A device for rapid and automatic measurement of trackside equipment limits

Abstract

The present invention provides a device for rapid and automatic measurement of trackside equipment limits, which includes a measuring vehicle, a laser scanner, a camera, a lens, an encoder, and a computing device; wherein the laser scanner, the camera, and the encoder are all arranged on the measuring vehicle, and the lens is arranged on the camera; the laser scanner, the camera, and the encoder are respectively communicated with the computing device; an image is acquired by arranging the camera, and a pre-trained model is used to identify the target equipment area in the framed image, and whether it is the best image is determined based on the position of the framed area in the image. For the best image, the corresponding mileage data is recorded as the actual mileage of the corresponding target equipment. After completing the mileage calculation of all trackside equipment, the limit distance of adjacent trackside equipment is calculated to determine whether it meets the engineering requirements.

Description

Technical Field

[0001] The invention relates to the field of rail transportation, and in particular to a device for quickly and automatically measuring the limits of trackside equipment. Background Art

[0002] In modern life, daily commuting is becoming increasingly convenient. Within cities, people can choose to use public transportation such as buses and subways, while intercity travel can be achieved by trains and high-speed trains. For these types of trains, track systems must be laid and trackside equipment installed. Trackside equipment generally refers to devices installed at both ends of the track, including signal lights and speedometers. After trackside equipment is installed, it must be inspected by the railway department. This includes verifying that the installation locations of each device match the design drawings and checking the clearance distances between each device to verify that the actual installation distances are consistent with the pre-construction planning. Existing trackside equipment inspections are primarily conducted through on-site personnel surveys. However, due to the long lengths of track and the large amount of data required for measurement, the inspection process is labor-intensive, time-consuming, and labor-intensive. Furthermore, the numerous test parameters require dozens of different testing tools, which are extremely inconvenient to carry. Manual measurement is also inconvenient, time-consuming, and prone to data omissions and measurement errors. Therefore, a device and method for quickly and easily measuring the clearances of trackside equipment is needed. Summary of the Invention

[0003] The purpose of the present invention is to solve the deficiencies of the prior art and to provide a device for rapid and automatic measurement of trackside equipment limits, which has a simple structure and is easy to use.

[0004] A device for automatically measuring the limits of trackside equipment includes a measuring vehicle, a laser scanner, a camera, a lens, an encoder, and a computing device; wherein the laser scanner, the camera, and the encoder are all arranged on the measuring vehicle, and the lens is arranged on the camera; the laser scanner, the camera, and the encoder are respectively communicatively connected to the computing device.

[0005] Furthermore, the laser scanner is arranged in the middle position of the measuring vehicle, wherein the scanning direction of the laser scanner is on both sides of the measuring vehicle; the scanning direction of the laser scanner is always perpendicular to the moving direction of the measuring vehicle; the encoder is arranged on the wheel of the measuring vehicle; and the computing device is a service terminal.

[0006] A method for rapid and automatic measurement of trackside equipment clearances comprises the following steps:

[0007] Step 1: The measuring vehicle receives a start signal and moves forward along the track. Simultaneously, the camera collects image information on both sides of the track, the laser scanner collects scan data, and the encoder collects coded data. The collected image data, scan data, and coded data are transmitted to the computing device in real time.

[0008] Step 2: The computing device receives the image data and reads the coded data received simultaneously, converts the coded data into mileage data, and stores the image data and the mileage data in association with each other. The computing device receives the scanned data and reads the coded data received simultaneously, converts the coded data into mileage data, and stores the scanned data and the mileage data in association with each other.

[0009] Step 3: The measuring vehicle receives a stop signal and stops moving on the track; the computing device begins processing the stored image data, scan data, and mileage data;

[0010] Step 4: The computing device completes image detection based on the received image data and the corresponding mileage data;

[0011] Step 5: After image detection is completed, the computing device processes the scanned data based on the received scanned data and the corresponding mileage data to obtain the distance data from the wayside equipment to the track;

[0012] Step 6: Output the information of the trackside equipment and end the step.

