A method for identifying foreign matter intruding into a linear section of an urban rail transit

Abstract

The application discloses a kind of urban rail transit straight section line foreign matter invasion identification method, by setting up image detection device and radar detection device at the end of urban rail transit locomotive, for data acquisition;Then utilize train-mounted computing device to identify detection in real-time field of view image in acquisition data, obtain field of view image feature detection result;Real-time radar data is identified monitoring, and current radar detection foreign matter position is obtained;According to data synchronization frequency, the current radar detection object position after coordinate conversion and field of view image feature detection result are carried out target position fusion, and train-mounted foreign matter invasion detection result is obtained;Through train-ground wireless communication system, train-mounted invasion detection result is uploaded to foreign matter invasion detection center system, and real-time train position information K and radar detection object distance Δx are summed, and foreign matter position information K+Δx is calculated, combined with foreign matter invasion detection result, mapping alarm is carried out on the line diagram of foreign matter invasion detection center system.

Description

A method for identifying foreign object encroachment on straight sections of urban rail transit lines Technical Field

[0001] This invention belongs to the field of rail transit safety technology, and more specifically, relates to a method for identifying foreign object encroachment on straight sections of urban rail transit lines. Background Technology

[0002] Foreign objects specifically refer to any object (such as tools, materials, equipment parts, foreign items, etc.) or non-rail vehicles / machinery entering the clearance limits of a rail transit line, exceeding the safe operating space, and potentially causing a collision, derailment, or other accidents. In recent years, urban rail transit accidents caused by foreign object intrusion have been frequent.

[0003] Elevated sections of urban rail transit lines are susceptible to foreign objects being blown onto the tracks by the wind (such as packaging bags, billboards, tree branches, etc.), and objects falling from construction sites or vehicles outside the tracks can intrude into the track safety protection zone (such as construction sites not being properly enclosed). Tunnels and vehicle depots are vulnerable to threats to train safety due to uncleaned construction tools or materials, and foreign objects left behind by worker errors. Trains travel at high speeds on straight sections, and without efficient and accurate methods for detecting and identifying foreign object intrusions, it is difficult to ensure train safety. Existing urban rail transit lines mostly rely on ground-mounted millimeter-wave radar, lidar, or visual and acoustic sensors for obstacle intrusion detection. While this reduces the occurrence of missed obstacles, its effective detection distance is short, and blind spots exist, resulting in insufficient accuracy and efficiency in obstacle identification and location. Considering the installation of ground-mounted foreign object intrusion detection devices across the entire line would significantly increase construction costs. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method for identifying foreign object encroachment on straight sections of urban rail transit lines. This method solves the problem that the existing detection of foreign object encroachment on urban rail transit lines is not comprehensive and accurate enough, while also improving the economic efficiency of the system.

[0005] To achieve the above-mentioned objectives, the present invention provides a method for identifying foreign object encroachment on urban rail transit lines, characterized by comprising the following steps:

[0006] (1) Set up a foreign object intrusion detection center system for straight sections in the urban rail transit line control center, and set up a dedicated interface for train position information between the foreign object intrusion detection center system and the train control system to obtain train position information K in real time.

[0007] (2) The image detection device and radar detection device are set at the end of the locomotive to capture real-time visual images of the train's direction of travel and to detect real-time radar data of the train's direction of travel on straight sections.

[0008] (3) Use the visual field image foreign object detection neural network in the train on-board computing device to identify and detect real-time visual field images, and obtain visual field image feature detection results;

[0009] (4) Based on the Kalman filter algorithm, the train onboard computing device is used to identify and monitor the real-time radar data to obtain the distance Δx between the radar-detected object and the end of the locomotive. Then, the vehicle-to-ground wireless communication system is used to upload the distance Δx between the radar-detected object and the foreign object intrusion detection center system in real time.

[0010] (5) Based on the data synchronization frequency, the train onboard computing device is used to fuse the current radar-detected object position and the field-of-view image feature detection results to obtain the foreign object intrusion detection results.

