CN114743166A - Method for detecting brake of railway wagon - Google Patents
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Abstract
The invention discloses a method for detecting a brake of a railway wagon, which comprises the following steps: acquiring parameter information of a railway wagon; when the RGV trolley runs along a first direction, acquiring positioning information of the RGV trolley in real time, generating a first target detection area of a plurality of rows of carriages in real time based on the parameter information and the positioning information, and collecting a plurality of first detection images in the first target detection area by the RGV trolley; acquiring a pre-trained neural network model, and inputting a plurality of first detection images into the neural network model to generate a first defect detection result; generating a second target detection area when the RGV trolley runs along a second direction based on the first defect detection result, acquiring a plurality of second detection images in the second target detection area by the RGV trolley, and inputting the plurality of second detection images into the neural network model to generate a second defect detection result; generating a brake defect detection report based on the first defect detection result and the second defect detection result.
Description
Technical Field
The invention relates to the technical field of rail vehicle detection, in particular to a method for detecting a brake of a railway wagon.
Background
To ensure safe operation, fault detection plays a major role in the transportation domain. There are a number of typical instances of railway, aeronautical, maritime and highway bridge maintenance where fault detection is employed. Because of the great responsibility in the transportation field, once important equipment fails, huge losses of personnel and property are caused, and thus, a great deal of manpower, material resources and financial resources are invested in the research of fault detection in many countries in the world. Fault detection has become one of the research hotspots in the transportation field today.
For the railway freight car, the operation and maintenance side sets a large number of train inspection fields to detect freight car vehicles so as to ensure the operation safety. At present, the train inspection field mainly depends on manual inspection, and the problems of large workload, incapability of standardizing detection results and the like exist. For the problem, the prior research thought of my department is as follows: the method comprises the steps of continuously acquiring images of the bottom of a railway wagon based on an RGV, finding out images containing a brake from the images, and finally performing fault diagnosis. However, the following two problems exist in the research idea: all detection pictures are screened at the RGV trolley end, but the requirement on the calculation force of the trolley is high, and the requirements (strong calculation force and large size) are difficult to meet no matter the heat dissipation, the calculation force or the size is large; the detection pictures are transmitted to a server side, and screening is carried out by using the calculation power of the server, but the method has high requirement on network transmission bandwidth, and although the processing speed is high, the transmission time is too long, and the real-time requirement cannot be met.
In conclusion, the existing method for detecting the brake of the railway wagon has the problems of low detection efficiency and poor real-time performance.
Disclosure of Invention
In view of this, the invention provides a method for detecting a brake of a railway wagon, which solves the problems of low detection efficiency and poor real-time performance of the traditional method for detecting the brake of the railway wagon by improving a data acquisition and data processing method.
In order to solve the problems, the technical scheme of the invention is to adopt a method for detecting a brake of a railway wagon, which comprises the following steps: acquiring parameter information of a railway wagon; when the RGV trolley runs along a first direction, acquiring positioning information of the RGV trolley in real time, generating a first target detection area of a plurality of rows of carriages in real time based on the parameter information and the positioning information, and collecting a plurality of first detection images in the first target detection area by the RGV trolley; acquiring a pre-trained neural network model, and inputting a plurality of first detection images into the neural network model to generate a first defect detection result; generating a second target detection area when the RGV trolley runs along a second direction based on the first defect detection result, acquiring a plurality of second detection images in the second target detection area by the RGV trolley, and inputting the plurality of second detection images into the neural network model to generate a second defect detection result; generating a brake defect detection report based on the first defect detection result and the second defect detection result.
Optionally, the obtaining of parameter information of the railway wagon includes: when a railway wagon drives into a train inspection field, extracting the wagon number information of the railway wagon and vehicle wheelbase information and vehicle axle number information corresponding to the wagon number information based on a wagon number identification unit; when a railway wagon drives into a train inspection field, extracting the number of axles of the part of the railway wagon, which has driven into the train inspection field, based on an axle counting sensor; and forming the parameter information based on the number of axles, the number information of the vehicle, the wheel base information of the vehicle and the number information of the axles of the vehicle.