[0013] Furthermore, the image detection in step 4 includes calculation of the clearance distance between trackside equipment, and the calculation of the clearance distance includes the following steps:

[0014] Step 41: Receive input image data based on the model obtained through deep learning training, traverse the image data, read each frame of the image in sequence, and identify and select the target device in each frame of the image;

[0015] Step 42: Read the selected area in each frame image, and determine whether it is the best image based on the distance between the center point of the selected area and the center point of the corresponding image; determine the mileage corresponding to the target device based on the mileage data corresponding to the best image;

[0016] Step 43: Obtain the mileage corresponding to all track equipment, sort the track equipment according to mileage, and calculate the clearance distance between adjacent equipment in mileage;

[0017] Step 44: Arrange the limit distances and output them in a table, and then end the step.

[0018] Furthermore, the deep learning-based model in step 41 adopts the Yolov3 Darknet53 basic network model.

[0019] Furthermore, the selection of the best image in step 42 includes the following steps:

[0020] Step 421: Read the selected area in the image and obtain the coordinates of two vertices on any diagonal line of the selected area, which are expressed as (Sx, Sy) and (Ex, Ey) respectively. When reading the selected area of the image for the first time, read the selected area of the first frame of the image.

[0021] Step 422: Calculate the midpoint coordinates of the two vertex coordinates (Sx, Sy) and (Ex, Ey), where the midpoint coordinates are expressed as (Cx, Cy);

[0022] Step 423: Obtain the coordinates of the center point of the image, and obtain the horizontal coordinate distance DistanceCW between the coordinates of the midpoint of the selected area and the coordinates of the center point of the image;

[0023] Step 424: Determine whether all images have been traversed; if so, proceed to step 425; otherwise, return to step 421;

[0024] Step 425: traverse all images, select an image frame with the smallest horizontal coordinate distance DistanceCW as the best image, and end the step.

[0025] Furthermore, the image detection in step 4 also includes verification of the installation position of the trackside equipment, and the verification of the installation position of the trackside equipment includes the following steps:

[0026] Step 41a: Obtain the trackside equipment and corresponding mileage data identified during the calculation of the clearance distance; obtain a preset trackside equipment installation data table;

[0027] Step 42a: Label the mileage data and sequentially read the IDs of the identified trackside equipment and the corresponding mileage data L1; sequentially read the IDs of the trackside equipment and the corresponding mileage data L2 in the installation data table according to the serial number; the IDs of the trackside equipment include signal lights and speedometers;

[0028] Step 43a: Determine whether the trackside equipment ID obtained by identification is consistent with the trackside equipment ID of the corresponding order in the installation data table; if the ID is consistent, write "yes" in the corresponding position in the installation data table; otherwise, write "no";

[0029] Step 44a: Calculate the absolute value of the difference between the mileage data L1 obtained by the identification calculation of the corresponding order and the mileage data L2 in the installation data table, and write the calculation result to the corresponding position in the installation data table;

[0030] Step 45a: Complete the comparative calculation of the last identified trackside equipment number and the last identified trackside equipment number in the installation data table; output the installation data table and end the step.

[0031] Furthermore, the processing of the scanned data in step 5 includes the following steps:

[0032] Step 51: Read the image data of the best image acquired by the camera and the corresponding mileage data; and sort and number the acquired image data according to the mileage data;

[0033] Step 52: Compensating the mileage data corresponding to the image data of the optimal image to obtain compensated mileage data;

[0034] Step 53: Read a corresponding scan data according to the compensated mileage of the best image; when reading the scan data for the first time, read it according to the compensated mileage data of the first labeled image data; and read the scan data in sequence according to the compensated mileage data;