[0011] (6) The foreign object intrusion detection center system creates a central system route map based on the mileage characteristics of the rail transit line, then maps the train's travel position onto the route map, establishes a real-time train position display, and sums the train position information K and the distance Δx of the radar-detected object in real time to calculate the foreign object position information, and then maps and alarms on the route map of the foreign object intrusion detection center system.

[0012] The objective of this invention is achieved as follows:

[0013] This invention provides a method for identifying foreign object encroachment on straight sections of urban rail transit lines. First, a foreign object encroachment detection system for straight sections of lines is established. This system consists of a foreign object encroachment detection center system and an on-board foreign object encroachment detection subsystem. The on-board foreign object encroachment detection subsystem includes: an image detection device, a radar detection device, and a train-mounted computing device. Image detection devices and radar detection devices are installed at the ends of urban rail transit locomotives for data acquisition. Then, the onboard computing device of the train is used to identify and detect the real-time field-of-view images in the acquired data to obtain the field-of-view image feature detection results. The real-time radar data is identified and monitored to obtain the current location of the foreign object detected by the radar. According to the data synchronization frequency, the current location of the object detected by the radar after coordinate transformation and the field-of-view image feature detection results are fused to obtain the onboard foreign object intrusion detection results. The onboard intrusion detection results are uploaded to the foreign object intrusion detection center system through the existing vehicle-to-ground wireless communication system of urban rail transit. The foreign object intrusion detection center system sums the train position information K and the distance Δx of the radar detected object in real time to calculate the foreign object position information K+Δx. Combined with the foreign object intrusion detection results, a mapping alarm is generated on the line map of the foreign object intrusion detection center system.

[0014] Meanwhile, the method for identifying foreign object encroachment on straight sections of urban rail transit lines according to the present invention also has the following beneficial effects:

[0015] (1) This invention constructs an urban rail transit foreign object intrusion detection system by combining vehicle-mounted and ground-based methods. The main application scenario of this system is on straight sections of urban rail transit lines. By using train-based active detection, it effectively reduces the number of ground-based foreign object detection devices installed, thereby saving on equipment investment along the line. It proposes an economical and efficient detection method for foreign object detection on straight sections of urban rail transit lines.

[0016] (2) This invention utilizes a field-of-view image foreign object detection neural network to identify and detect real-time field-of-view images, a Kalman filter algorithm to process radar data, and, based on the data synchronization frequency, fuses the radar-detected object position after coordinate transformation with the field-of-view image feature detection results to improve the accuracy and precision of foreign object detection.

[0017] (3) This invention utilizes the existing vehicle-to-ground wireless communication system and train control system of urban rail transit to simulate and construct a track map. Alarm information and train location information can be mapped on the track map, realizing the visual management of foreign object intrusion. This makes it easier to intuitively understand the situation of foreign object intrusion, make quick decisions and take corresponding countermeasures, and effectively improve the emergency response speed and management efficiency. Attached Figure Description

[0018] Figure 1 is a flowchart of a method for identifying foreign object encroachment on a straight section of urban rail transit line according to the present invention;

[0019] Figure 2 is a block diagram of a foreign object intrusion monitoring system for straight sections of urban rail transit lines according to the present invention. Detailed Implementation

[0020] The specific embodiments of the present invention will now be described with reference to the accompanying drawings to enable those skilled in the art to better understand the invention. It should be particularly noted that in the following description, detailed descriptions of known functions and designs that might obscure the main content of the invention will be omitted here.

[0021] Example

[0022] Figure 1 is a flowchart of a method for identifying foreign object encroachment on a straight section of urban rail transit line according to the present invention.

[0023] In this embodiment, as shown in Figure 1, the method for identifying foreign object encroachment on a straight section of urban rail transit line according to the present invention includes the following steps:

[0024] (1) Set up a foreign object intrusion detection center system for straight sections in the urban rail transit line control center, and set up a dedicated interface for train position information between the foreign object intrusion detection center system and the train control system to obtain train position information K in real time.

[0025] (1.1) The existing train control system of urban rail transit contains real-time train location information. By setting up a dedicated interface for train location information in the rail transit line control center, the train location information is obtained and transmitted to the foreign object intrusion detection center system to obtain the corresponding real-time train location system K.

[0026] (1.2) Create a central system route map based on the mileage characteristics of rail transit lines. The train's position can be mapped onto the route map to establish a real-time train position display.

