Method and system for identifying and evaluating foreign matter invasion risk of rail transit

Abstract

The invention provides a rail transit foreign matter invasion risk identification and evaluation method and system, and belongs to the technical field of target identification of image processing. Image data of a running environment in front of a train are collected in real time; performing foreign matter detection on the acquired image data frame by frame through a foreign matter detection algorithm, and obtaining the category and position information of the detected foreign matter if the foreign matter is detected to exist in a certain frame of image; segmenting a track region in the frame of image through a track segmentation algorithm to obtain position information of the track region; based on the relative position relation between the foreign matter and the rail area, the distance between the foreign matter and the train and the category of the foreign matter, comprehensively evaluating and outputting the rail transit foreign matter invasion risk level. According to the method, the foreign matter invasion risk in the rail transit scene can be accurately identified in real time, meanwhile, a rail transit foreign matter invasion risk grade evaluation system is established, and efficient detection, identification and evaluation of the rail transit foreign matter invasion risk are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of target recognition in image processing, and in particular to a method and system for identifying and assessing the risk of foreign object intrusion in rail transit. Background Art

[0002] As a common means of transportation, the safety and stability of rail transit during operation has a profound impact on the safety of people's lives and property. Foreign object intrusion along the track is an important factor causing rail transit safety accidents, which poses a serious threat to the safe operation of trains. Therefore, it is necessary to accurately and timely detect the risk of foreign object intrusion that may occur in rail transit scenarios, so as to ensure the safety and stability of train operation.

[0003] At present, the detection of foreign body intrusion risks in rail transit is mainly based on video detection. Traditional video detection methods mostly use cameras installed along the track to obtain video data, and then use traditional detection methods such as frame difference method and background difference method to detect foreign body intrusion around the line. However, it is difficult and costly to deploy surveillance cameras along the entire railway line. Traditional detection methods can only be applied in simple scenarios, and it is difficult to balance speed and accuracy. At the same time, the existing rail transit foreign body intrusion detection technology only has the function of detecting the type of foreign objects, and cannot actively identify and evaluate the risk of foreign body intrusion, resulting in the driver needing to make a secondary risk judgment based on the type of foreign objects. This will delay the best time to deal with the risk and pose a huge safety hazard. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for identifying and assessing the risk of foreign object intrusion in rail transit, so as to solve at least one technical problem existing in the above-mentioned background technology.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] In a first aspect, the present invention provides a method for identifying and assessing the risk of foreign body intrusion in rail transit, comprising:

[0007] Collect image data of the running environment ahead of the train in real time;

[0008] Using a pre-trained foreign object detection model, the collected image data is detected for foreign objects frame by frame. If a foreign object is detected in a certain frame of image, the category and location information of the detected foreign object is obtained. Otherwise, the next frame of image is detected until a foreign object is detected in a certain frame of image. Training the foreign object detection model includes: constructing a foreign object detection data set, including four categories: pedestrians, large foreign objects, small foreign objects and floating objects; using a foreign object detection algorithm to train and verify the foreign object data set to obtain the optimal weight; using the foreign object detection algorithm to detect each frame of image data obtained, and outputting the category and location information of the foreign object when a foreign object is detected;

[0009] When a foreign object is detected in a certain frame image, the track area in the frame image is segmented by a pre-trained track segmentation model to obtain the position information of the track area; wherein the training of the track segmentation model includes: constructing a track segmentation data set, including two categories of track area and background; if the foreign object detection algorithm detects that a foreign object exists in a certain frame image, the track area in the frame image is segmented by the pre-trained track segmentation algorithm; and the position information of the track area is obtained by using the output result;

[0010] Based on the three key risk factors of the relative position relationship between the foreign object and the track area, the distance between the foreign object and the train, and the type of the foreign object, the risk level of foreign object intrusion in rail transit is comprehensively assessed and output.

[0011] Furthermore, a comprehensive spatial risk score of foreign objects is set based on the location information of foreign objects and the relative position relationship between foreign objects and the track area; a category risk score of foreign objects is set based on the severity of accidents caused by different categories of foreign objects; based on the risk matrix theory, the comprehensive spatial risk score and category risk score of foreign objects are combined to calculate the foreign object intrusion risk score for rail transit, and finally the risk level is output according to the score.

[0012] Furthermore, the specific process of setting the spatial risk comprehensive score of foreign objects based on the location information of foreign objects and the relative position relationship between foreign objects and the track area is as follows:

[0013] A plane rectangular coordinate system is established with the onboard camera as the origin, the train running direction as the Y axis, parallel to the track plane, and perpendicular to the train running direction as the X axis;

[0014] The spatial risk scoring standard in the horizontal direction is set according to the relative position relationship between the foreign matter and the track area in the horizontal direction;

[0015] Set the spatial risk scoring standard in the train running direction according to the distance between the foreign object and the train in the train running direction;

[0016] The comprehensive spatial risk score of foreign objects is calculated based on the spatial risk scores of foreign objects in the horizontal direction and the train running direction.

[0017] Furthermore, the specific process of setting the spatial risk scoring standard in the horizontal direction according to the relative position relationship between the foreign matter and the track area in the horizontal direction is as follows:

[0018] Calculate the actual distance between the foreign object and the track area in the horizontal direction. The calculation formula is as follows:

[0019]

[0020] Where, X Ris the horizontal coordinate of the lower right corner of the foreign body detection box in the image, X 1 , X 2 are the horizontal coordinates of the intersection of the extended line of the lower frame line of the foreign object detection frame in the figure and the left and right rails, and N is the actual distance between the foreign object and the track area;

[0021] According to the actual distance between the foreign object and the track area in the horizontal direction, the spatial risk scoring standard in the horizontal direction is set as follows:

[0022] When the spatial position relationship is N>1, the spatial risk score is 0.5;

[0023] When the spatial position relationship is N<1, the spatial risk score is 1;

[0024] When the spatial position relationship is located within the track area, the spatial risk score is 2.