[0035] Step 54: Determine the position of the camera in the scanned image based on the height difference between the camera and the scanner and the left and right deviation of the camera and the scanner perpendicular to the direction of travel of the measuring vehicle;

[0036] Step 55: Calculate the field of view of the camera in the scanned image;

[0037] Step 56: extracting contour points of the cross section of the trackside equipment according to the field of view of the camera within the scanned data;

[0038] Step 57: Fit the smallest circumscribed rectangle based on the scanned contour points, and calculate the center point of the circumscribed rectangle;

[0039] Step 58: Calculate the difference in the horizontal coordinates between the center point of the circumscribed rectangle and the center point of the scanned image as the distance between the trackside equipment and the track in the scanned image; scale the distance in the scanned image according to the scan ratio to obtain the actual distance and output it;

[0040] Step 59: Complete the processing of the scan data corresponding to the compensated mileage data of the last labeled image data, and end the step.

[0041] Furthermore, the value compensated in step 52 is the deviation distance between the scanner and the camera in the direction of travel of the measuring vehicle, and the compensation mileage=mileage-deviation distance.

[0042] Furthermore, the height difference and left-right deviation of the camera and scanner in step 54 need to be converted to a scan ratio when converted to the scanned image; wherein the height difference ch in the scanned image = actual height difference * scan ratio; the left-right deviation dh in the scanned image = actual left-right deviation * scan ratio; the coordinates of the center point of the scanned image are expressed as (Rx, Ry), and the coordinates of the position of the camera in the scanned image are expressed as (Rx-dh, Ry-ch);

[0043] In step 55, L is set to represent the scanning distance of the scanner, and H is set to represent the maximum shooting range of the camera in the scanned image; the coordinate position of the camera (Rx-dh, Ry-ch) is taken as vertex a, the target surface width of the camera is h, and the focal length of the lens is f, and H=hL / f is obtained, the coordinates of vertex b are obtained as (Rx-dh+L, Ry-ch+H / 2), and the coordinates of vertex c are obtained as (Rx-dh+L, Ry-ch-H / 2); a triangular area is drawn with vertices a, b, and c, and the triangular area represents the field of view range of the camera in the scanned image.

[0044] The beneficial effects of the present invention are:

[0045] The camera is set up to capture images, and a pre-trained model is used to identify the target equipment area in the framed image. The optimal image is determined based on the position of the framed area in the image. For the optimal image, the corresponding mileage data is recorded as the actual mileage of the corresponding target equipment. After the mileage calculation of all trackside equipment is completed, the clearance distance of adjacent trackside equipment is calculated to determine whether it meets the project requirements.

[0046] By comparing the ID of the target device identified by the best image with the preset table, it is determined whether there is any error in the order of installation of trackside equipment;

[0047] By comparing the mileage corresponding to the best image with the preset table, it is determined whether the trackside equipment is installed at the preset mileage;

[0048] By compensating the mileage data, it is ensured that the scanner can face the target device at the compensated mileage data point, thus achieving accurate data collection;

[0049] By processing the scan data identical to the mileage data corresponding to the optimal image, the separation distance between the wayside equipment and the track is obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a schematic structural diagram of embodiment 1 of the present invention;

[0051] Figure 2 This is a general flow chart of Embodiment 1 of the present invention;

[0052] Figure 3 This is a schematic diagram of a frame selection for model training according to the first embodiment of the present invention;

[0053] Figure 4 Schematic diagram of the process of calculating the clearance distance according to the first embodiment of the present invention;

[0054] Figure 5 This is a schematic diagram of a process for verifying the installation position of trackside equipment according to the first embodiment of the present invention;

[0055] Figure 6 This is a flow chart of calculating the distance from the trackside equipment to the track according to the first embodiment of the present invention;

[0056] Figure 7 Schematic diagram of the camera position in the scanned image according to the first embodiment of the present invention;

[0057] Figure 8 Schematic diagram of the field of view of a camera in a scanned image according to the first embodiment of the present invention;

[0058] Figure 9 It is the horizontal axis distance from the center point of the framed area in the scanned image to the center point of the scanned image in the first embodiment of the present invention. DETAILED DESCRIPTION

[0059] The following describes the embodiments of the present invention through specific examples. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments. The details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other unless they conflict.