[0027] (2) The image detection device and radar detection device are set at the end of the locomotive to capture real-time visual images of the train's direction of travel and to detect real-time radar data of the train's direction of travel on straight sections.

[0028] In this embodiment, the image detection device uses a visual sensor, and the radar detection device uses millimeter-wave radar; the process of acquiring real-time field-of-view images and real-time radar data is as follows:

[0029] (2.1) Set the image detection device at the center of the locomotive end and use the image detection device to capture real-time visual images of the train in the direction of travel on the straight section;

[0030] (2.2) The radar detection device is placed above the image detection device so that the radar detection device is located on the central axis of the track line; then, a rectangular coordinate system is constructed with the radar detection device as the origin, the train travel direction as the longitudinal coordinate axis, and the direction perpendicular to the track line as the transverse coordinate axis.

[0031] In this embodiment, detecting an object means that the radar signal continuously detects an object for more than a preset detection threshold. If the number of times an object is detected is less than the preset detection threshold, the object is considered lost or the detection fails, and the process does not proceed to the next step of processing the detection data. Only when the number of times an object is detected exceeds the preset detection threshold will the next step of processing the detection data begin. In this embodiment, the radar detection device uses a triangular wave modulated signal for foreign object detection.

[0032] (2.3) Based on the difference frequency of the Doppler frequency shift, the radar detection device is used to detect the distance, angle and relative speed of objects in the direction of train travel until the object is detected.

[0033] (2.4) Number the detected objects and combine the object numbers with the corresponding distance, angle and relative speed to obtain real-time radar data of the train's direction of travel.

[0034] (3) Use the visual field image foreign object detection neural network in the train on-board computing device to identify and detect real-time visual field images, and obtain visual field image feature detection results;

[0035] (3.1) Obtain several field-view images with or without foreign objects, and perform weighted average grayscale processing and Gaussian filtering processing on the field-view images in sequence to obtain the preprocessed field-view images.

[0036] In this embodiment, the model for weighted average grayscale processing is as follows:

[0037] Gray(a,b)=(ω R R(a,b)+ω G G(a,b)+ω B B(a,b))

[0038] Where Gray(a,b) represents the gray value of the pixel at position (a,b) in the visual field image, ω R ω G ω B R(a,b), G(a,b), and B(a,b) represent the weight coefficients of the red, green, and blue channels, respectively. R(a,b), G(a,b), and B(a,b) represent the red, green, and blue channel pixel values ​​of the pixel at position (a,b) in the visual field image, respectively. a is the horizontal coordinate of the visual field image, and b is the vertical coordinate of the visual field image.

[0039] In this embodiment, the model for Gaussian filtering is as follows:

[0040]

[0041] Where g(a,b) represents the Gaussian filtering result of the pixel at position (a,b) in the grayscale image of the field of view, e represents the exponential basis constant, and σ represents the standard deviation of the Gaussian distribution;

[0042] (3.2) Perform histogram equalization and contrast enhancement on the preprocessed visual field image to obtain the enhanced visual field image;

[0043] (3.3) Use the Sobel operator to perform edge detection on the enhanced field-view image to obtain the edge features of the field-view image;

[0044] In this embodiment, the gradient of the Gaussian-smoothed field of view image in the horizontal direction is calculated using a Sobel operator convolution kernel in the horizontal direction, and the gradient of the Gaussian-smoothed field of view image in the vertical direction is calculated using a Sobel operator convolution kernel in the vertical direction. Then, the edge features of the field of view image are obtained by calculating the magnitude of the gradients in the two directions.

[0045] (3.4) Based on the edge features of the field of view image, Hough transform is used to detect the straight line of the image trajectory and obtain the straight line trajectory features of the field of view image;

[0046] In this embodiment, the Hough transform line detection method is as follows: the field of view image is converted to polar coordinate parameter space and discretized into a grid; based on the edge features of the field of view image, all possible lines corresponding to the pixels with edge features in the field of view image in the parameter space are calculated respectively, and the parameter positions of the lines are accumulated and counted; the pixels whose accumulated count results exceed a set threshold are found, and the line parameters in the field of view image are obtained accordingly, thereby obtaining the orbit features of the field of view image.

