WO2022170633A1 - Rail transit vehicle collision avoidance detection method based on vision and laser ranging - Google Patents
Rail transit vehicle collision avoidance detection method based on vision and laser ranging Download PDFInfo
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- the invention relates to the technical field of anti-collision detection of rail transit vehicles, in particular to a method for anti-collision detection of rail transit vehicles based on vision and laser ranging.
- the urban rail transit system intelligence and vehicle driverless technology have entered the design and application stage.
- the collision avoidance safety issue in the operation and maintenance of rail transit vehicles is a difficult problem in urban rail transit operations and an important prerequisite for ensuring operational safety.
- the traditional vehicle collision avoidance method usually mainly relies on the daily inspection and prevention of maintenance personnel, and relies on the driver to make human judgments and collision avoidance operations when the vehicle is in operation.
- the current mainstream technology of intelligent collision avoidance of rail transit vehicles adopts machine vision.
- image acquisition and processing technology By using image acquisition and processing technology to achieve the purpose of detecting and tracking vehicle targets, the traffic environment and traffic targets can be analyzed in time. Automatically perform statistical analysis on relevant traffic parameters such as the number of motor vehicles, running speed, and vehicle type driving on the traffic section, and then store the detected traffic information, so that the traffic analysis and management work can be based on evidence.
- machine vision technology is susceptible to environmental interference in complex environments, and the judgment of emergencies also requires a process of machine learning. As a result, the vehicle collision avoidance vision system cannot make timely and correct judgments and there is a major problem of vehicle collision warning errors.
- the technical problem to be solved by the present invention is to provide an anti-collision detection method for rail vehicles based on vision and laser ranging, and the detection result is more accurate.
- the present invention provides a collision avoidance detection method for rail vehicles based on vision and laser ranging, including the following steps:
- Step 1 obtain a grayscale image of the target to be detected
- Step 2 Hough transform the grayscale image to implement image preprocessing
- Step 3 Using Hough transform to preprocess the grayscale image to obtain a vehicle shadow detection feature image
- the Sobel edge detection method is used to process and binarize the image of the vehicle area to be inspected, and the continuous horizontal edge position information of the binary image is counted to realize the vehicle edge detection and obtain the precise position information of the vehicle area;
- CCD image is used to detect and analyze the obtained early warning target, and the lidar pan-tilt is linked to scan the fan-shaped target area in front of the early warning target, and the data information of the azimuth and distance of the early warning target is measured by laser;
- Step 4 Use fusion image processing information and laser ranging information to obtain vehicle detection and tracking detection data, and issue collision warning information to the human-computer interaction system.
- the present invention has the following beneficial effects: the present invention adopts an anti-collision detection method based on the mutual fusion of vision and laser ranging, and the detection result is more accurate.
- FIG. 1 is a schematic diagram of collision avoidance detection of rail transit vehicles based on vision and laser ranging provided by the present invention.
- Fig. 1 is a schematic diagram of collision avoidance detection of rail transit vehicles based on vision and laser ranging provided by the present invention, as shown in Fig. 1, including the following steps:
- Step 1 obtain a grayscale image of the target to be detected
- Step 2 Hough transform the grayscale image to implement image preprocessing
- Step 3 Using Hough transform to preprocess the grayscale image to obtain a vehicle shadow detection feature image
- the Sobel edge detection method is used to process and binarize the image of the vehicle area to be inspected, and the continuous horizontal edge position information of the binary image is counted to realize the vehicle edge detection and obtain the precise position information of the vehicle area;
- CCD image is used to detect and analyze the obtained early warning target, and the lidar pan-tilt is linked to scan the fan-shaped target area in front of the early warning target, and the data information of the azimuth and distance of the early warning target is measured by laser;
- Step 4 Use fusion image processing information and laser ranging information to obtain vehicle detection and tracking detection data, and issue collision warning information to the human-computer interaction system.
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Abstract
Disclosed in the present invention are a rail transit vehicle collision avoidance detection method based on vision and laser ranging, comprising: acquiring a grayscale image of a target to be detected; performing image preprocessing using Hough transform; obtaining a vehicle shadow detection feature image, and using a method based on block area statistics to detect and process the shadow detection feature image; using a Sobel edge detection method to obtain vehicle edge detection information; according to an early-warning target obtained by the image detection, linking a laser radar gimbal to scan a fan-shaped target area in front of the early-warning target to obtain laser measurement data; fusing image processing information and laser ranging information, integrating vehicle collision avoidance detection data, and sending a collision early warning to a human-computer interaction system. The detection result of the present invention is more accurate.
Description
本发明涉及轨交车辆防撞检测技术领域，尤其涉及一种基于视觉与激光测距的轨交车辆防撞检测方法。The invention relates to the technical field of anti-collision detection of rail transit vehicles, in particular to a method for anti-collision detection of rail transit vehicles based on vision and laser ranging.