Optionally, acquiring location information of the RGV car in real time includes: acquiring running distance information based on an encoder of the RGV trolley; the wheel sensor based on the RGV is used for generating the number information of the wheels of the RGV passing through the rail wagon and used as auxiliary correction information; and generating the positioning information of the RGV trolley compared with the railway wagon based on the running distance information, the auxiliary correction information and the initial position information of the RGV trolley, wherein the initial position of the RGV trolley is the head starting position of the railway wagon.
Optionally, generating a first target detection area in real time based on the parameter information and the positioning information includes: generating the first target detection area for detecting an area between a second axle and a third axle of each car of the railway freight car based on the positioning information, the vehicle wheel base information, and the vehicle axle count information in a case where a brake is provided between the second axle and the third axle of each car of the railway freight car.
Optionally, pre-training the neural network model comprises: constructing an initialization network model, wherein the network model comprises a semantic segmentation model; acquiring a training data set and a testing data set which are formed by brake image samples containing multi-class marks, wherein the mark classes comprise the brakes and multi-class defects of the brakes; training and testing the neural network model based on the training dataset and the testing dataset.
Optionally, inputting a plurality of the first detection images into the neural network model to generate a first defect detection result, including: inputting a plurality of first detection images of the same compartment into the neural network model one by one, stopping image recognition of the rest of first detection images in the same compartment and inputting a plurality of first detection images of the next compartment into the neural network model one by one when a brake detection frame is generated by the neural network model for the first time, and generating a defect detection result of the compartment based on the brake detection frame and the brake defect detection frame if brake defect detection frames of other categories are generated at the same time when the brake detection frame is generated by the neural network model for the first time; and when the neural network model finishes traversing the first detection images of all the carriages, generating a first defect detection result based on the defect detection results of all the carriages.
Optionally, generating a second target detection area when the RGV car travels in a second direction based on the first defect detection result includes: and generating a second target detection area with the area smaller than that of the first target detection area and larger than that of the detection frame in the first target detection area of each train of carriages based on the area information and the coordinate information of the detection frame contained in the brake detection frame of each train of carriages in the first defect detection result, wherein the second target detection area contains the area to which the brake detection frame belongs.
Optionally, inputting a plurality of second detection images into the neural network model to generate a second defect detection result, including: inputting a plurality of second detection images of the same compartment into the neural network model one by one, stopping image recognition of the rest of second detection images in the same compartment and inputting a plurality of second detection images of the next compartment into the neural network model one by one when the neural network model generates a brake detection frame for the first time, and generating a defect detection result of the compartment based on the brake detection frame and the brake defect detection frame if brake defect detection frames of other categories are generated at the same time when the brake detection frame is generated for the first time by the neural network model; and when the neural network model finishes traversing the second detection images of all the carriages, generating a second defect detection result based on the defect detection results of all the carriages.
The invention has the primary improvement that the method for detecting the brake of the railway freight car accurately positions the axle area where the brake is positioned as the first target detection area by acquiring the parameter information of the railway freight car and the positioning information of the RGV compared with the positioning information of the railway train, thereby effectively avoiding the problem that the number of pictures is huge and the computational load of a data processing unit is huge because the RGV needs to continuously acquire the pictures at the bottom of the train in real time in the running process, ensuring that the RGV only needs to discontinuously detect the first target detection area in the running process, reducing the data volume input to the RGV trolley data processing unit and realizing the real-time detection of the brake defects of the RGV. Meanwhile, the brake cylinder of each carriage may be arranged on the left side or the right side of the vehicle body, so that the RGV trolley can only generate an axle area where the brake is located as a first target detection area based on the axle position of each carriage and the positioning information of the RGV trolley when detecting along the first direction, but after a first defect detection result is generated, the data processing unit can determine whether the brake cylinder of each carriage is arranged on the left side or the right side of the vehicle body according to the information of the brake detection frame, so that the detection area is further reduced, a more accurate second target detection area is generated, the data quantity input to the RGV trolley data processing unit is further reduced, the real-time and accurate detection of the brake of the railway wagon is realized, and the problems of low detection efficiency and poor real-time performance of the traditional brake detection method of the railway wagon are solved.