[0060] It should be noted that the illustrations provided in the following embodiments are merely schematic illustrations of the basic concept of the present invention. Therefore, the illustrations only show components related to the present invention and are not drawn according to the number, shape, and size of components in actual implementation. In actual implementation, the type, quantity, and proportion of each component may be changed arbitrarily, and the component layout may also be more complex.

[0061] Example 1:

[0062] like Figure 1 As shown, a device for automatically measuring the clearances of trackside equipment includes a measuring vehicle, a laser scanner, a camera, a lens, an encoder, and a computing device. The laser scanner, camera, and encoder are all mounted on the measuring vehicle, while the lens is mounted on the camera. The laser scanner, camera, and encoder are each connected to the computing device for communication. The measuring vehicle can be positioned on the track to be inspected and travel along the track.

[0063] The laser scanner is positioned in the middle of the measuring vehicle, scanning both sides of the vehicle. In this example, the laser scanner's scanning direction is perpendicular to the vehicle's direction of travel. The laser scanner is used to scan trackside equipment and measure the distance from the equipment to the track.

[0064] The camera, installed at any location on the measuring vehicle, captures images of the trackside as the vehicle travels. Software then matches and calculates the image data with the scanner data to verify the installation position of the trackside equipment and calculate the clearance distance (i.e., the clearance distance) of the trackside equipment. In this example, the camera's direction of capture is always perpendicular to the vehicle's direction of travel.

[0065] The encoder is mounted on the wheels of the measuring vehicle and is used to record the travel of the measuring vehicle and assist in locating the mileage position during the detection of the trackside equipment.

[0066] The computing device may be a service terminal for storing and computing information data collected by a camera, a laser scanner, and an encoder.

[0067] During the implementation process, the measuring vehicle is set on the track and moves along the track. The relevant position information of the trackside equipment is obtained through laser scanners and cameras, and the travel distance of the measuring vehicle is obtained through encoders. Then, the limit distance of the trackside equipment and the specific mileage position of the trackside equipment relative to the track are obtained through the computing device, thereby completing the detection of the trackside equipment.

[0068] like Figure 2 As shown, a method for rapid automatic measurement of trackside equipment clearance includes the following steps:

[0069] Step 1: The measuring vehicle receives a start signal and moves forward along the track. Simultaneously, the camera collects image information on both sides of the track, the laser scanner collects scan data, and the encoder collects coded data. The collected image data, scan data, and coded data are transmitted to the computing device in real time.

[0070] Step 2: The computing device receives the image data and reads the coded data received simultaneously, converts the coded data into mileage data, and stores the image data and the mileage data in association with each other. The computing device receives the scanned data and reads the coded data received simultaneously, converts the coded data into mileage data, and stores the scanned data and the mileage data in association with each other.

[0071] Step 3: The measuring vehicle receives a stop signal and stops moving on the track; the computing device begins processing the stored image data, scan data, and mileage data;

[0072] Step 4: The computing device completes image detection based on the received image data and the corresponding mileage data;

[0073] Step 5: After image detection is completed, the computing device processes the scanned data based on the received scanned data and the corresponding mileage data to obtain the distance data from the wayside equipment to the track;

[0074] Step 6: Output the information of the trackside equipment and end the step.

[0075] The start signal in step 1 and the stop signal in step 3 are both remote control signals.