[0047] (3.5) Set the track foreign object intrusion detection limit;

[0048]

[0049] Among them, u ar (v i' ) indicates the right-hand position of the foreign object intrusion detection limit, u ri' U represents a characteristic point on the right side of the subway line. li' U represents a characteristic point on the left side of the subway line. al (v i' ) indicates the left position point of the foreign object detection limit on the track, v i' This represents the horizontal coordinate of the i'th track line;

[0050] (3.6) Replace the standard convolutions in the backbone network and feature fusion network of the traditional yolov11 with depthwise separable convolutions. Then, use a top-down path aggregation mechanism and lateral connections to strengthen the image feature extraction and analysis between the layers of the feature fusion network. Use the C2f-Faster-EMA feature fusion strategy to fuse image feature information at different scales to transmit to the neck network, and obtain the improved yolov11 neural network.

[0051] In this embodiment, by replacing the standard convolutions in the backbone and feature fusion networks of YOLOv11 with depthwise separable convolutions, which include both depthwise and pointwise convolutions, feature extraction is performed on each input channel before channel fusion, which can significantly reduce parameters and computational cost without affecting accuracy. In addition, the use of an automatic downward path aggregation mechanism and lateral connections can reduce additional computational overhead. The C2f-Faster-EMA feature fusion strategy can better fuse information at different scales while controlling computational cost.

[0052] (3.7) Based on the foreign object intrusion dataset of the track line, and based on the straight track features of the field of view image and the foreign object intrusion detection limit of the track, the improved yolov11 neural network is trained for foreign object detection and recognition to obtain the field of view image foreign object detection neural network.

[0053] In this embodiment, the orbital foreign object intrusion detection boundary can efficiently determine the region of interest during foreign object detection and recognition training for monitoring and recognition training. When training the improved YOLOv11 neural network, half-precision floating-point numbers can be used for gradient updates to reduce memory consumption and improve GPU utilization, thereby accelerating the training speed without affecting convergence.

[0054] (3.8) Use the field of view image foreign object detection neural network to detect real-time field of view images and obtain the field of view image feature detection results.

[0055] (4) Based on the Kalman filter algorithm, the train onboard computing device is used to identify and monitor the real-time radar data to obtain the distance Δx between the radar-detected object and the end of the locomotive. Then, the vehicle-to-ground wireless communication system is used to upload the distance Δx between the radar-detected object and the foreign object intrusion detection center system in real time.

[0056] (4.1) Set a continuous monitoring time window for the real-time radar data of the train's straight section of travel direction, and count the number of times an object is successfully detected within the continuous monitoring time window as the object detection count.

[0057] (4.2) If the number of object detections is less than the first detection threshold, the track line status is determined to be the state of foreign object intrusion disappearance, and return to step (4.1); otherwise, proceed to step (4.3).

[0058] (4.3) If the number of object detections is greater than the second detection threshold, the track line is determined to be in a state of continuous foreign object intrusion and proceeds to step (4.4); otherwise, proceed directly to step (4.1).

[0059] (4.4) Construct a foreign object intrusion state space model based on real-time radar data;

[0060]

[0061] v kτ =v k(τ-1) +a k(τ-1) ×Δt

[0062] Where, p kτ Let v represent the position of the k-th object at time τ. kτ Let a represent the velocity of the k-th object at time τ. k(τ-1) Let Δt represent the acceleration of the k-th object at time τ-1, and let Δt represent the length of the time interval between adjacent objects.

[0063] (4.5) Based on the foreign object intrusion state space model, construct the foreign object intrusion state equation;

[0064]

[0065] Where, ω τ-1 z represents the process noise at time τ-1. τ H represents the position measurement vector at time τ. τ-1 S represents the observation matrix at time τ-1. τ-1 The measurement noise at time τ-1 is represented.

[0066] (4.6) Based on the Kalman filter algorithm, the foreign object intrusion state equation is updated in time and state to obtain the current distance Δx between the radar-detected object and the locomotive end;

[0067] (4.7) Utilize the existing vehicle-to-ground wireless communication system of urban rail transit to upload the distance Δx of radar-detected objects to the central system in real time.