城市轨道交通系统智能化和车辆无人驾驶技术已经进入设计应用阶段，其中轨交车辆运维中的防撞安全问题是城市轨交运营中的难题，也是保障运营安全的重要前提。The urban rail transit system intelligence and vehicle driverless technology have entered the design and application stage. The collision avoidance safety issue in the operation and maintenance of rail transit vehicles is a difficult problem in urban rail transit operations and an important prerequisite for ensuring operational safety.
传统的车辆防撞方法平时主要依靠检修维护人员的日常检查防范，在车辆运营时则依靠驾驶员对碰撞发生险情做出人为判断和防撞操作。在无人驾驶系统中，轨交车辆的智能防撞目前主流技术采用机器视觉，通过运用图像采集和处理技术以达到检测、跟踪车辆目标的目的，可及时对交通环境情况以及交通目标进行分析，对行驶于交通路段之上的机动车数目、运行速度、车辆类别等相关交通参数自动进行统计分析，然后对检测到的交通信息进行存储， 使交通分析管理工作有据可依。但是机器视觉技术在复杂的环境下容易受到环境干扰，对突发事件判断也需要机器学习的过程，导致车辆防撞视觉系统不能及时正确判断而出现车辆碰撞预警失误的重大问题。The traditional vehicle collision avoidance method usually mainly relies on the daily inspection and prevention of maintenance personnel, and relies on the driver to make human judgments and collision avoidance operations when the vehicle is in operation. In the driverless system, the current mainstream technology of intelligent collision avoidance of rail transit vehicles adopts machine vision. By using image acquisition and processing technology to achieve the purpose of detecting and tracking vehicle targets, the traffic environment and traffic targets can be analyzed in time. Automatically perform statistical analysis on relevant traffic parameters such as the number of motor vehicles, running speed, and vehicle type driving on the traffic section, and then store the detected traffic information, so that the traffic analysis and management work can be based on evidence. However, machine vision technology is susceptible to environmental interference in complex environments, and the judgment of emergencies also requires a process of machine learning. As a result, the vehicle collision avoidance vision system cannot make timely and correct judgments and there is a major problem of vehicle collision warning errors.
本发明所要解决的技术问题在于，提供一种基于视觉与激光测距的轨交车辆防撞检测方法，检测结果更准确。The technical problem to be solved by the present invention is to provide an anti-collision detection method for rail vehicles based on vision and laser ranging, and the detection result is more accurate.
为了解决上述技术问题，本发明提供了一种基于视觉与激光测距的轨交车辆防撞检测方法，包括步骤如下：In order to solve the above technical problems, the present invention provides a collision avoidance detection method for rail vehicles based on vision and laser ranging, including the following steps:
步骤一：获取待检测目标的灰度图像；Step 1: obtain a grayscale image of the target to be detected;
步骤二：将所述灰度图像进行Hough变换实施图像预处理；Step 2: Hough transform the grayscale image to implement image preprocessing;
步骤三：采用Hough变换对灰度图像进行预处理后得到车辆阴影检测特征图像；Step 3: Using Hough transform to preprocess the grayscale image to obtain a vehicle shadow detection feature image;
采用基于块区域统计的阴影检测分析方法对所述车辆阴影检测特征图像进行检测处理；Use the shadow detection analysis method based on block area statistics to detect and process the vehicle shadow detection feature image;
采用Sobel边缘检测方法处理车辆待检区域的图像并进行二值化，统计二值图像的连续水平边缘位置信息以实现车辆边缘检测，获取车辆区域精确位置信息；The Sobel edge detection method is used to process and binarize the image of the vehicle area to be inspected, and the continuous horizontal edge position information of the binary image is counted to realize the vehicle edge detection and obtain the precise position information of the vehicle area;
采用CCD图像检测分析所获得预警目标，联动激光雷达云台扫描预警目标前方扇形目标区域，激光测量预警目标物的方位和距离的数据信息；CCD image is used to detect and analyze the obtained early warning target, and the lidar pan-tilt is linked to scan the fan-shaped target area in front of the early warning target, and the data information of the azimuth and distance of the early warning target is measured by laser;
步骤四：采用融合图像处理信息和激光测距信息，获得车辆检测、跟踪检测的数据，向人机交互系统发出碰撞预警信息。Step 4: Use fusion image processing information and laser ranging information to obtain vehicle detection and tracking detection data, and issue collision warning information to the human-computer interaction system.
本发明，具有如下有益效果：本发明采用基于视觉与激光测距相互融合的防撞检测方法，检测结果更准确。The present invention has the following beneficial effects: the present invention adopts an anti-collision detection method based on the mutual fusion of vision and laser ranging, and the detection result is more accurate.
为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。In order to explain the embodiments of the present invention or the technical solutions in the prior art more clearly, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only These are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained from these drawings without creative efforts.
图1是本发明提供的基于视觉与激光测距的轨交车辆防撞检测的示意图。FIG. 1 is a schematic diagram of collision avoidance detection of rail transit vehicles based on vision and laser ranging provided by the present invention.