[0076] like Figure 3 、 4 As shown, the image detection in step 4 includes two parts: one is the calculation of the clearance distance between the trackside equipment, and the other is the verification of the installation position of the trackside equipment. The calculation of the clearance distance includes the following steps:

[0077] Step 41: Receive input image data based on the model obtained through deep learning training, traverse the image data, read each frame of the image in sequence, and identify and select the target device in each frame of the image;

[0078] Step 42: Read the selected area in each frame image, and determine whether it is the best image based on the distance between the center point of the selected area and the center point of the corresponding image; determine the mileage corresponding to the target device based on the mileage data corresponding to the best image;

[0079] Step 43: Obtain the mileage corresponding to all track equipment, sort the track equipment according to mileage, and calculate the clearance distance between adjacent equipment in mileage;

[0080] Step 44: Arrange and output the limit distances, and end the step.

[0081] The deep learning-based model in step 41 adopts the yolov3 Darknet53 basic network model. When training the model, it is first necessary to collect a batch of images including the target device, and perform frame selection processing on the image, use the smallest frame selection area to select the target device in the image, and input the coordinates of the vertices on any diagonal of the frame selection area. During training, the network model automatically learns the relative relationship between the frame selection area and the image pixel distribution within the frame selection range. After the training is completed, the result also outputs the frame selection area containing all the pixel points of the identified target, including the coordinates of the two vertices on any diagonal of the output frame selection area. The training of the model includes the following steps:

[0082] Step 411: Input a training sample image and annotations, where the annotations include the coordinates of any diagonal vertex of the selected area;

[0083] Step 412: Normalize the images in the training set and scale them to an integer multiple of 32;

[0084] Step 413: setting the width and height of the initial candidate box in the boundary regression module;

[0085] Step 414: training the network model using the pre-processed training set images;

[0086] Step 415: Input the image to be detected into the trained network model to detect the target in the image, and output the detection results of the small target category and position;

[0087] Step 416: Filter the detection results according to the preset category confidence threshold and overlap threshold to obtain the final detection result;

[0088] Step 417: Generate a prediction model and export it. After the model is exported, it is called for inference when the image is subsequently detected. After the model is inferred, the detection results of the target category and position in the image are output, and the step ends.

[0089] The selection of the best image in step 42 includes the following steps:

[0090] Step 421: Read the selected area in the image and obtain the coordinates of two vertices on any diagonal line of the selected area, which are expressed as (Sx, Sy) and (Ex, Ey) respectively. When reading the selected area of the image for the first time, read the selected area of the first frame of the image.

[0091] Step 422: Calculate the midpoint coordinates of the two vertex coordinates (Sx, Sy) and (Ex, Ey), where the midpoint coordinates are expressed as (Cx, Cy);

[0092] Step 423: Obtain the coordinates of the center point of the image, and obtain the horizontal coordinate distance DistanceCW between the coordinates of the midpoint of the selected area and the coordinates of the center point of the image;

[0093] Step 424: Determine whether all images have been traversed; if so, proceed to step 425; otherwise, return to step 421;

[0094] Step 425: traverse all images, select an image frame with the smallest horizontal coordinate distance DistanceCW as the best image, and end the step.

[0095] In step 423 , the distance between the horizontal coordinates is expressed as DistanceCW=|Cx−Width / 2|, where Width represents the length of the image in the horizontal coordinate direction.

[0096] In step 425, because the camera's viewing axis is perpendicular to the direction of travel of the measuring vehicle in this example, and the measuring vehicle moves along the track, the mileage data of the target device's selected area closest to the center point of the image is the mileage corresponding to the trackside device.