[0025] Furthermore, the specific process of setting the spatial risk scoring standard in the train running direction according to the distance between the foreign object and the train in the train running direction is as follows:

[0026] Calculate the actual distance between the foreign object and the train in the direction of train operation. The calculation formula is as follows:

[0027]

[0028] Where Y R is the ordinate of the lower right corner of the foreign object detection box in the figure, indicating the pixel distance between the foreign object and the train in the running direction of the train, M is the actual distance between the foreign object and the train in the running direction of the train, and Y 0 is the farthest pixel distance captured by the camera in the direction of train movement in the figure, and 150 is the actual farthest shooting distance of the onboard camera in the direction of train movement;

[0029] According to the actual distance between the foreign object and the train in the direction of train operation, the spatial risk scoring standard in the direction of train operation is set as follows:

[0030] When the spatial position relationship is 100<M≤150, the spatial risk score is 0.5;

[0031] When the spatial position relationship is 50<M≤100, the spatial risk score is 1;

[0032] When the spatial position relationship is M≤50, the spatial risk score is 2.

[0033] Furthermore, the comprehensive score of spatial risk of foreign matter is:

[0034] L=L h +L v

[0035] Where, L is the comprehensive score of spatial risk of foreign matter, L h is the spatial risk score in the horizontal direction, L v Score the spatial risk in the direction of train movement;

[0036] The risk score of foreign body intrusion in rail transit is:

[0037] R=L×P

[0038] Where R is the foreign body intrusion risk score of rail transit, L is the comprehensive spatial risk score of foreign bodies, and P is the category risk score of foreign bodies.

[0039] In a second aspect, the present invention provides a rail transit foreign body intrusion risk identification and assessment system, comprising:

[0040] An acquisition module is used to collect image data of the running environment in front of the train in real time;

[0041] The detection module is used to use a pre-trained foreign object detection model to perform foreign object detection on the collected image data frame by frame. If a foreign object is detected in a certain frame of image, the category and location information of the detected foreign object is obtained. Otherwise, the next frame of image is detected until a foreign object is detected in a certain frame of image. The training of the foreign object detection model includes: constructing a foreign object detection data set, including four categories: pedestrians, large foreign objects, small foreign objects and floating objects; using a foreign object detection algorithm to train and verify the foreign object data set to obtain the optimal weight; using the foreign object detection algorithm to detect each frame of image data obtained, and outputting the category and location information of the foreign object when a foreign object is detected;

[0042] The segmentation module is used to segment the track area in a frame image by using a pre-trained track segmentation model when a foreign object is detected in the frame image, and obtain the position information of the track area; wherein the training of the track segmentation model includes: constructing a track segmentation data set, including two categories of track area and background; if the foreign object detection algorithm detects that a foreign object exists in a frame image, segmenting the track area in the frame image by using the pre-trained track segmentation algorithm; and obtaining the position information of the track area by using the output result;

[0043] The assessment module is used to comprehensively assess and output the risk level of foreign object intrusion in rail transit based on three key risk factors: the relative position relationship between the foreign object and the track area, the distance between the foreign object and the train, and the type of the foreign object.

[0044] In a third aspect, the present invention provides a non-transitory computer-readable storage medium, which is used to store computer instructions. When the computer instructions are executed by a processor, the rail transit foreign object intrusion risk identification and assessment method as described in the first aspect is implemented.

[0045] In a fourth aspect, the present invention provides a computer device comprising a memory and a processor, wherein the processor and the memory communicate with each other, the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute the rail transit foreign object intrusion risk identification and assessment method as described in the first aspect.

[0046] In a fifth aspect, the present invention provides an electronic device comprising: a processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory so that the electronic device executes instructions for implementing the method for identifying and assessing the risk of foreign object intrusion in rail transit as described in the first aspect.

[0047] Beneficial effects of the present invention: By adopting a detection process of first detecting foreign objects and then performing track segmentation when foreign objects are present in the image, real-time detection of foreign object intrusion is achieved, effectively improving the speed of the entire rail transit foreign object intrusion risk identification and assessment process. Based on the three key risk factors of the positional relationship between the foreign object and the track area, the distance between the foreign object and the train, and the type of the foreign object, the rail transit foreign object intrusion risk level is comprehensively assessed. Compared with other risk assessment methods, this method considers more comprehensive factors and has a more reasonable evaluation system. It can accurately judge the intrusion risks of various types of foreign objects in real time in complex and diverse rail transit scenarios, ensuring the safe operation of rail transit scenarios.

[0048] Additional advantages of the present invention will be more clearly presented in the following description or learned through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0050] Figure 1 This is a flow chart of the method for identifying and assessing the risk of foreign object intrusion in rail transit described in an embodiment of the present invention.

[0051] Figure 2 This is a detection effect diagram of the foreign object detection algorithm described in an embodiment of the present invention.

[0052] Figure 3 This is a segmentation effect diagram of the track segmentation algorithm described in an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. The embodiments described below by the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be interpreted as limiting the present invention.

[0054] It should be understood by those skilled in the art that unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art to which this invention belongs.

[0055] It should also be understood that terms, such as those defined in commonly used dictionaries, should be understood to have a meaning consistent with that in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless as defined herein.

[0056] Those skilled in the art will appreciate that, unless otherwise stated, the singular forms "a", "an", "said" and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements and / or groups thereof.

[0057] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. Different embodiments or examples described in this specification and features of different embodiments or examples may be combined and combined by those skilled in the art without contradiction.

[0058] To facilitate understanding of the present invention, the present invention is further explained below with reference to specific embodiments in conjunction with the accompanying drawings, and the specific embodiments do not constitute a limitation on the embodiments of the present invention.

[0059] Those skilled in the art should understand that the drawings are merely schematic diagrams of embodiments, and the components in the drawings are not necessarily necessary for implementing the present invention.

[0060] Example 1

[0061] In this embodiment 1, a rail transit foreign object intrusion risk identification and assessment system is first provided, including: an acquisition module, which is used to collect image data of the running environment in front of the train in real time; a detection module, which is used to use a pre-trained foreign object detection model to perform foreign object detection on the collected image data frame by frame, and if a foreign object is detected in a certain frame of image, the category and location information of the detected foreign object is obtained, otherwise the next frame of image is detected until a foreign object is detected in a certain frame of image; wherein the training of the foreign object detection model includes: constructing a foreign object detection data set, including four categories: pedestrians, large foreign objects, small foreign objects and floating objects; using a foreign object detection algorithm to train and verify the foreign object data set to obtain the optimal weight; using the foreign object detection algorithm to detect each frame of image data obtained, and in When a foreign object is detected, the category and location information of the foreign object are output; the segmentation module is used to segment the track area in a frame image through a pre-trained track segmentation model when a foreign object is detected in the frame image, and obtain the location information of the track area; wherein, the training of the track segmentation model includes: constructing a track segmentation data set, including two categories: track area and background; if the foreign object detection algorithm detects that there is a foreign object in a frame image, the track area in the frame image is segmented through the pre-trained track segmentation algorithm; the location information of the track area is obtained by using the output result; the evaluation module is used to comprehensively evaluate and output the risk level of foreign object intrusion in rail transit based on the three key risk factors of the relative position relationship between the foreign object and the track area, the distance between the foreign object and the train, and the category of the foreign object.

