m, and the length of the vehicle shall generally not exceed 18m. According to the working principle of LiDAR, the scanning angle and effective scanning distance of LiDAR are crucial to the recognition effect. In order to avoid recognition failure due to too small longitudinal angle, the scanning angle of LiDAR is usually required to be greater than 30 degrees. Therefore, according to the principles of trigonometry, it can be deduced that the minimum installation height of LiDAR is: 9×sin30°+4.2=8.7m. In order to leave redundant space, the installation height of each LiDAR in this embodiment is preferably 9 meters.
为了方便理解，下面可以基于服务区的简化结构对激光雷达的局部布置过程进行详细的描述。如图2所示，图中黑色阴影部分区域代表了适合架设激光雷达的位置，阴影区域不仅包括车道和停车区之间的交汇处，还特别标注了停车区上方的部分区域。需要注意的是，这些阴影仅为示意性标注，用以展示哪些区域适合布设雷达。实际布设时，应考虑激光雷达布控点的具体位置，这些区域通常位于车位之间的交点和车道交汇处，激光雷达在这些关键位置上能有效监测到车辆。通过图2可以直观的看到哪些部分适合放置激光雷达，图中所示的阴影区域覆盖了服务区的关键地带，包括车道、停车位及出入口区域。激光雷达的布设应集中在这些交汇区域，以确保不遗漏任何重要的车辆流动信息和状态监测。For ease of understanding, the following is a detailed description of the local layout process of the LiDAR based on the simplified structure of the service area. As shown in Figure 2, the black shaded area in the figure represents the location suitable for setting up the LiDAR. The shaded area not only includes the intersection between the lane and the parking area, but also specially marks some areas above the parking area. It should be noted that these shadows are only schematic annotations to show which areas are suitable for laying radars. When actually laying out, the specific location of the LiDAR control points should be considered. These areas are usually located at the intersection between parking spaces and the intersection of lanes. The LiDAR can effectively monitor vehicles at these key locations. Figure 2 shows which parts are suitable for placing LiDARs. The shaded area shown in the figure covers the key areas of the service area, including lanes, parking spaces, and entrance and exit areas. The layout of LiDARs should be concentrated in these intersection areas to ensure that no important vehicle flow information and status monitoring are missed.
具体的，在步骤S300中，对于有色底图线条模型的区域划分包括如下过程：Specifically, in step S300, the region division of the colored base map line model includes the following process:
S310：根据有色底图线条模型将服务区划分为三个区域，包括需要被识别区域、不可移动物体区域和可架设雷达区域。S310: Divide the service area into three areas according to the colored base map line model, including the area that needs to be identified, the area for immovable objects, and the area where radar can be set up.
可以理解的是，需要被识别区域包括行车道、停车区车位、加油站区域和充电区域等，此区域需要确保激光雷达能够全覆盖，并且不得安装激光雷达设备。不可移动物体区域包括服务器的建筑物、立柱和标志牌等区域，这些区域不适合放置雷达。可架设雷达区域包括绿化带、路侧区域、停车区与道路中间部分以及多个停车位交点等区域。It is understandable that the areas that need to be identified include lanes, parking spaces, gas station areas, and charging areas. This area needs to ensure that the lidar can be fully covered, and lidar equipment must not be installed. The immovable object area includes areas such as server buildings, columns, and signboards, which are not suitable for placing radars. Areas where radars can be set up include green belts, roadside areas, parking areas and the middle part of the road, and intersections of multiple parking spaces.
S320：对整个服务区进行网格划分并对划分的网格进行横纵方向的编号。S320: Divide the entire service area into grids and number the divided grids in horizontal and vertical directions.