[0097] like Figure 5 As shown, the verification of the installation position of the trackside equipment includes the following steps:

[0098] Step 41a: Obtain the trackside equipment and corresponding mileage data identified during the calculation of the clearance distance; obtain a preset trackside equipment installation data table;

[0099] Step 42a: Label the mileage data and sequentially read the IDs of the identified trackside equipment and the corresponding mileage data L1; sequentially read the IDs of the trackside equipment and the corresponding mileage data L2 in the installation data table according to the serial number; the IDs of the trackside equipment include signal lights and speedometers;

[0100] Step 43a: Determine whether the trackside equipment ID obtained by identification is consistent with the trackside equipment ID of the corresponding order in the installation data table; if the ID is consistent, write "yes" in the corresponding position in the installation data table; otherwise, write "no";

[0101] Step 44a: Calculate the absolute value of the difference between the mileage data L1 obtained by the identification calculation of the corresponding order and the mileage data L2 in the installation data table, and write the calculation result to the corresponding position in the installation data table;

[0102] Step 45a: Complete the comparative calculation of the last identified trackside equipment number and the last identified trackside equipment number in the installation data table; output the installation data table and end the step.

[0103] like Figure 6 As shown, the processing of the scanned data in step 5 includes the following steps:

[0104] Step 51: Read the image data of the best image acquired by the camera and the corresponding mileage data; and sort and number the acquired image data according to the mileage data;

[0105] Step 52: Compensating the mileage data corresponding to the image data of the optimal image to obtain compensated mileage data;

[0106] Step 53: Read a corresponding scan data according to the compensated mileage of the best image; when reading the scan data for the first time, read it according to the compensated mileage data of the first labeled image data; and read the scan data in sequence according to the compensated mileage data;

[0107] Step 54: Determine the position of the camera in the scanned image based on the height difference between the camera and the scanner and the left and right deviation of the camera and the scanner perpendicular to the direction of travel of the measuring vehicle;

[0108] Step 55: Calculate the field of view of the camera in the scanned image;

[0109] Step 56: extracting contour points of the cross section of the trackside equipment according to the field of view of the camera within the scanned data;

[0110] Step 57: Fit the smallest circumscribed rectangle based on the scanned contour points, and calculate the center point of the circumscribed rectangle;

[0111] Step 58: Calculate the difference in the horizontal coordinates between the center point of the circumscribed rectangle and the center point of the scanned image as the distance between the trackside equipment and the track in the scanned image; scale the distance in the scanned image according to the scan ratio to obtain the actual distance and output it;

[0112] Step 59: Complete the processing of the scan data corresponding to the compensated mileage data of the last labeled image data, and end the step.

[0113] In step 52, due to the misalignment between the camera and scanner's installation positions, the mileage data will deviate. Therefore, compensation is performed on the scanned image data to improve measurement accuracy. The direction of the measuring vehicle's travel is considered positive, and the compensation value is the distance between the scanner and camera installation positions relative to the vehicle's direction of travel. Compensated mileage = mileage - misalignment. Note that if the scanner is installed in front of the camera, the misalignment is positive; if it is installed behind the camera, the misalignment is negative. This misalignment ensures that the scanner is facing the target device when the mileage compensation is performed.

[0114] like Figure 7 As shown, the height difference and left-right deviation between the camera and the scanner in step 54 need to be converted to a scan ratio when converted to the scanned image. The height difference ch in the scanned image = actual height difference * scan ratio. Similarly, the left-right deviation dh in the scanned image = actual left-right deviation * scan ratio. If the coordinates of the center point of the scanned image are represented as (Rx, Ry), the position coordinates of the camera in the scanned image are represented as (Rx-dh, Ry-ch). In this example, the left-right deviation between the camera and the scanner is 0.

[0115] like Figure 8 As shown, in step 55, the coordinate position of the camera in the scanned image is determined based on step 54, with L representing the scanner's scanning distance and H representing the camera's maximum shooting range in the scanned image. The camera's coordinate position (Rx-dh, Ry-ch) is used as vertex a, the camera's target width is h, and the lens's focal length is f. H = hL / f is calculated, and the coordinates of vertex b are (Rx-dh+L, Ry-ch+H / 2), and the coordinates of vertex c are (Rx-dh+L, Ry-ch-H / 2). A triangular area is drawn with vertices a, b, and c, and this triangular area represents the camera's field of view in the scanned image.