Drawings
Fig. 1 is a simplified flow diagram of a brake detection method of a railway freight car in accordance with the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood by those skilled in the art, the present invention will be further described in detail with reference to the accompanying drawings and specific embodiments.
As shown in fig. 1, a brake detection method for a railway freight car includes: acquiring parameter information of a railway wagon; when the RGV trolley runs along a first direction, acquiring positioning information of the RGV trolley in real time, generating a first target detection area of a plurality of rows of carriages in real time based on the parameter information and the positioning information, and collecting a plurality of first detection images in the first target detection area by the RGV trolley; acquiring a pre-trained neural network model, and inputting a plurality of first detection images into the neural network model to generate a first defect detection result; generating a second target detection area when the RGV trolley runs along a second direction based on the first defect detection result, acquiring a plurality of second detection images in the second target detection area by the RGV trolley, and inputting the plurality of second detection images into the neural network model to generate a second defect detection result; generating a brake defect detection report based on the first defect detection result and the second defect detection result. The first direction can be the direction of the train head towards the train tail, and the second direction can be the direction of the train tail towards the train head; because the first detection image and the second detection image are respectively shot by the RGV under different angles in different driving directions, a certain difference exists between a first defect detection result and a second defect detection result output by the neural network model, meanwhile, the credibility of the detection result output by the neural network model is interfered by factors such as environmental interference and camera parameters, and a user can freely select a mode of generating a brake defect detection report based on the first defect detection result and the second defect detection result according to the credibility of the first defect detection result and the second defect detection result output by the neural network model, for example: in the case that the credibility of the first defect detection result and the second defect detection result is higher, generating a brake defect detection report based on the union of the first defect detection result and the second defect detection result; in the case where the first and second defect detection results are less reliable, a brake defect detection report may be generated based on the intersection of the first and second defect detection results.
Further, acquiring parameter information of the rail wagon, including: when a railway wagon drives into a train inspection field, extracting the wagon number information of the railway wagon and vehicle wheelbase information and vehicle axle number information corresponding to the wagon number information based on a wagon number identification unit; when a railway wagon drives into a train inspection field, extracting the number of axles of the part of the railway wagon, which has driven into the train inspection field, based on an axle counting sensor; and forming the parameter information based on the number of axles, the number information of the vehicle, the wheel base information of the vehicle and the number information of the axles of the vehicle. The car number identification unit and the axle counting sensor belong to mature general devices in the field, so that the setting mode and the model are not specifically limited in the application.
Further, acquiring the positioning information of the RGV in real time comprises: acquiring running distance information based on an encoder of the RGV trolley; the wheel sensor based on the RGV trolley generates the number information of the wheels of the RGV trolley passing through the rail wagon as auxiliary correction information; and generating the positioning information of the RGV trolley compared with the railway wagon based on the running distance information, the auxiliary correction information and the initial position information of the RGV trolley, wherein the initial position of the RGV trolley is the head starting position of the railway wagon. The RGV trolley is an automatic trolley based on rail guidance, belongs to a mature general device in the field, and therefore the arrangement mode and the model of the RGV trolley are not specifically limited, and meanwhile, the RGV trolley can be provided with a conventional detection device in the field, such as an encoder, a wheel sensor, a camera, a data processing unit and the like.
Further, generating a first target detection area in real time based on the parameter information and the positioning information includes: generating the first target detection area for detecting an area between a second axle and a third axle of each car of the railway wagon based on the positioning information, the vehicle wheel base information, and the vehicle axle count information in a case where a brake is provided between the second axle and the third axle of each car of the railway wagon. In the art, a brake is usually disposed between the second axle and the third axle of each car of the railway wagon, so that how to generate the first target detection area is described in this case. When the brake is provided between the other axles of each car of the railway wagon, the area between the other axles may be used as the first target detection area.