可以理解的是，为了方便后续对激光雷达布设的方向进行计算，可以先对服务区进行空间网格划分，每个网格的尺寸可以根据实际需要自行进行设置，例如每个网格的尺寸为15m×15m。为了确保有效识别车辆，并且最大限度地减少雷达设备数量，需要对服务区的网格进行编号，为了方便区分，横纵方向可以采用不同类型的编号，例如横向编号使用字母，纵向编号使用阿拉伯数字。例如图2所示，假设服务区的尺寸为225m×120m，那么服务区可以被划分为15×8个网格区域，横向编号从A至O，纵向编号从1至8。It is understandable that in order to facilitate the subsequent calculation of the direction of the LiDAR deployment, the service area can be divided into spatial grids first, and the size of each grid can be set according to actual needs, for example, the size of each grid is 15m×15m. In order to ensure effective identification of vehicles and minimize the number of radar equipment, the grids in the service area need to be numbered. For easy distinction, different types of numbers can be used in the horizontal and vertical directions, such as letters for horizontal numbering and Arabic numerals for vertical numbering. For example, as shown in Figure 2, assuming that the size of the service area is 225m×120m, the service area can be divided into 15×8 grid areas, with horizontal numbers from A to O and vertical numbers from 1 to 8.
S330：根据划分的网格结合有色底图线条模型获取需要被识别区域的编号位置，以及每个网格四个角点的坐标。S330: Obtain the numbered positions of the areas to be identified and the coordinates of the four corner points of each grid according to the divided grids combined with the colored base map line model.
可以理解的是，在完成网格划分后，需要进一步根据底图和网格位置来判断哪个网格区域是需要被识别的。例如图2所示，第一行的网格都不需要被识别，相应的D2至M2等网格区域也不需要识别。在进行网格区域是否被需要识别的同时，还需要获取每个网格四个角点的坐标，通过这些坐标可以判断激光雷达覆盖的点是否位于该网格内。It is understandable that after completing the grid division, it is necessary to further determine which grid area needs to be identified based on the base map and the grid position. For example, as shown in Figure 2, the grids in the first row do not need to be identified, and the corresponding grid areas such as D2 to M2 do not need to be identified. While determining whether the grid area needs to be identified, it is also necessary to obtain the coordinates of the four corner points of each grid. These coordinates can be used to determine whether the point covered by the lidar is located within the grid.
本实施例中，服务区出入口位置的车辆识别优先级大于其他位置，即激光雷达需要首先确保服务区出入口的车流信息，以及车辆从入口进入后的行驶方式以及驶出出口前的进入方向等动态信息，这样才能保证车辆在服务区内行驶时的识别准确。In this embodiment, the vehicle identification priority at the entrance and exit of the service area is higher than that at other locations, that is, the laser radar needs to first ensure the traffic information at the entrance and exit of the service area, as well as dynamic information such as the driving mode of the vehicle after entering from the entrance and the entry direction before exiting the exit, so as to ensure accurate identification of the vehicle when driving in the service area.
具体的，如图2和图3所示，在步骤S300中对于服务区出入口位置的激光雷达布置包括如下过程：Specifically, as shown in FIG. 2 and FIG. 3 , the laser radar arrangement at the entrance and exit of the service area in step S300 includes the following process:
S340：于划分的网格中定位服务区出入口位置的中点坐标(x1，y1)，以及服务区的中心点坐标(x2，y2)。S340: Locate the midpoint coordinates (x1 , y1 ) of the entrance and exit of the service area in the divided grid, and the center point coordinates (x2 , y2 ) of the service area.
S350：根据获得的坐标计算激光雷达的放置向量v=( x2-x1，y2-y1)。S350: Calculate the placement vector v=(x2 -x1 ,y2 -y1 ) of the laser radar according to the obtained coordinates.
S360：根据放置向量v的方向，确定雷达放置的候选区域。S360: Determine a candidate area for radar placement according to the direction of the placement vector v.
S370：遍历候选区域内的每个网格以进行激光雷达的识别范围计算，选择局部最优的网格并通过逼近原则确定激光雷达的精确布置位置。S370: Traverse each grid in the candidate area to calculate the recognition range of the laser radar, select the local optimal grid and determine the precise layout position of the laser radar through the approximation principle.
为了方便理解，下面将以服务区入口处的激光雷达布置方式结合图2进行详细的描述。For ease of understanding, the following will describe in detail the arrangement of the laser radar at the entrance of the service area in conjunction with Figure 2.