[0116] In step 56, the intersection points of the field of view range obtained in step 55 and the scanning points in the scanned image are calculated, and a set of all intersection points is output as a cross-sectional contour point set of the target device.

[0117] like Figure 9 As shown, the distance X from the trackside equipment to the track in step 58 is d The calculation of is shown as follows:

[0118]

[0119] Wherein x represents the horizontal coordinate of the center point of the circumscribed rectangle in step 57. Taking the center point of the circumscribed rectangle as the position of the target device can eliminate the influence of different areas of different trackside devices in the scanned image.

[0120] Actual distance X td , denoted as X td =X d / S, where S represents the scanning ratio of the scanner.

[0121] During the implementation process, a camera is set up to acquire an image, and a pre-trained model is used to identify the target equipment area in the framed image. The position of the framed area in the image is used to determine whether it is the best image. For the best image, the corresponding mileage data is recorded as the actual mileage of the corresponding target equipment. After completing the mileage calculation of all trackside equipment, the limit distance of adjacent trackside equipment is calculated to determine whether it meets the engineering requirements; the ID of the target equipment identified by the best image is compared with the preset table in turn to determine whether there is an error in the sequential installation of the trackside equipment; the mileage corresponding to the best image is compared with the preset table in turn to determine whether the trackside equipment is installed at the preset mileage; the mileage data is compensated to ensure that the scanner can face the target equipment at the compensated mileage data; the scanning data with the same mileage data as that corresponding to the best image is processed to obtain the interval distance between the trackside equipment and the track.

[0122] The above description is merely a specific example of the present invention and does not constitute any limitation thereto. It is apparent to those skilled in the art, after understanding the content and principles of the present invention, that various modifications and alterations in form and detail may be made without departing from the principles and structure of the present invention. However, such modifications and alterations based on the concepts of the present invention remain within the scope of protection of the claims of the present invention.

Claims

1. A device for automatically measuring the clearance of trackside equipment, characterized in that: It includes a measuring vehicle, a laser scanner, a camera, a lens, an encoder and a computing device; wherein the laser scanner, the camera and the encoder are all arranged on the measuring vehicle, and the lens is arranged on the camera; the laser scanner, the camera and the encoder are respectively communicated with the computing device, and the computing device is used to calculate the limit distance between the trackside equipment. The computing device receives the input image data based on the model obtained by deep learning training, traverses the image data, reads each frame of the image in sequence, and identifies and selects the target device in each frame of the image. The computing device reads the framed area in each frame of the image, and determines whether it is the best image based on the distance between the center point of the framed area and the corresponding center point of the image; determines the mileage corresponding to the target device based on the corresponding mileage data of the best image, and the computing device determines the mileage corresponding to the target device based on the mileage data corresponding to the best image. The mileage corresponding to all track equipment is obtained, the track equipment is sorted according to the mileage, the limit distances between equipment adjacent in mileage are calculated, the limit distances are sorted and output in a table, the laser scanner is set in the middle position of the measuring vehicle, wherein the scanning direction of the laser scanner is on both sides of the measuring vehicle; the scanning direction of the laser scanner is always perpendicular to the travel direction of the measuring vehicle; the encoder is set on the wheel of the measuring vehicle; the computing device is a service terminal, the camera is used to obtain images of the measuring vehicle on the track, and the shooting direction of the camera is always perpendicular to the travel direction of the measuring vehicle. The measuring vehicle can be set on the track to be detected and move along the line of the track. The laser scanner is used to scan the trackside equipment and detect and calculate the distance from the trackside equipment to the track.

2. The device for automatically measuring the clearance of trackside equipment according to claim 1, characterized in that: The camera is arranged at any position on the measuring vehicle.

3. The device for automatically measuring the clearance of trackside equipment according to claim 1, characterized in that: The encoder is used to record the travel of the measuring vehicle and assist in locating the mileage position during the inspection of the trackside equipment.

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