[0062] In this embodiment, the above-mentioned system is used to implement a method for identifying and assessing the risk of foreign object intrusion in rail transit, including: using the on-board forward-looking camera installed at the head of the train to collect image data of the running environment in front of the train in real time; using a pre-trained foreign object detection model to perform foreign object detection on the collected image data frame by frame, and if a foreign object is detected in a certain frame of image, the category and location information of the detected foreign object is obtained, otherwise the next frame of image is detected until a foreign object is detected in a certain frame of image; wherein, training the foreign object detection model includes: constructing a foreign object detection data set, including four categories: pedestrians, large foreign objects, small foreign objects and floating objects; using a foreign object detection algorithm to train and verify the foreign object data set to obtain the optimal weight; using the foreign object detection algorithm to detect each frame of image data obtained, and outputting the category and location information of the foreign object when a foreign object is detected;

[0063] When a foreign object is detected in a certain frame image, the track area in the frame image is segmented by a pre-trained track segmentation model to obtain the position information of the track area; wherein the training of the track segmentation model includes: constructing a track segmentation data set, including two categories of track area and background; if the foreign object detection algorithm detects that a foreign object exists in a certain frame image, the track area in the frame image is segmented by the pre-trained track segmentation algorithm; and the position information of the track area is obtained by using the output result;

[0064] Based on the three key risk factors of the relative position relationship between the foreign object and the track area, the distance between the foreign object and the train, and the type of the foreign object, the risk level of foreign object intrusion in rail transit is comprehensively assessed and output.

[0065] The spatial risk comprehensive score of foreign objects is set based on the location information of foreign objects and the relative position relationship between foreign objects and the track area. The category risk score of foreign objects is set based on the severity of accidents caused by different categories of foreign objects. Based on the risk matrix theory, the spatial risk comprehensive score and category risk score of foreign objects are integrated to calculate the foreign object intrusion risk score of rail transit, and finally the risk level is output according to the score.

[0066] The specific process of setting the spatial risk comprehensive score of foreign objects based on the location information of foreign objects and the relative position relationship between foreign objects and the track area is as follows:

[0067] A plane rectangular coordinate system is established with the onboard camera as the origin, the train running direction as the Y axis, parallel to the track plane, and perpendicular to the train running direction as the X axis;

[0068] The spatial risk scoring standard in the horizontal direction is set according to the relative position relationship between the foreign matter and the track area in the horizontal direction;

[0069] Set the spatial risk scoring standard in the train running direction according to the distance between the foreign object and the train in the train running direction;

[0070] The comprehensive spatial risk score of foreign objects is calculated based on the spatial risk scores of foreign objects in the horizontal direction and the train running direction.

[0071] The specific process of setting the horizontal spatial risk scoring standard according to the relative position relationship between foreign objects and the track area in the horizontal direction is as follows:

[0072] Calculate the actual distance between the foreign object and the track area in the horizontal direction. The calculation formula is as follows:

[0073]

[0074] Where, X R is the horizontal coordinate of the lower right corner of the foreign body detection box in the image, X 1 , X 2are the horizontal coordinates of the intersection of the extended line of the lower frame line of the foreign object detection frame in the figure and the left and right rails, and N is the actual distance between the foreign object and the track area;

[0075] According to the actual distance between the foreign object and the track area in the horizontal direction, the spatial risk scoring standard in the horizontal direction is set as follows:

[0076] When the spatial position relationship is N>1, the spatial risk score is 0.5;

[0077] When the spatial position relationship is N<1, the spatial risk score is 1;

[0078] When the spatial position relationship is located within the track area, the spatial risk score is 2.

[0079] The specific process of setting the spatial risk scoring standard in the train running direction according to the distance between the foreign object and the train in the train running direction is as follows:

[0080] Calculate the actual distance between the foreign object and the train in the direction of train operation. The calculation formula is as follows:

[0081]

[0082] Where Y R is the ordinate of the lower right corner of the foreign object detection box in the figure, indicating the pixel distance between the foreign object and the train in the running direction of the train, M is the actual distance between the foreign object and the train in the running direction of the train, and Y 0 is the farthest pixel distance captured by the camera in the direction of train movement in the figure, and 150 is the actual farthest shooting distance of the onboard camera in the direction of train movement;

[0083] According to the actual distance between the foreign object and the train in the direction of train operation, the spatial risk scoring standard in the direction of train operation is set as follows:

[0084] When the spatial position relationship is 100<M≤150, the spatial risk score is 0.5;

[0085] When the spatial position relationship is 50<M≤100, the spatial risk score is 1;

[0086] When the spatial position relationship is M≤50, the spatial risk score is 2.

[0087] The comprehensive spatial risk score of foreign matter is:

[0088] L=L h +L v

[0089] Where, L is the comprehensive score of spatial risk of foreign matter, L h is the spatial risk score in the horizontal direction, L vScore the spatial risk in the direction of train movement;

[0090] The risk score of foreign body intrusion in rail transit is:

[0091] R=L×P

[0092] Where R is the foreign body intrusion risk score of rail transit, L is the comprehensive spatial risk score of foreign bodies, and P is the category risk score of foreign bodies.

[0093] Example 2

[0094] In this embodiment, a rail transit foreign object intrusion risk identification and assessment method is provided to address the problems of poor real-time performance, low accuracy, and an imprecise assessment system in the existing rail transit foreign object intrusion risk identification and assessment. The method can actively detect foreign object intrusion on the line ahead in real time during the movement of the train based on the video images taken by the on-board front-end camera, and comprehensively assess the rail transit foreign object intrusion risk level based on three key risk factors: the positional relationship between the foreign object and the track area, the distance between the foreign object and the train, and the type of the foreign object. This assists train drivers in taking risk avoidance measures based on the risk level, effectively ensuring the safety and stability of rail transit scene operations.