如图2所示，通过图像处理技术提取服务区的车道信息，判断车道的拓宽、分叉或明显拐点位置。从图2可知，车辆在服务区入口位置的分流区域为B3格，那么需要确保入口处激光雷达能够覆盖该区域，同时对周边区域进行一定的冗余覆盖。服务区中心点位于H4和H5格的交汇点，坐标为(x2，y2)；B3格的中心点坐标为(x1，y1)。根据服务区的尺寸可以得到两个坐标的具体值，进而计算出激光雷达布置的放置向量。As shown in Figure 2, the lane information of the service area is extracted through image processing technology to determine the widening, bifurcation or obvious turning point of the lane. As can be seen from Figure 2, the diversion area of the vehicle at the entrance of the service area is the B3 grid, so it is necessary to ensure that the laser radar at the entrance can cover this area, and at the same time provide a certain degree of redundant coverage of the surrounding area. The center point of the service area is located at the intersection of the H4 and H5 grids, with coordinates (x2 ,y2 ); the coordinates of the center point of the B3 grid are (x1 ,y1 ). The specific values of the two coordinates can be obtained according to the size of the service area, and then the placement vector of the laser radar layout can be calculated.
根据计算得到的放置向量v的方向，确定激光雷达放置的候选区域，并确保激光雷达的有效覆盖范围能够完全覆盖B3格以及A、B两列格子的最大覆盖面积。为了确定激光雷达的最佳放置点，在每个候选区域内进行遍历，并基于激光雷达的识别范围计算能否覆盖B3区域及A、B两列格子。最终，选择一个局部最优的放置格子，并在该格子内进一步通过逼近方法来精确确定放置位置。假设激光雷达的有效识别范围为70米，激光雷达的扫描角度满足30度的要求。设激光雷达放置点为(xp，yp)，则激光雷达的有效覆盖区域为以该点为圆心，半径为70米的圆形区域，具体可以通过如下公式进行表达。According to the direction of the calculated placement vector v, determine the candidate area for the placement of the laser radar, and ensure that the effective coverage of the laser radar can completely cover the maximum coverage area of the B3 grid and the A and B grids. In order to determine the best placement point for the laser radar, traverse each candidate area, and calculate whether it can cover the B3 area and the A and B grids based on the recognition range of the laser radar. Finally, select a local optimal placement grid, and further use the approximation method to accurately determine the placement position in the grid. Assume that the effective recognition range of the laser radar is 70 meters and the scanning angle of the laser radar meets the requirement of 30 degrees. Assume that the placement point of the laser radar is (xp , yp ), then the effective coverage area of the laser radar is a circular area with a radius of 70 meters and this point as the center, which can be expressed by the following formula.
。 .
可以理解的是，单独使用格子中心作为放置点会影响激光雷达的放置精度，且有些区域的中心点可能在道路上或者停车位上，无法满足放置的需求。因此，需要在这个格子的可以放置区域中寻找最佳的放置区域；即在单独的格子内通过逼近原则进行放置点的选择，基于逼近原则确定激光雷达精确布置位置的过程如下：It is understandable that using the center of the grid as the placement point alone will affect the placement accuracy of the LiDAR, and the center point of some areas may be on the road or parking space, which cannot meet the placement requirements. Therefore, it is necessary to find the best placement area in the placement area of this grid; that is, to select the placement point in a single grid based on the approximation principle. The process of determining the precise placement position of the LiDAR based on the approximation principle is as follows:
S301：在对应的局部最优网格中遍历全部可以放置激光雷达的布置区域。S301: Traverse all layout areas where laser radars can be placed in the corresponding local optimal grid.
S302：对布置区域按照设定间隔距离划分多个放置点。S302: Divide the layout area into a plurality of placement points according to a set interval distance.
S303：对每个放置点进行激光雷达的假设放置并判断激光雷达的覆盖范围，选择其中具有最佳覆盖范围的放置点作为激光雷达的精确布置位置。S303: Perform a hypothetical placement of the laser radar for each placement point and determine the coverage range of the laser radar, and select the placement point with the best coverage range as the precise layout position of the laser radar.