[0068] In this embodiment, both the train control system and the vehicle-to-ground wireless communication system are existing systems in urban rail transit.

[0069] (5) Based on the data synchronization frequency, the train onboard computing device is used to fuse the current radar-detected object position and the field-of-view image feature detection results to obtain the foreign object intrusion detection results.

[0070] (5.1) The data synchronization frequency is obtained based on the detection frequency of the radar detection device and the detection frequency of the image detection device;

[0071] (5.2) Obtain the transformation model between radar coordinate system and world coordinate system, the transformation model between pixel coordinate system and camera coordinate system, and the transformation model between camera coordinate system and world coordinate system;

[0072] (5.3) Based on the transformation model between the radar coordinate system and the world coordinate system, the current radar detection object position is transformed into the current radar detection position in the world coordinate system;

[0073] (5.4) Based on the transformation model between pixel coordinate system and camera coordinate system and the transformation model between camera coordinate system and world coordinate system, the field image feature detection results are converted into image detection results in world coordinate system;

[0074] (5.5) Based on the data synchronization frequency, the target position is fused with the current radar detection position and image detection result at the same time according to the following target association model to obtain the vehicle intrusion detection result;

[0075] The target association model is as follows:

[0076]

[0077] Where Δd represents the distance between the current radar detection position and the center point of the object in the image detection result, O R Indicates the current radar detection location, O C The image detection result indicates the location of the object's center point, l1 represents the first correlation threshold, IOU represents the intersection-union ratio of the radar and image-detected object regions, IOU represents the contour region of the object detected by the radar, and S represents the object's contour region detected by the radar. C l1 represents the contour region of the object being detected in the image, and l2 represents the second correlation threshold.

[0078] In this embodiment, if the distance between the current radar detection position and the center point of the object in the image detection result is less than the first correlation threshold, and the cross-union ratio of the radar and the object detection area in the image is greater than the second correlation threshold, the object fused at the target position can be effectively used as the vehicle intrusion detection result.

[0079] (6) The foreign object intrusion detection center system sums the train position information K and the distance Δx of the radar-detected object in real time to calculate the foreign object position information, and then maps and alarms it on the line map of the foreign object intrusion detection center system.

[0080] (6.1) Upload the vehicle-mounted intrusion detection results to the foreign object intrusion detection center system through the existing vehicle-to-ground wireless communication system of urban rail transit;

[0081] (6.2) The foreign object intrusion detection center system sums the train position information K and the distance Δx of the radar-detected object in real time to calculate the foreign object position information K+Δx, and maps and alarms on the center system line map in combination with the foreign object intrusion detection results.

[0082] As shown in Figure 2, the present invention provides a detection system for identifying foreign object encroachment on straight sections of urban rail transit lines based on the above-mentioned method for identifying foreign object encroachment on straight sections of urban rail transit lines, namely, a foreign object encroachment detection system for straight sections of urban rail transit lines; the foreign object encroachment detection system for straight sections of urban rail transit lines includes: a foreign object encroachment detection center system set up in the urban rail transit line control center and an on-board foreign object encroachment detection subsystem set up at the locomotive of the train.

[0083] The vehicle-mounted foreign object intrusion detection subsystem can detect and identify foreign objects by combining images and radar. It uses the train's onboard computing device to process the detection results of the intruding foreign objects and encapsulates them into an upload information data packet. Then, it sends the data packet information to the foreign object intrusion detection center system through the existing vehicle-to-ground wireless communication system of rail transit.

[0084] The foreign object intrusion detection center system creates a simulated route map based on the track mileage characteristics. It has a dedicated interface with the existing train control system for real-time train position information acquisition. The system also analyzes data packets uploaded by the train's onboard computing device in real time, and calculates the location of intruding foreign objects through summation. This system enables visualized management of foreign object intrusion and train position mapping alarms on the route map.

[0085] This invention comprehensively utilizes technologies such as fusion monitoring of vehicle vision and radar, neural network recognition, Kalman filtering algorithm, and visualization management to comprehensively improve the efficiency of foreign object intrusion detection on straight sections of urban rail transit lines. It also enhances the real-time performance and accuracy of foreign object intrusion detection, intelligent fusion and target positioning capabilities, as well as visualization management and rapid response capabilities, providing strong protection for train operation safety.