[0095] like Figure 1 As shown, the rail transit foreign body intrusion risk identification and assessment method described in this embodiment includes the following steps:

[0096] Step 1: Use the onboard forward-looking camera installed at the head of the train to collect image data of the running environment in front of the train in real time;

[0097] Step 2: Using the foreign object detection algorithm, perform foreign object detection on the image data in step 1 frame by frame. If a foreign object is detected in a certain frame of image, obtain the category and location information of the detected foreign object. Otherwise, continue to detect the next frame of image until a foreign object is detected in a certain frame of image.

[0098] The step 2 specifically includes the following steps:

[0099] Step 2.1, construct a foreign object detection dataset, including four categories: pedestrians, large foreign objects, small foreign objects, and floating objects;

[0100] Step 2.2, using the foreign body detection algorithm to train and verify the foreign body data set to obtain the optimal weight;

[0101] Step 2.3, using the foreign object detection algorithm to detect each frame of image data acquired in step 1, and outputting the category and location information of the foreign object when a foreign object is detected.

[0102] Step 3: When a foreign object is detected in a frame image, the track area in the frame image is segmented by a track segmentation algorithm to obtain the position information of the track area;

[0103] The step 3 specifically comprises the following steps:

[0104] Step 3.1, construct a track segmentation dataset, including two categories: track area and background;

[0105] Step 3.2: If the foreign object detection algorithm detects that there is a foreign object in a certain frame image, the track area in the frame image is segmented by the pre-trained track segmentation algorithm;

[0106] Step 3.3, use the output result to obtain the location information of the track area.

[0107] Step 4: Based on the three key risk factors of the relative position relationship between the foreign object and the track area, the distance between the foreign object and the train, and the type of the foreign object, comprehensively assess the risk level of foreign object intrusion in rail transit and output it.

[0108] The step 4 specifically comprises the following steps:

[0109] Step 4.1, set the comprehensive spatial risk score of the foreign object based on the location information of the foreign object and the relative position relationship between the foreign object and the track area.

[0110] The step 4.1 specifically includes the following steps:

[0111] Step 4.1.1, establish a plane rectangular coordinate system with the onboard camera as the origin, the train running direction as the Y axis, parallel to the track plane, and perpendicular to the train running direction as the X axis;

[0112] Step 4.1.2, setting the spatial risk scoring standard in the horizontal direction according to the relative position relationship between the foreign matter and the track area in the horizontal direction;

[0113] The calculation formula for the distance between the foreign object and the track area in the horizontal direction in step 4.1.2 is:

[0114]

[0115] Where, X R is the horizontal coordinate of the lower right corner of the foreign body detection box in the image, X 1 , X 2 They are the horizontal coordinates of the intersection of the extended line of the lower frame line of the foreign object detection frame in the figure and the left and right rails, N is the actual distance between the foreign object and the track area, and 1.435 is the actual track gauge (unit: meter).

[0116] The spatial risk scoring criteria in the horizontal direction in step 4.1.2 are:

[0117] When the actual distance between the foreign object and the track area is greater than 1 meter, there is a lower risk of foreign object intrusion; when the actual distance between the foreign object and the track area is less than one meter, there is a higher risk of foreign object intrusion; when the foreign object is located in the track area, it constitutes foreign object intrusion, and the risk is the highest at this time. Based on this, the spatial risk score of foreign objects in the horizontal direction is set as shown in Table 1.

[0118] Table 1. Spatial risk scoring criteria in the horizontal direction

[0119] Spatial position relationship Spatial risk score N>1 0.5 N<1 1 Located in the track area 2

[0120] Step 4.1.3, setting a spatial risk scoring standard in the train running direction according to the distance between the foreign object and the train in the train running direction;

[0121] The calculation formula for the distance between the foreign object and the train in the train running direction in step 4.1.3 is:

[0122]

[0123] Where Y R is the ordinate of the lower right corner of the foreign object detection box in the figure, indicating the pixel distance between the foreign object and the train in the running direction of the train, M is the actual distance between the foreign object and the train in the running direction of the train (unit: meter), and Y 0 is the farthest pixel distance captured by the camera in the direction of train movement in the figure, and 150 is the actual farthest shooting distance of the onboard camera in the direction of train movement;

[0124] The spatial risk scoring standard in the train running direction in step 4.1.3 is:

[0125] When 100<M≤150, the foreign object is far away from the train, and there is a low risk of foreign object intrusion; when 50<M≤100, the foreign object is close to the train, and there is a high risk of foreign object intrusion; when M≤50, the foreign object is very close to the train, and there is a very high risk of foreign object intrusion. Based on this, the spatial risk score of foreign objects in the direction of train operation is set as shown in Table 2.

[0126] Table 2 Spatial risk scoring criteria in the train running direction

[0127] Spatial position relationship Spatial risk score 100＜M≤150 0.5 50＜M≤100 1 M≤50 2

[0128] Step 4.1.4, calculating the spatial risk comprehensive score of the foreign object based on the spatial risk score of the foreign object in the horizontal direction and the train running direction;

[0129] The calculation formula for the spatial risk comprehensive score of foreign matter in step 4.1.4 is:

[0130] L=L h +L v

[0131] Where, L is the comprehensive score of spatial risk of foreign matter, L h is the spatial risk score in the horizontal direction, L v Score the spatial risk in the direction of train movement.

[0132] The comprehensive spatial risk score of foreign matter in step 4.1.4 is shown in Table 3:

[0133] Table 3 Comprehensive score of foreign body space risk

[0134]

[0135] Step 4.2, according to the severity of accidents caused by different types of foreign objects, set the risk score of the foreign object category;

[0136] The risk scoring criteria for the categories of foreign matter in step 4.2 are shown in Table 4:

[0137] Table 4 Risk scoring criteria for foreign body categories

[0138] Foreign matter type Risk Scoring pedestrian 2 Large foreign matter 1.5 Floating Objects 1 Small foreign matter 0.5

[0139] Step 4.3, based on the risk matrix theory, the spatial risk comprehensive score of foreign objects and the category risk score of foreign objects are integrated to calculate the foreign object intrusion risk score of rail transit, and finally the risk level is output according to the score.