Further, pre-training the neural network model comprises: constructing an initialization network model, wherein the network model comprises a semantic segmentation model; acquiring a training data set and a testing data set which are formed by brake image samples containing multi-class marks, wherein the mark classes comprise the brakes and multi-class defects of the brakes; training and testing the neural network model based on the training dataset and the testing dataset. It should be noted that the neural network model used in the present application is conventional in the art, and does not involve further improvement of the model architecture, and therefore, the type and architecture of the neural network model are not specifically limited. The type of the neural network model can be YOLO-V3, FASTER RCNN, and the like.
Further, inputting a plurality of first detection images into the neural network model to generate a first defect detection result, including: inputting a plurality of first detection images of the same compartment into the neural network model one by one, stopping image recognition of the rest of first detection images in the same compartment and inputting a plurality of first detection images of the next compartment into the neural network model one by one when a brake detection frame is generated by the neural network model for the first time, and generating a defect detection result of the compartment based on the brake detection frame and the brake defect detection frame if brake defect detection frames of other categories are generated at the same time when the brake detection frame is generated by the neural network model for the first time; and when the neural network model finishes traversing the first detection images of all the carriages, generating a first defect detection result based on the defect detection results of all the carriages. Wherein, the multiple type of information that detects the frame of neural network model output all includes (x, y, w, h, i), (x, y) are the upper left corner coordinate of detecting the frame, w is the width of detecting the frame, h is the height of detecting the frame, i is the confidence coefficient of detecting the frame.
Further, generating a second target detection area when the RGV car travels in a second direction based on the first defect detection result, includes: and generating a second target detection area with the area smaller than that of the first target detection area and larger than that of the detection frame in the first target detection area of each train of carriages based on the area information and the coordinate information of the detection frame contained in the brake detection frame of each train of carriages in the first defect detection result, wherein the second target detection area contains the area to which the brake detection frame belongs.
Further, inputting a plurality of second detection images into the neural network model to generate a second defect detection result, including: inputting a plurality of second detection images of the same compartment into the neural network model one by one, stopping image recognition of the rest of second detection images in the same compartment and inputting a plurality of second detection images of the next compartment into the neural network model one by one when the neural network model generates a brake detection frame for the first time, and generating a defect detection result of the compartment based on the brake detection frame and the brake defect detection frame if brake defect detection frames of other categories are generated at the same time when the brake detection frame is generated for the first time by the neural network model; and when the neural network model finishes traversing the second detection images of all the carriages, generating a second defect detection result based on the defect detection results of all the carriages.
According to the method, the axle area where the brake is located is accurately positioned as the first target detection area by acquiring the parameter information of the railway wagon and the positioning information of the RGV compared with the positioning information of the railway train, so that the problem that the number of pictures is huge and the computational load of a data processing unit is huge due to the fact that the pictures at the bottom of the train need to be continuously acquired in real time in the running process of the RGV is effectively solved, the RGV only needs to intermittently detect the first target detection area in the running process, the data input to the RGV data processing unit is reduced, and the defect of the brake of the RGV is detected in real time. Meanwhile, the brake cylinder of each carriage may be arranged on the left side or the right side of the vehicle body, so that the RGV trolley can only generate an axle area where the brake is located as a first target detection area based on the axle position of each carriage and the positioning information of the RGV trolley when detecting along the first direction, but after a first defect detection result is generated, the data processing unit can determine whether the brake cylinder of each carriage is arranged on the left side or the right side of the vehicle body according to the information of the brake detection frame, so that the detection area is further reduced, a more accurate second target detection area is generated, the data quantity input to the RGV trolley data processing unit is further reduced, the real-time and accurate detection of the brake of the railway wagon is realized, and the problems of low detection efficiency and poor real-time performance of the traditional brake detection method of the railway wagon are solved.
The brake detection method of the railway wagon provided by the embodiment of the invention is described in detail above. The embodiments are described in a progressive manner in the specification, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed in the embodiment corresponds to the method disclosed in the embodiment, so that the description is simple, and the relevant points can be referred to the description of the method part. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
Claims (8)
1. A brake detection method of a railway wagon is characterized by comprising the following steps:
acquiring parameter information of a railway wagon;
when the RGV trolley runs along a first direction, acquiring positioning information of the RGV trolley in real time, generating a first target detection area of a plurality of rows of carriages in real time based on the parameter information and the positioning information, and collecting a plurality of first detection images in the first target detection area by the RGV trolley;
acquiring a pre-trained neural network model, and inputting a plurality of first detection images into the neural network model to generate a first defect detection result;
generating a second target detection area when the RGV trolley runs along a second direction based on the first defect detection result, acquiring a plurality of second detection images in the second target detection area by the RGV trolley, and inputting the plurality of second detection images into the neural network model to generate a second defect detection result;
generating a brake defect detection report based on the first defect detection result and the second defect detection result.