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.
图1是本发明提供的基于视觉与激光测距的轨交车辆防撞检测的示意图，如图1所示，包括步骤如下：Fig. 1 is a schematic diagram of collision avoidance detection of rail transit vehicles based on vision and laser ranging provided by the present invention, as shown in Fig. 1, including the following steps:
步骤一：获取待检测目标的灰度图像；Step 1: obtain a grayscale image of the target to be detected;
步骤二：将所述灰度图像进行Hough变换实施图像预处理；Step 2: Hough transform the grayscale image to implement image preprocessing;
步骤三：采用Hough变换对灰度图像进行预处理后得到车辆阴影检测特征图像；Step 3: Using Hough transform to preprocess the grayscale image to obtain a vehicle shadow detection feature image;
采用基于块区域统计的阴影检测分析方法对所述车辆阴影检测特征图像进行检测处理；Use the shadow detection analysis method based on block area statistics to detect and process the vehicle shadow detection feature image;
采用Sobel边缘检测方法处理车辆待检区域的图像并进行二值化，统计二值图像的连续水平边缘位置信息以实现车辆边缘检测，获取车辆区域精确位置信息；The Sobel edge detection method is used to process and binarize the image of the vehicle area to be inspected, and the continuous horizontal edge position information of the binary image is counted to realize the vehicle edge detection and obtain the precise position information of the vehicle area;
采用CCD图像检测分析所获得预警目标，联动激光雷达云台扫描预警目标前方扇形目标区域，激光测量预警目标物的方位和距离的数据信息；CCD image is used to detect and analyze the obtained early warning target, and the lidar pan-tilt is linked to scan the fan-shaped target area in front of the early warning target, and the data information of the azimuth and distance of the early warning target is measured by laser;
步骤四：采用融合图像处理信息和激光测距信息，获得车辆检测、跟踪检测的数据，向人机交互系统发出碰撞预警信息。Step 4: Use fusion image processing information and laser ranging information to obtain vehicle detection and tracking detection data, and issue collision warning information to the human-computer interaction system.
需要说明的是，在本文中，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者装置不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者装置所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括该要素的过程、方法、物品或者装置中还存在另外的相同要素。It should be noted that, herein, the terms "comprising", "comprising" or any other variation thereof are intended to encompass non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements, It also includes other elements not expressly listed or inherent to such a process, method, article or apparatus. Without further limitation, an element qualified by the phrase "comprising a..." does not preclude the presence of additional identical elements in a process, method, article or apparatus that includes the element.
对所公开的实施例的上述说明，使本领域专业技术人员能够实现或使用本发明。对这些实施例的多种修改对本领域的专业技术人员来说将是显而易见的，本文中所定义的一般原理可以在不脱离本发明的精神或范围的情况下，在其它实施例中实现。因此，本发明将不会被限制于本文所示的这些实施例，而是要符合与本文所公开的原理和新颖特点相一致的最宽的范围。The above description of the disclosed embodiments enables any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (1)
- 一种基于视觉与激光测距的轨交车辆防撞检测方法，其特征在于，包括步骤如下：步骤一：获取待检测目标的灰度图像；步骤二：将所述灰度图像进行Hough变换实施图像预处理；步骤三：采用Hough变换对灰度图像进行预处理后得到车辆阴影检测特征图像；采用基于块区域统计的阴影检测分析方法对所述车辆阴影检测特征图像进行检测处理；采用Sobel边缘检测方法处理车辆待检区域的图像并进行二值化，统计二值图像的连续水平边缘位置信息以实现车辆边缘检测，获取车辆区域精确位置信息；采用CCD图像检测分析所获得预警目标，联动激光雷达云台扫描预警目标前方扇形目标区域，激光测量预警目标物的方位和距离的数据信息；步骤四：采用融合图像处理信息和激光测距信息，获得车辆碰撞检测的数据，向人机交互系统发出碰撞预警。A method for collision avoidance detection of rail transit vehicles based on vision and laser ranging, which is characterized in that it includes the following steps: step 1: acquiring a grayscale image of a target to be detected; step 2: performing Hough transform on the grayscale image to implement Image preprocessing; step 3: using Hough transform to preprocess the grayscale image to obtain a vehicle shadow detection feature image; use a shadow detection analysis method based on block area statistics to detect and process the vehicle shadow detection feature image; use Sobel edge The detection method processes and binarizes the image of the vehicle area to be inspected, counts the continuous horizontal edge position information of the binary image to realize vehicle edge detection, and obtains the precise position information of the vehicle area; adopts CCD image detection and analysis to obtain the early warning target, and links the laser The radar pan/tilt scans the fan-shaped target area in front of the early warning target, and laser measures the data information of the azimuth and distance of the early warning target; Step 4: Use the fusion image processing information and laser ranging information to obtain the data of vehicle collision detection, and report it to the human-computer interaction system. Issue a collision warning.
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