[0140] The rail transit foreign body intrusion risk score in step 4.3 is:

[0141] R=L×P

[0142] Where R is the foreign body intrusion risk score of rail transit, L is the comprehensive spatial risk score of foreign bodies, and P is the category risk score of foreign bodies.

[0143] The risk level of foreign body intrusion in rail transit in step 4.3 is:

[0144] When R≤1, it means that a small foreign object has appeared outside the track area. At this time, the risk level is the lowest, which is level I risk; when 1<R≤2.5, it means that a small foreign object has invaded the track area or there are other foreign objects outside the track. At this time, there is a certain risk, and the risk level is level II risk; when 2.5<R≤4, it means that there is one of the following three situations: floating objects have invaded the nearby track area, pedestrians have approached the outside track area, and large foreign objects have invaded the distant track area. At this time, the risk is high, which is level III risk; when 4<R≤8, it means that there is a large foreign object intruding in the nearby track area or pedestrians have invaded the track area. At this time, the risk is extremely high, and the risk level is the highest, which is level IV risk. The risk level of foreign object intrusion in rail transit is shown in Table 5.

[0145] Table 5 Risk level of foreign body intrusion in rail transit

[0146] Risk Scoring R≤1 1＜R≤2.5 2.5＜R≤4 4＜R≤8 Risk Level Level Ⅰ risk Level II risk Level III risk Level IV risk

[0147] In summary, the common foreign body intrusion detection process for rail transit is generally to segment the track first and then detect foreign bodies. Since the track segmentation link is much more time-consuming than the foreign body detection link, when there is no foreign body in the image, the track segmentation link will waste a lot of time. This embodiment adopts a detection process of first detecting foreign bodies and then segmenting the track only when there is a foreign body in the image, which realizes real-time detection of foreign body intrusion and effectively improves the speed of the entire rail transit foreign body intrusion risk identification and assessment process. This embodiment comprehensively assesses the foreign body intrusion risk level of rail transit based on three key risk factors: the positional relationship between the foreign body and the track area, the distance between the foreign body and the train, and the category of the foreign body. Compared with other risk assessment methods, this method considers more comprehensive factors and has a more reasonable evaluation system. It can accurately judge the intrusion risks of various foreign bodies in real time in complex and diverse rail transit scenarios, and ensure the safe operation of rail transit scenarios.

[0148] Example 3

[0149] In this embodiment 3, a rail transit tunnel intrusion detection method is provided, and the method includes the following steps:

[0150] Step 1: Use the onboard forward-looking camera installed at the head of the train to collect image data of the running environment in front of the train in real time;

[0151] Step 2: Using the foreign object detection algorithm, perform foreign object detection on the image data in step 1 frame by frame. If a foreign object is detected in a certain frame of image, obtain the category and location information of the detected foreign object. Otherwise, continue to detect the next frame of image until a foreign object is detected in a certain frame of image.

[0152] The step 2 specifically includes the following steps:

[0153] Step 2.1, construct a foreign object detection dataset, including four categories: pedestrians, large foreign objects, small foreign objects, and floating objects;

[0154] Step 2.2, using the foreign body detection algorithm to train and verify the foreign body data set to obtain the optimal weight;

[0155] Step 2.3, using the foreign object detection algorithm to detect each frame of image data acquired in step 1, and outputting the category and location information of the foreign object when a foreign object is detected.

[0156] Step 3: When a foreign object is detected in a frame image, the track area in the frame image is segmented by a track segmentation algorithm to obtain the position information of the track area;

[0157] The step 3 specifically comprises the following steps:

[0158] Step 3.1, construct a track segmentation dataset, including two categories: track area and background;

[0159] Step 3.2: If the foreign object detection algorithm detects that there is a foreign object in a certain frame image, the track area in the frame image is segmented by the pre-trained track segmentation algorithm;

[0160] Step 3.3, use the output result to obtain the location information of the track area.

[0161] Step 4: Based on the three key risk factors of the relative position relationship between the foreign object and the track area, the distance between the foreign object and the train, and the type of the foreign object, comprehensively assess the risk level of foreign object intrusion in rail transit and output it.

[0162] The step 4 specifically comprises the following steps:

[0163] Step 4.1, set the comprehensive spatial risk score of the foreign object based on the location information of the foreign object and the relative position relationship between the foreign object and the track area.

[0164] The step 4.1 specifically includes the following steps:

[0165] Step 4.1.1, establish a plane rectangular coordinate system with the onboard camera as the origin, the train running direction as the Y axis, parallel to the track plane, and perpendicular to the train running direction as the X axis;

[0166] Step 4.1.2, setting the spatial risk scoring standard in the horizontal direction according to the relative position relationship between the foreign matter and the track area in the horizontal direction;

[0167] The calculation formula for the distance between the foreign object and the track area in the horizontal direction in step 4.1.2 is:

[0168]

[0169] Where, X R is the horizontal coordinate of the lower right corner of the foreign body detection box in the image, X 1 , X 2 are the horizontal coordinates of the intersection of the extension line of the lower frame line of the foreign object detection frame in the figure and the left and right rails, N is the actual distance between the foreign object and the track area, and 1.435 is the actual track gauge (unit: meter);

[0170] The spatial risk scoring criteria in the horizontal direction in step 4.1.2 are:

[0171] When the actual distance between the foreign object and the track area is greater than 1 meter, there is a lower risk of foreign object intrusion; when the actual distance between the foreign object and the track area is less than one meter, there is a higher risk of foreign object intrusion; when the foreign object is located in the track area, it constitutes foreign object intrusion, and the risk is the highest at this time. Based on this, the spatial risk score of foreign objects in the horizontal direction is set as shown in Table 1.

[0172] Table 1. Spatial risk scoring criteria in the horizontal direction

[0173] Spatial position relationship Spatial risk score N>1 0.5 N<1 1 Located in the track area 2

[0174] Step 4.1.3, setting a spatial risk scoring standard in the train running direction according to the distance between the foreign object and the train in the train running direction;

[0175] The calculation formula for the distance between the foreign object and the train in the train running direction in step 4.1.3 is:

[0176]

[0177] Where Y R is the ordinate of the lower right corner of the foreign object detection box in the figure, indicating the pixel distance between the foreign object and the train in the running direction of the train, M is the actual distance between the foreign object and the train in the running direction of the train (unit: meter), and Y 0 is the farthest pixel distance captured by the camera in the direction of train operation, and 150 is the actual farthest shooting distance of the on-board camera in the direction of train operation.