应当知道的是，对于步骤S302中间隔距离的设置可以根据本领域技术人员的实际需要自行进行选择，例如间隔距离可以设置为1m。It should be known that the setting of the interval distance in step S302 can be selected according to the actual needs of those skilled in the art, for example, the interval distance can be set to 1 m.
本实施例中，在完成服务区出入口位置的激光雷达的布置后，需要对服务区中间其他位置的布控点进行选择。如图3至图5所示，对于服务区除出入口位置的其他区域的激光雷达布置过程如下：In this embodiment, after completing the layout of the laser radar at the entrance and exit of the service area, it is necessary to select the control points at other locations in the middle of the service area. As shown in Figures 3 to 5, the laser radar layout process for other areas of the service area except the entrance and exit is as follows:
S381：确定服务区出入口位置激光雷达的覆盖范围边缘对应的网格，进而得到服务区未被完全识别的网格区域编号。S381: Determine the grid corresponding to the edge of the coverage range of the laser radar at the entrance and exit of the service area, and then obtain the grid area number that is not fully recognized in the service area.
S382：沿横向或纵向方向遍历每个空的网格区域并模拟激光雷达的放置。S382: Traverse each empty grid area in the horizontal or vertical direction and simulate the placement of the lidar.
S383：对激光雷达在每个放置位置所覆盖的空网格区域以及边缘位置进行记录。S383: Record the empty grid area and edge position covered by the laser radar at each placement position.
S384：选择覆盖空网格区域最多，同时与服务区出入口位置激光雷达所占网格区域的交叉网格数最多的放置点作为最佳放置网格区域。S384: Select the placement point that covers the most empty grid areas and has the largest number of intersection grids with the grid area occupied by the laser radar at the entrance and exit of the service area as the optimal placement grid area.
S385：于最佳放置网格区域内通过逼近原则确定激光雷达精确布置位置。S385: Determine the precise placement position of the laser radar in the optimal placement grid area by using the approximation principle.
可以理解的是，对于服务区中间位置的激光雷达布置主要是通过基于出入口位置激光雷达的覆盖范围向中间进行逼近，通过这种方式可以有效的减少激光雷达的布置数量。为了方便理解，下面将结合附图对服务区中间区域的激光雷达的布设过程进行详细的描述。It can be understood that the layout of the laser radar in the middle of the service area is mainly based on the coverage range of the laser radar at the entrance and exit positions, which can effectively reduce the number of laser radars deployed. For ease of understanding, the layout process of the laser radar in the middle area of the service area will be described in detail with reference to the accompanying drawings.
如图3所示，两个圆圈分别代表服务区出入口位置的两个激光雷达的覆盖范围。提取两个出入口激光雷达点位的完全覆盖区域，对于未被完全识别的区域，将其划定为空区域。如图4所示，灰色阴影部分为出入口位置激光雷达能够完全覆盖的网格区域范围，从图中可以看出，目前的服务区所需识别但仍未被完全识别的区域包含E2，E7-E8，G3-G8，H3-H8，I3以及I6-I8。As shown in Figure 3, the two circles represent the coverage of the two laser radars at the entrance and exit of the service area. The fully covered area of the two entrance and exit laser radar points is extracted, and the areas that are not fully recognized are defined as empty areas. As shown in Figure 4, the gray shaded area is the grid area that can be fully covered by the laser radar at the entrance and exit. It can be seen from the figure that the areas that need to be recognized but have not yet been fully recognized in the current service area include E2, E7-E8, G3-G8, H3-H8, I3 and I6-I8.
在得到这些区域后，首先需要对区域的跨度进行划分；因为每个格子都是15m×15m的区间，因此可以先判断横向的格子跨度和纵向的格子跨度之间的比例与范围。通过这个比例与范围可以进一步估算出大致还需要布设多少个激光雷达。以当前服务区为例，未被识别的区域的纵跨为六个格子，为90米，即单侧45米。横跨为四个格子，为60米，即单侧30米。因此，理论上只需要再布设一个激光雷达即可。After obtaining these areas, we first need to divide the span of the area; because each grid is an interval of 15m×15m, we can first determine the ratio and range between the horizontal grid span and the vertical grid span. Through this ratio and range, we can further estimate how many laser radars need to be deployed. Taking the current service area as an example, the vertical span of the unrecognized area is six grids, which is 90 meters, or 45 meters on one side. The horizontal span is four grids, which is 60 meters, or 30 meters on one side. Therefore, in theory, only one more laser radar needs to be deployed.