[0086] Although the illustrative specific embodiments of the present invention have been described above to enable those skilled in the art to understand the invention, it should be understood that the invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the invention as defined and determined by the appended claims, and all inventions utilizing the concept of the present invention are protected.

Claims

1. A method for identifying foreign object encroachment on straight sections of urban rail transit lines, characterized in that, The steps include: (1) Setting up a foreign object intrusion detection center system for straight sections in the urban rail transit line control center, setting up a dedicated interface for train position information between the foreign object intrusion detection center system and the train control system, and obtaining train position information K in real time; (2) Setting up image detection devices and radar detection devices at the end of the locomotive to capture real-time visual images of the train's direction of travel and to detect real-time radar data of the train in the direction of travel on straight sections. (3) Use the foreign object detection neural network in the train's onboard computing device to identify and detect real-time field images, and obtain the field image feature detection results; (3.1) Obtain several field-view images with or without foreign objects, and perform weighted average grayscale processing and Gaussian filtering processing on the field-view images in sequence to obtain preprocessed field-view images; (3.2) Perform histogram equalization processing and contrast enhancement processing on the preprocessed field-view images to obtain enhanced field-view images; (3.3) Use the Sobel operator to perform edge detection on the enhanced field-view images to obtain the edge features of the field-view images; (3.4) Based on the edge features of the field of view image, Hough transform is used to detect the straight line of the image trajectory to obtain the straight line trajectory features of the field of view image; (3.5) Set the track foreign object intrusion detection limit; ;in, This indicates the right-hand position of the foreign object intrusion detection clearance. This indicates the characteristic points of the right track of the subway line. This indicates the characteristic points of the left track of the subway line. This indicates the left-hand position of the foreign object detection clearance on the track. Indicates the first (3.6) Replace the standard convolutions in the backbone network and feature fusion network of the traditional yolov11 with depth-separable convolutions. Then, use a top-down path aggregation mechanism and lateral connections to strengthen the image feature extraction and analysis between the layers of the feature fusion network. Use the C2f-Faster-EMA feature fusion strategy to fuse image feature information of different scales to transmit to the neck network to obtain the improved yolov11 neural network. (3.7) Based on the urban rail transit track line foreign object intrusion dataset, based on the straight track features of the field of view image and the track foreign object intrusion detection limit, train the improved yolov11 neural network for foreign object detection and identification to obtain the field of view image foreign object detection neural network. (3.8) Use the field of view image foreign object detection neural network to detect real-time field of view images to obtain the field of view image feature detection results. (4) Based on the Kalman filter algorithm, use the train on-board computing device to identify and monitor real-time radar data to obtain the distance between the current radar detected object and the locomotive end. Then, the vehicle-to-ground wireless communication system is used to upload the distance of the radar-detected object to the foreign object intrusion detection center system in real time. (5) Based on the data synchronization frequency, the train onboard computing device is used to fuse the current radar-detected object position and the field-of-view image feature detection results to obtain the foreign object intrusion detection result; (6) The foreign object intrusion detection center system creates a center system route map based on the mileage characteristics of the rail transit line, then maps the train's travel position onto the route map, establishes a real-time train position display, and monitors the train position information K and the distance between the radar-detected object and the target position in real time. The summation is performed to calculate the location information of the foreign object, and then the information is mapped and alarmed on the circuit diagram of the foreign object intrusion detection center system.

2. The method for identifying foreign object encroachment on straight sections of urban rail transit lines according to claim 1, characterized in that, The step (1) includes the following steps: (1.1) Set up a dedicated interface for train position information in the rail transit line control center, obtain train position information from the train control system, and transmit the train position information to the foreign object intrusion detection center system to obtain the corresponding real-time train position system K; (1.2) Create a central system route map based on the mileage characteristics of the rail transit line, and then map the train's travel position onto the route map to establish a real-time train position display.