[0178] The spatial risk scoring standard in the train running direction in step 4.1.3 is:

[0179] When 100<M≤150, the foreign object is far away from the train, and there is a low risk of foreign object intrusion; when 50<M≤100, the foreign object is close to the train, and there is a high risk of foreign object intrusion; when M≤50, the foreign object is very close to the train, and there is a very high risk of foreign object intrusion. Based on this, the spatial risk score of foreign objects in the direction of train operation is set as shown in Table 2.

[0180] Table 2 Spatial risk scoring criteria in the train running direction

[0181] Spatial position relationship Spatial risk score 100＜M≤150 0.5 50＜M≤100 1 M≤50 2

[0182] Step 4.1.4, calculating the spatial risk comprehensive score of the foreign body according to the spatial risk scores in the horizontal direction and the train running direction;

[0183] The calculation formula for the spatial risk comprehensive score of foreign matter in step 4.1.4 is:

[0184] L=L h +L v

[0185] Where, L is the comprehensive score of spatial risk of foreign matter, L h is the spatial risk score in the horizontal direction, L v Score the spatial risk in the direction of train movement.

[0186] The comprehensive spatial risk score of foreign matter in step 4.1.4 is shown in Table 3:

[0187] Table 3 Comprehensive score of foreign body space risk

[0188]

[0189] Step 4.2, according to the severity of accidents caused by different types of foreign objects, set the risk score of the foreign object category;

[0190] The risk scoring criteria for the categories of foreign matter in step 4.2 are shown in Table 4:

[0191] Table 4 Risk scoring criteria for foreign body categories

[0192] Foreign matter type Risk Scoring pedestrian 2 Large foreign matter 1.5 Floating Objects 1 Small foreign matter 0.5

[0193] Step 4.3, based on the risk matrix theory, the spatial risk comprehensive score of foreign objects and the category risk score of foreign objects are integrated to calculate the foreign object intrusion risk score of rail transit, and finally the risk level is output according to the score.

[0194] The rail transit foreign body intrusion risk score in step 4.3 is:

[0195] R=L×P

[0196] Where R is the foreign body intrusion risk score of rail transit, L is the comprehensive spatial risk score of foreign bodies, and P is the category risk score of foreign bodies.

[0197] The risk level of foreign body intrusion in rail transit in step 4.3 is:

[0198] When R≤1, it means that a small foreign object has appeared outside the track area. At this time, the risk level is the lowest, which is level I risk; when 1<R≤2.5, it means that a small foreign object has invaded the track area or there are other foreign objects outside the track. At this time, there is a certain risk, and the risk level is level II risk; when 2.5<R≤4, it means that there is one of the following three situations: floating objects have invaded the nearby track area, pedestrians have approached the outside track area, and large foreign objects have invaded the distant track area. At this time, the risk is high, which is level III risk; when 4<R≤8, it means that there is a large foreign object intruding in the nearby track area or pedestrians have invaded the track area. At this time, the risk is extremely high, and the risk level is the highest, which is level IV risk. The risk level of foreign object intrusion in rail transit is shown in Table 5.

[0199] Table 5 Risk level of foreign body intrusion in rail transit

[0200] Risk Scoring R≤1 1＜R≤2.5 2.5＜R≤4 4＜R≤8 Risk Level Level Ⅰ risk Level II risk Level III risk Level IV risk

[0201] In this embodiment, the YOLOv5 algorithm is used as the foreign object detection algorithm, and the STDC algorithm is used as the track segmentation algorithm. The risk identification and assessment of foreign object intrusion in rail transit is performed according to the above technical solution. First, a dataset constructed from 1788 images of pedestrians, rocks, plastic sheets, and cartons invading or about to intrude different tracks from the perspective of the on-board camera is used to train and test the two algorithms. The test results show that the speed and accuracy of the two algorithms are very high. The test results of the two algorithms are shown in Tables 6 to 8. Figures 2-3 shown.

[0202] Table 6 Foreign body detection accuracy test results

[0203] category Number of images Sample size Accuracy Recall F1 total 179 526 0.875 0.806 0.839 pedestrian 179 262 0.840 0.821 0.830 rock 179 154 0.896 0.897 0.896 Plastic sheeting 179 24 0.900 0.749 0.818 Carton 179 86 0.865 0.756 0.807

[0204] Table 7 Foreign body detection speed test results

[0205] Pre-processing time Inference time Non-maximum suppression (NMS) time 0.6 ms 7.8 ms 0.9 ms

[0206] Table 8 Track segmentation algorithm test results

[0207] <![CDATA[IoU background ]]> <![CDATA[IoU trac k]]> MIoU <![CDATA[PA background ]]> <![CDATA[PA trac k]]> MPA 0.9983 0.9706 0.9845 0.9991 0.9877 0.9934

[0208] Then, the spatial risk comprehensive score of the foreign object is calculated based on the location information of the foreign object in the image and the relative position relationship between the foreign object and the track area. At the same time, the category risk score of the foreign object is set according to the severity of the accident caused by different foreign object categories. In this embodiment, rocks are large foreign objects, plastic sheets are floating objects, and cartons are small foreign objects. According to the foreign object category risk score standard in the above technical solution, the risk scores of the four foreign objects of pedestrians, rocks, plastic sheets, and cartons are set as shown in Table 9:

[0209] Table 9 Risk score of foreign body categories

[0210] Foreign matter type Risk Scoring pedestrian 2 rock 1.5 Plastic sheeting 1 Carton 0.5

[0211] Finally, based on the risk matrix theory, combined with the spatial risk comprehensive score of foreign objects and the category risk score of foreign objects, the rail transit foreign object intrusion risk score is calculated, and finally the risk level is output according to the score. The rail transit foreign object intrusion risk score in this embodiment is shown in Table 10.