接下来，需要确定这个额外激光雷达的布设位置；沿横向或纵向方向依次选择未被完全占满的中间格子，然后判断该格子是否可以布控激光雷达，具体的判断方法可以参照出入口的激光雷达的布置判断方法。若该网格区域可以布置激光雷达，则可以对激光雷达进行虚拟布置并记录其覆盖范围。在全部可以布置激光雷达的网格区域都完成了激光雷达的虚拟布置后，对记录的全部覆盖范围进行对比，选择能够占据最多空网格区域，同时与出入口位置激光雷达具有最大交叉重叠范围的覆盖范围所对应的网格区域作为最佳放置网格区域。通过对未覆盖区域进行仔细分析，选定激光雷达放置区域后，需要进一步进行精确调整。激光雷达放置点的选择与之前出入口激光雷达布置的原则相同。为了确保每个激光雷达的覆盖范围最大化，选择合适的放置点后，再次验证激光雷达的有效覆盖范围，确保其能够覆盖未被完全识别的区域。如图5所示，基于当前的三个激光雷达的放置点，可以实现对服务区内全部需要被识别区域的全覆盖。Next, it is necessary to determine the layout position of this additional laser radar; select the middle grid that is not completely occupied in the horizontal or vertical direction, and then determine whether the grid can be deployed with a laser radar. The specific judgment method can refer to the layout judgment method of the laser radar at the entrance and exit. If the laser radar can be deployed in the grid area, the laser radar can be virtually deployed and its coverage range can be recorded. After the virtual layout of the laser radar is completed in all grid areas where the laser radar can be deployed, the recorded coverage range is compared, and the grid area that can occupy the most empty grid areas and has the largest cross-overlapping range with the laser radar at the entrance and exit is selected as the best grid area for placement. After carefully analyzing the uncovered area and selecting the laser radar placement area, further precise adjustments are required. The selection of laser radar placement points is the same as the principle of the previous entrance and exit laser radar layout. In order to ensure that the coverage range of each laser radar is maximized, after selecting the appropriate placement point, the effective coverage range of the laser radar is verified again to ensure that it can cover the area that is not fully identified. As shown in Figure 5, based on the current placement points of the three laser radars, full coverage of all areas that need to be identified in the service area can be achieved.
本实施例中，步骤S400包括如下过程：In this embodiment, step S400 includes the following process:
S410：对激光雷达采集的三维点云进行目标标注，包括小轿车、箱式货车和大卡车三类目标，其他类型被标注为负样本。S410: Target annotation is performed on the three-dimensional point cloud collected by the lidar, including three types of targets: cars, box trucks, and large trucks. Other types are marked as negative samples.
可以理解的是，为了方便数据的管理，对于三维点云的标注可以采用LabelCloud工具，并生成符合KITTI数据集格式的标签。需要注意的是，在标注车辆时，车头方向必须正确标注，否则会影响后续的目标识别精度。It is understandable that in order to facilitate data management, the LabelCloud tool can be used to annotate the 3D point cloud and generate labels that conform to the KITTI dataset format. It should be noted that when annotating the vehicle, the direction of the vehicle head must be correctly annotated, otherwise it will affect the subsequent target recognition accuracy.
S420：于服务器端构建车辆识别模型并进行模型训练。S420: Construct a vehicle recognition model on the server side and perform model training.
可以理解的是，用于车辆识别模型训练的具体方式有多种，本实施例通过PointPillars模型进行模型训练，具体包括如下过程：It is understandable that there are many specific methods for vehicle recognition model training. This embodiment uses the PointPillars model to perform model training, which specifically includes the following process:
S421：将三维点云数据划分为多个柱状体；若单一柱状体内包含的点数超过阈值，则计算该柱状体的特征；若单一柱状体内点数不足阈值，则填充为零。S421: Divide the three-dimensional point cloud data into multiple cylinders; if the number of points contained in a single cylinder exceeds a threshold, calculate the features of the cylinder; if the number of points in a single cylinder is less than the threshold, fill it with zero.