2. The brake detection method of claim 1, wherein obtaining parameter information of a rail wagon comprises:
when a railway wagon drives into a train inspection field, extracting the wagon number information of the railway wagon and vehicle wheelbase information and vehicle axle number information corresponding to the wagon number information based on a wagon number identification unit;
when a railway wagon drives into a train inspection field, extracting the number of axles of the part of the railway wagon, which has driven into the train inspection field, based on an axle counting sensor;
and forming the parameter information based on the number of axles, the number information of the vehicle, the wheel base information of the vehicle and the number information of the axles of the vehicle.
3. The brake detection method of claim 2, wherein obtaining location information of the RGV car in real time comprises:
acquiring running distance information based on an encoder of the RGV trolley;
the wheel sensor based on the RGV trolley generates the number information of the wheels of the RGV trolley passing through the rail wagon as auxiliary correction information;
and generating the positioning information of the RGV compared with the rail wagon based on the running distance information, the auxiliary correction information and the initial position information of the RGV, wherein the initial position of the RGV is the head starting position of the rail wagon.
4. The brake detection method of claim 3, wherein generating a first target detection area in real time based on the parameter information and the positioning information comprises:
in the case where a brake is provided between the second axle and the third axle of each of the cars of the railway wagon,
generating the first target detection area for detecting an area between a second axle and a third axle of each car of the railway wagon based on the positioning information, the vehicle wheel base information, and the vehicle axle number information.
5. The brake detection method of claim 1, wherein pre-training the neural network model comprises:
constructing an initialization network model, wherein the network model comprises a semantic segmentation model;
acquiring a training data set and a testing data set which are formed by brake image samples containing multi-class marks, wherein the mark classes comprise the brakes and multi-class defects of the brakes;
training and testing the neural network model based on the training dataset and the testing dataset.
6. The brake detection method of claim 4, wherein inputting a plurality of the first inspection images into the neural network model to generate a first defect detection result comprises:
inputting a plurality of first detection images of the same compartment into the neural network model one by one, stopping image recognition of the rest of first detection images in the same compartment and inputting a plurality of first detection images of the next compartment into the neural network model one by one when a brake detection frame is generated by the neural network model for the first time, and generating a defect detection result of the compartment based on the brake detection frame and the brake defect detection frame if brake defect detection frames of other categories are generated at the same time when the brake detection frame is generated by the neural network model for the first time;
and when the neural network model finishes traversing the first detection images of all the carriages, generating a first defect detection result based on the defect detection results of all the carriages.
7. The brake detection method of claim 5, wherein generating a second target detection zone when the RGV is traveling in a second direction based on the first defect detection result comprises:
and generating a second target detection area with the area smaller than that of the first target detection area and larger than that of the detection frame in the first target detection area of each train of carriages based on the area information and the coordinate information of the detection frame contained in the brake detection frame of each train of carriages in the first defect detection result, wherein the second target detection area contains the area to which the brake detection frame belongs.
8. The brake detection method of claim 7, wherein inputting a plurality of the second detection images into the neural network model to generate a second defect detection result comprises:
inputting a plurality of second detection images of the same compartment into the neural network model one by one, stopping image recognition of the rest of second detection images in the same compartment and inputting a plurality of second detection images of the next compartment into the neural network model one by one when the neural network model generates a brake detection frame for the first time, and generating a defect detection result of the compartment based on the brake detection frame and the brake defect detection frame if brake defect detection frames of other categories are generated at the same time when the brake detection frame is generated for the first time by the neural network model;
and when the neural network model finishes traversing the second detection images of all the carriages, generating a second defect detection result based on the defect detection results of all the carriages.
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