[0212] Table 10 Rail transit foreign body intrusion risk score table

[0213]

[0214] According to the calculation results in Table 10, in this embodiment, Level I risk represents the presence of cardboard foreign objects outside the track area, and timely cleaning by relevant staff can effectively avoid the risk; Level II risk represents the presence of plastic sheets, rocks or pedestrians outside the track area, or cardboard boxes invading the track area. At this time, the driver and relevant personnel need to concentrate their attention to prevent dangerous situations; Level III risk represents one of the following three situations: plastic sheets invading the track area close to the train, rocks invading the track area far from the train, and pedestrians close to the train but not invading the track area. Relevant personnel need to handle it in time to avoid dangerous situations; Level IV risk represents the presence of rocks invading the track area close to the train and pedestrians invading the track area. At this time, the risk is extremely high. If handled improperly, serious accidents may occur, and the risk level is the highest.

[0215] Example 4

[0216] This embodiment 4 provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the rail transit foreign object intrusion risk identification and assessment method as described above is implemented. The method includes:

[0217] Collect image data of the running environment ahead of the train in real time;

[0218] Using a pre-trained foreign object detection model, the collected image data is detected for foreign objects frame by frame. If a foreign object is detected in a certain frame of image, the category and location information of the detected foreign object is obtained. Otherwise, the next frame of image is detected until a foreign object is detected in a certain frame of image. The training of the foreign object detection model includes: constructing a foreign object detection data set, including four categories: pedestrians, large foreign objects, small foreign objects and floating objects; using a foreign object detection algorithm to train and verify the foreign object data set to obtain the optimal weight; using the foreign object detection algorithm to detect each frame of image data obtained, and outputting the category and location information of the foreign object when a foreign object is detected;

[0219] When a foreign object is detected in a certain frame image, the track area in the frame image is segmented by a pre-trained track segmentation model to obtain the position information of the track area; wherein the training of the track segmentation model includes: constructing a track segmentation data set, including two categories of track area and background; if the foreign object detection algorithm detects that a foreign object exists in a certain frame image, the track area in the frame image is segmented by the pre-trained track segmentation algorithm; and the position information of the track area is obtained by using the output result;

[0220] Based on the three key risk factors of the relative position relationship between the foreign object and the track area, the distance between the foreign object and the train, and the type of the foreign object, the risk level of foreign object intrusion in rail transit is comprehensively assessed and output.

[0221] Example 5

[0222] This embodiment 5 provides a computer device, including a memory and a processor, the processor and the memory communicate with each other, the memory stores program instructions executable by the processor, the processor calls the program instructions to execute the above-mentioned rail transit foreign object intrusion risk identification and assessment method, the method comprising:

[0223] Collect image data of the running environment ahead of the train in real time;

[0224] Using a pre-trained foreign object detection model, the collected image data is detected for foreign objects frame by frame. If a foreign object is detected in a certain frame of image, the category and location information of the detected foreign object is obtained. Otherwise, the next frame of image is detected until a foreign object is detected in a certain frame of image. The training of the foreign object detection model includes: constructing a foreign object detection data set, including four categories: pedestrians, large foreign objects, small foreign objects and floating objects; using a foreign object detection algorithm to train and verify the foreign object data set to obtain the optimal weight; using the foreign object detection algorithm to detect each frame of image data obtained, and outputting the category and location information of the foreign object when a foreign object is detected;

[0225] When a foreign object is detected in a certain frame image, the track area in the frame image is segmented by a pre-trained track segmentation model to obtain the position information of the track area; wherein the training of the track segmentation model includes: constructing a track segmentation data set, including two categories of track area and background; if the foreign object detection algorithm detects that a foreign object exists in a certain frame image, the track area in the frame image is segmented by the pre-trained track segmentation algorithm; and the position information of the track area is obtained by using the output result;

[0226] Based on the three key risk factors of the relative position relationship between the foreign object and the track area, the distance between the foreign object and the train, and the type of the foreign object, the risk level of foreign object intrusion in rail transit is comprehensively assessed and output.

[0227] Example 6

[0228] This embodiment 6 provides an electronic device, including: a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes instructions for implementing the above-mentioned rail transit foreign object intrusion risk identification and assessment method, the method comprising:

[0229] Collect image data of the running environment ahead of the train in real time;

[0230] Using a pre-trained foreign object detection model, the collected image data is detected for foreign objects frame by frame. If a foreign object is detected in a certain frame of image, the category and location information of the detected foreign object is obtained. Otherwise, the next frame of image is detected until a foreign object is detected in a certain frame of image. The training of the foreign object detection model includes: constructing a foreign object detection data set, including four categories: pedestrians, large foreign objects, small foreign objects and floating objects; using a foreign object detection algorithm to train and verify the foreign object data set to obtain the optimal weight; using the foreign object detection algorithm to detect each frame of image data obtained, and outputting the category and location information of the foreign object when a foreign object is detected;

[0231] When a foreign object is detected in a certain frame image, the track area in the frame image is segmented by a pre-trained track segmentation model to obtain the position information of the track area; wherein the training of the track segmentation model includes: constructing a track segmentation data set, including two categories of track area and background; if the foreign object detection algorithm detects that a foreign object exists in a certain frame image, the track area in the frame image is segmented by the pre-trained track segmentation algorithm; and the position information of the track area is obtained by using the output result;

[0232] Based on the three key risk factors of the relative position relationship between the foreign object and the track area, the distance between the foreign object and the train, and the type of the foreign object, the risk level of foreign object intrusion in rail transit is comprehensively assessed and output.

[0233] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0234] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0235] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0236] These computer program instructions can also be loaded onto a computer or other programmable data processing device, and a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0237] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative work on the basis of the technical solution disclosed in the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for identifying and assessing the risk of foreign body intrusion in rail transit, characterized in that: include: Collect image data of the running environment ahead of the train in real time; Using a pre-trained foreign object detection model, the collected image data is detected for foreign objects frame by frame. If a foreign object is detected in a certain frame of image, the category and location information of the detected foreign object is obtained. Otherwise, the next frame of image is detected until a foreign object is detected in a certain frame of image. The training of the foreign object detection model includes: constructing a foreign object detection data set, including four categories: pedestrians, large foreign objects, small foreign objects and floating objects; using a foreign object detection algorithm to train and verify the foreign object data set to obtain the optimal weight; using the foreign object detection algorithm to detect each frame of image data obtained, and outputting the category and location information of the foreign object when a foreign object is detected; When a foreign object is detected in a certain frame image, the track area in the frame image is segmented by a pre-trained track segmentation model to obtain the position information of the track area; wherein the training of the track segmentation model includes: constructing a track segmentation data set, including two categories of track area and background; if the foreign object detection algorithm detects that a foreign object exists in a certain frame image, the track area in the frame image is segmented by the pre-trained track segmentation algorithm; and the position information of the track area is obtained by using the output result; Based on the three key risk factors of the relative position relationship between the foreign object and the track area, the distance between the foreign object and the train, and the type of the foreign object, the risk level of foreign object intrusion in rail transit is comprehensively assessed and output.