可以理解的是，每个柱状体代表固定区域内的点云信息，PointPillars模型将空间分割成大小一致的三维网格，即柱状体；网格尺寸可根据实际场景进行调整。在完成柱状体的特征计算后，每个柱状体被表示为一个点云特征向量，包括坐标信息，反射强度等。It can be understood that each column represents the point cloud information in a fixed area. The PointPillars model divides the space into three-dimensional grids of uniform size, namely columns; the grid size can be adjusted according to the actual scene. After completing the feature calculation of the column, each column is represented as a point cloud feature vector, including coordinate information, reflection intensity, etc.
S422：将每个柱状体内的点进行聚合，计算该柱状体的均值特征并压缩为固定长度的特征向量；将所有柱状体的特征向量进行拼接形成二维特征图。S422: Aggregate the points in each column, calculate the mean feature of the column and compress it into a feature vector of fixed length; and concatenate the feature vectors of all the columns to form a two-dimensional feature map.
应当知道的是，特征图的尺寸为柱状体数量乘以每个柱状体的特征维度。It should be noted that the size of the feature map is the number of bins multiplied by the feature dimension of each bin.
S423：通过主干网络对二维特征图进行处理，逐步捕捉区域形状、纹理及不同区域间的空间关系，从而提取出二维特征图中的高级空间特征。S423: Processing the two-dimensional feature map through the backbone network, gradually capturing the regional shape, texture and spatial relationship between different regions, so as to extract high-level spatial features in the two-dimensional feature map.
可以理解的是，通过2D卷积神经网络作为主干网络，进而通过多个卷积层对特征图进行处理以得到相应的高级空间特征。卷积层通常集合BatchNorm和ReLU激活函数，以增强网络的非线性表达能力。It can be understood that the 2D convolutional neural network is used as the backbone network, and then the feature map is processed through multiple convolutional layers to obtain the corresponding high-level spatial features. The convolutional layer usually combines BatchNorm and ReLU activation functions to enhance the nonlinear expression ability of the network.
S424：通过Feature Pyramid Networks网络结构对提取的高级空间特征进行融合并传递至head模块，进而head模块对最终的检测结果进行生成，包括目标类别、位置及置信度。S424: The extracted high-level spatial features are fused through the Feature Pyramid Networks network structure and passed to the head module, and then the head module generates the final detection results, including target category, location and confidence.
应当知道的是，对于head模块的检测结果生成包括如下过程：先进行分类任务，即预测每个柱状体是否包含目标；然后进行回归任务，即预测目标的位置和尺寸。通过与标注数据进行对比，反向传播并调整网络权重，经过多轮训练可以有效的提高车辆识别精度。It should be known that the detection result generation of the head module includes the following process: first, the classification task is performed, that is, predicting whether each column contains an object; then the regression task is performed, that is, predicting the position and size of the object. By comparing with the labeled data, back propagating and adjusting the network weights, the vehicle recognition accuracy can be effectively improved after multiple rounds of training.
S430：对激光雷达的坐标系进行标定，确保激光雷达采集的点云数据的坐标统一。S430: Calibrate the coordinate system of the laser radar to ensure that the coordinates of the point cloud data collected by the laser radar are unified.
可以理解的是，通过上述两种坐标系对激光雷达进行标定，确保所有激光雷达采集的点云数据都能统一到已标定的坐标系下。之后，将实时采集到的点云数据传输至服务器端，结合已训练好的模型进行检测，即可实现对车辆的识别。It is understandable that the laser radar is calibrated through the above two coordinate systems to ensure that all point cloud data collected by the laser radar can be unified into the calibrated coordinate system. After that, the point cloud data collected in real time is transmitted to the server side, and combined with the trained model for detection, the vehicle can be identified.
S440：将激光雷达采集的点云数据上传至服务器端，进而结合训练好的车辆识别模型进行检测，根据检测的结果对车辆进行追踪和行为识别。S440: The point cloud data collected by the laser radar is uploaded to the server, and then detected in combination with the trained vehicle recognition model, and the vehicle is tracked and the behavior is recognized according to the detection results.