3. The method for identifying foreign object encroachment on straight sections of urban rail transit lines according to claim 1, characterized in that, The step (2) includes the following steps: (2.1) Setting the image detection device at the center of the locomotive end and using the image detection device to capture a real-time visual image of the train in the direction of travel on the straight section; (2.2) The radar detection device is placed above the image detection device so that the radar detection device is located on the central axis of the track line; then, a rectangular coordinate system is constructed with the radar detection device as the origin, the train travel direction as the longitudinal coordinate axis, and the direction perpendicular to the track line as the transverse coordinate axis. (2.3) Based on the difference frequency of the Doppler frequency shift, the radar detection device is used to detect the distance, angle and relative speed of objects in the direction of train travel until the objects are detected; (2.4) The detected objects are numbered, and the object number is combined with the corresponding distance, angle and relative speed to obtain real-time radar data of the direction of train travel.

4. The method for identifying foreign object encroachment on straight sections of urban rail transit lines according to claim 1, characterized in that, The step (4) includes the following steps: (4.1) Set a continuous monitoring time window for the real-time radar data of the train's straight section travel direction, and count the number of times an object is successfully detected within the continuous monitoring time window as the object detection count; (4.2) If the number of object detections is less than the first detection threshold, the track line status is determined to be the foreign object intrusion disappearance state, and return to step (4.1); otherwise, proceed to step (4.3); (4.3) If the number of object detections is greater than the second detection threshold, the track line status is determined to be the foreign object intrusion continuous state, and proceed to step (4.4); otherwise, proceed directly to step (4.1); (4.4) Construct a foreign object intrusion state space model based on real-time radar data; ； ;in, Indicates the k-th object in Location at any given moment Indicates the k-th object in The speed of time, Indicates the k-th object in acceleration at any moment Represents the length of adjacent time intervals; (4.5) Based on the foreign object intrusion state space model, construct the foreign object intrusion state equation; ； ;in, express Time-based process noise, express Position measurement vector at time [time] express The observation matrix at time, express Measurement noise at any moment; (4.6) According to the Kalman filter algorithm, the foreign object intrusion state equation is updated in time and state to obtain the current distance between the radar-detected object and the locomotive end. (4.7) Utilize the existing vehicle-to-ground wireless communication system of urban rail transit to upload the distance of radar-detected objects to the central system in real time. 。 5. The method for identifying foreign object encroachment on straight sections of urban rail transit lines according to claim 1, characterized in that, Step (5) includes the following steps: (5.1) Obtain the data synchronization frequency based on the detection frequency of the radar detection device and the detection frequency of the image detection device; (5.2) Obtain the transformation model between the radar coordinate system and the world coordinate system, the transformation model between the pixel coordinate system and the camera coordinate system, and the transformation model between the camera coordinate system and the world coordinate system; (5.3) Based on the transformation model between the radar coordinate system and the world coordinate system, convert the current radar detection object position into the current radar detection position in the world coordinate system. (5.4) Based on the transformation model between the pixel coordinate system and the camera coordinate system and the transformation model between the camera coordinate system and the world coordinate system, the field-of-view image feature detection results are converted into image detection results in the world coordinate system; (5.5) According to the data synchronization frequency, the target position is fused with the current radar detection position and image detection results at the same time based on the following target association model to obtain the vehicle intrusion detection results; wherein, the target association model is: ;in, This indicates the distance between the current radar detection location and the center point of the object in the image detection result. Indicates the current radar detection location. This indicates the location of the center point of the object in the image detection results. This represents the first correlation threshold. This represents the cross-union ratio (CURBR) of the areas of the object detected by the radar and the image. This indicates the outline region of an object detected by radar. This represents the outline region of the object detected in the image. This represents the second correlation threshold.

6. The method for identifying foreign object encroachment on straight sections of urban rail transit lines according to claim 1, characterized in that, Step (6) includes the following steps: (6.1) Uploading the vehicle intrusion detection results to the foreign object intrusion center system through the existing vehicle-to-ground wireless communication system of urban rail transit; (6.2) The foreign object intrusion center system monitors the train's location information in real time. Distance to radar-detected objects Summation is performed to calculate the location information of the foreign object. The results of foreign object intrusion detection are mapped and alarmed on the central system circuit diagram.

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