2. The method for identifying and assessing the risk of foreign body intrusion in rail transit according to claim 1, characterized in that: The spatial risk comprehensive score of foreign objects is set based on the location information of foreign objects and the relative position relationship between foreign objects and the track area; Based on the severity of accidents caused by different types of foreign objects, a category risk score for foreign objects is set; based on the risk matrix theory, the spatial risk comprehensive score and category risk score of foreign objects are integrated to calculate the foreign object intrusion risk score for rail transit, and finally the risk level is output according to the score.

3. The method for identifying and assessing the risk of foreign body intrusion in rail transit according to claim 2 is characterized in that: The specific process of setting the spatial risk comprehensive score of foreign objects based on the location information of foreign objects and the relative position relationship between foreign objects and the track area is as follows: A plane rectangular coordinate system is established with the onboard camera as the origin, the train running direction as the Y axis, parallel to the track plane, and perpendicular to the train running direction as the X axis; The spatial risk scoring standard in the horizontal direction is set according to the relative position relationship between the foreign matter and the track area in the horizontal direction; Set the spatial risk scoring standard in the train running direction according to the distance between the foreign object and the train in the train running direction; The comprehensive spatial risk score of foreign objects is calculated based on the spatial risk scores of foreign objects in the horizontal direction and the train running direction.

4. The method for identifying and assessing the risk of foreign body intrusion in rail transit according to claim 3 is characterized in that: The specific process of setting the horizontal spatial risk scoring standard according to the relative position relationship between foreign objects and the track area in the horizontal direction is as follows: Calculate the actual distance between the foreign object and the track area in the horizontal direction. The calculation formula is as follows: Where, X R is the horizontal coordinate of the lower right corner of the foreign object detection frame in the image, X1 and X2 are the horizontal coordinates of the intersection of the extension line of the lower frame line of the foreign object detection frame in the figure and the left and right rails, and N is the actual distance between the foreign object and the track area; According to the actual distance between the foreign object and the track area in the horizontal direction, the spatial risk scoring standard in the horizontal direction is set as follows: When the spatial position relationship is N>1, the spatial risk score is 0.5; When the spatial position relationship is N<1, the spatial risk score is 1; When the spatial position relationship is located within the track area, the spatial risk score is 2.

5. The method for identifying and assessing the risk of foreign body intrusion in rail transit according to claim 3 is characterized in that: The specific process of setting the spatial risk scoring standard in the train running direction according to the distance between the foreign object and the train in the train running direction is as follows: Calculate the actual distance between the foreign object and the train in the direction of train operation. The calculation formula is as follows: Where Y R is the ordinate of the lower right corner of the foreign object detection frame in the figure, indicating the pixel distance between the foreign object and the train in the running direction of the train, M is the actual distance between the foreign object and the train in the running direction of the train, Y0 is the farthest pixel distance captured by the camera in the running direction of the train in the figure, and 150 is the actual farthest shooting distance of the on-board camera in the running direction of the train; According to the actual distance between the foreign object and the train in the direction of train operation, the spatial risk scoring standard in the direction of train operation is set as follows: When the spatial position relationship is 100<M≤150, the spatial risk score is 0.5; When the spatial position relationship is 50<M≤100, the spatial risk score is 1; When the spatial position relationship is M≤50, the spatial risk score is 2.

6. The method for identifying and assessing the risk of foreign body intrusion in rail transit according to claim 5, characterized in that: The comprehensive spatial risk score of foreign matter is: L=L h +L v Where, L is the comprehensive score of spatial risk of foreign matter, L h is the spatial risk score in the horizontal direction, L v Score the spatial risk in the direction of train movement; The risk score of foreign body intrusion in rail transit is: R=L×P Where R is the foreign body intrusion risk score of rail transit, L is the comprehensive spatial risk score of foreign bodies, and P is the category risk score of foreign bodies.

7. A rail transit foreign body intrusion risk identification and assessment system, characterized in that: include: An acquisition module is used to collect image data of the running environment in front of the train in real time; The detection module is used to use a pre-trained foreign object detection model to perform foreign object detection on the collected image data frame by frame. If a foreign object is detected in a certain frame of image, the category and location information of the detected foreign object is obtained. Otherwise, the next frame of image is detected until a foreign object is detected in a certain frame of image. The training of the foreign object detection model includes: constructing a foreign object detection data set, including four categories: pedestrians, large foreign objects, small foreign objects and floating objects; using a foreign object detection algorithm to train and verify the foreign object data set to obtain the optimal weight; using the foreign object detection algorithm to detect each frame of image data obtained, and outputting the category and location information of the foreign object when a foreign object is detected; The segmentation module is used to segment the track area in a frame image by using a pre-trained track segmentation model when a foreign object is detected in the frame image, and obtain the position information of the track area; wherein the training of the track segmentation model includes: constructing a track segmentation data set, including two categories of track area and background; if the foreign object detection algorithm detects that a foreign object exists in a frame image, segmenting the track area in the frame image by using the pre-trained track segmentation algorithm; and obtaining the position information of the track area by using the output result; The assessment module is used to comprehensively assess and output the risk level of foreign object intrusion in rail transit based on three key risk factors: the relative position relationship between the foreign object and the track area, the distance between the foreign object and the train, and the type of the foreign object.

8. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by the processor, the rail transit foreign object intrusion risk identification and assessment method as described in any one of claims 1-6 is implemented.

9. A computer device, characterized in that: It includes a memory and a processor, the processor and the memory communicate with each other, the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the rail transit foreign object intrusion risk identification and assessment method as described in any one of claims 1-6.

10. An electronic device, characterized in that: include: A processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory so that the electronic device executes instructions for implementing the rail transit foreign object intrusion risk identification and assessment method as described in any one of claims 1-6.

Images