可以理解的是，在激光雷达的实时监测阶段，车辆识别结果包括3D边界框、车辆中心坐标、尺寸等信息。对于每一辆被识别的车辆，系统会为其分配一个唯一标签，并将这些信息传入DeepSORT算法中。DeepSORT结合了卷积神经网络（CNN）提取的车辆外观特征，通过SORT框架和卡尔曼滤波器预测物体的运动轨迹，并利用物体间的IoU（Intersection overUnion）值进行目标匹配，从而更新物体的状态（如位置、速度）。It is understandable that during the real-time monitoring stage of the LiDAR, the vehicle recognition results include information such as the 3D bounding box, vehicle center coordinates, and size. For each identified vehicle, the system assigns it a unique label and passes this information into the DeepSORT algorithm. DeepSORT combines the vehicle appearance features extracted by the convolutional neural network (CNN), predicts the object's motion trajectory through the SORT framework and Kalman filter, and uses the IoU (Intersection over Union) value between objects for target matching, thereby updating the object's state (such as position and speed).
由于整个服务区实现了全覆盖的激光雷达布局，并且车辆在已标定的坐标系下被识别，连续两个激光雷达之间的识别结果可以通过车辆位置进行匹配，从而实现车辆的跨激光雷达追踪。最终，预测结果会实时上传至服务区的管理中心，用于违规行为检测和服务区管理。通过这种方式，能够有效提升服务区的管理与运维效率。Since the entire service area has achieved full coverage of the LiDAR layout, and the vehicle is identified in a calibrated coordinate system, the recognition results between two consecutive LiDARs can be matched by the vehicle position, thereby realizing cross-LiDAR tracking of the vehicle. Finally, the prediction results will be uploaded to the management center of the service area in real time for violation detection and service area management. In this way, the management and operation efficiency of the service area can be effectively improved.
应当知道的是，在步骤S400中，通过采用PointPillars算法和DeepSORT算法的结合，能够实现对服务区内车辆的精准实时识别与追踪。PointPillars算法通过点云数据的高效特征提取，能够准确识别不同类型的车辆（如小轿车、卡车等），并且支持在复杂环境中对车辆进行持续的追踪。DeepSORT算法则通过卡尔曼滤波器与外观特征提取，保证了车辆追踪的高稳定性与高精度。在此基础上，所有车辆的识别数据和位置信息将实时上传至服务区管理中心，实现了服务区管理的智能化和自动化，显著提升了车辆管理的效率和安全性，为服务区提供了智能化运维和数据分析支持。It should be known that in step S400, by combining the PointPillars algorithm and the DeepSORT algorithm, accurate real-time identification and tracking of vehicles in the service area can be achieved. The PointPillars algorithm can accurately identify different types of vehicles (such as cars, trucks, etc.) through efficient feature extraction of point cloud data, and supports continuous tracking of vehicles in complex environments. The DeepSORT algorithm ensures high stability and high precision of vehicle tracking through Kalman filter and appearance feature extraction. On this basis, the identification data and location information of all vehicles will be uploaded to the service area management center in real time, realizing the intelligent and automated management of the service area, significantly improving the efficiency and safety of vehicle management, and providing intelligent operation and maintenance and data analysis support for the service area.
以上描述了本申请的基本原理、主要特征和本申请的优点。本行业的技术人员应该了解，本申请不受上述实施例的限制，上述实施例和说明书中描述的只是本申请的原理，在不脱离本申请精神和范围的前提下本申请还会有各种变化和改进，这些变化和改进都落入要求保护的本申请的范围内。本申请要求的保护范围由所附的权利要求书及其等同物界定。The above describes the basic principles, main features and advantages of the present application. Those skilled in the art should understand that the present application is not limited by the above embodiments, and the above embodiments and the specification only describe the principles of the present application. The present application may have various changes and improvements without departing from the spirit and scope of the present application, and these changes and improvements fall within the scope of the present application for which protection is sought. The scope of protection claimed by the present application is defined by the attached claims and their equivalents.
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