CN115937798A - Method and device for detecting derailment of rail locomotive - Google Patents
Method and device for detecting derailment of rail locomotive Download PDFInfo
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Abstract
The application provides a method and a device for detecting derailment of a rail locomotive, wherein the method comprises the following steps: pre-constructing a locomotive derailment identification model; acquiring a picture to be detected between wheels of a rail locomotive and a rail; performing locomotive derailment detection on the picture to be detected through a locomotive derailment identification model to obtain the on-track state of the locomotive; judging whether the rail locomotive is derailed or not according to the on-track state of the locomotive; and when the rail locomotive derails, locomotive derail alarm information is output. Therefore, the method and the device can detect the derailment of the locomotive in time, save the manufacturing cost, reduce the complexity of the system and have wider adaptability.
Description
Technical Field
The application relates to the technical field of data processing, in particular to a method and a device for detecting derailment of a rail locomotive.
Background
At present, in the operation of a rail locomotive in the year over the month, derailment accidents occur due to factors such as sleeper settlement, too fast speed, train self faults and the like. Especially in the field of automatic driving of rail locomotives, when an automatic driving system of the rail locomotive cannot identify the derailment of the locomotive and cannot take safety measures in time, the locomotive with power further expands accidents, thereby causing more serious potential safety hazards. In the prior art, a transmitter on each wheel set is required to detect derailment, at least one receiver is arranged on at least one wheel set, the engineering installation amount is too large, and the system complexity is high.
Disclosure of Invention
An object of the embodiment of the application is to provide a method and a device for detecting derailment of a rail locomotive, which can detect derailment of the locomotive in time, save manufacturing cost, reduce system complexity and have wider adaptability.
The first aspect of the embodiment of the present application provides a method for detecting derailment of a rail locomotive, including:
pre-constructing a locomotive derailment identification model;
acquiring a picture to be detected between wheels of a rail locomotive and a rail;
performing locomotive derailment detection on the picture to be detected through the locomotive derailment identification model to obtain the on-track state of the locomotive;
judging whether the rail locomotive is derailed or not according to the on-rail state of the locomotive;
if yes, locomotive derailment alarm information is output.
In the implementation process, the method can preferably pre-construct a locomotive derailment identification model; acquiring a picture to be detected between wheels of the rail locomotive and a rail; then, performing locomotive derailment detection on the picture to be detected through a locomotive derailment identification model to obtain the on-track state of the locomotive; judging whether the rail locomotive is derailed or not according to the on-track state of the locomotive; when the rail locomotive derails, locomotive derail alarm information is immediately output. Therefore, the method can detect the derailment of the locomotive in time, save the manufacturing cost, reduce the complexity of the system and have wider adaptability.
Further, the pre-constructed locomotive derailment identification model comprises:
acquiring sample pictures of a train wheel set and a track; wherein the pictures comprise a derailed picture and a non-derailed picture;
calibrating the sample picture to obtain a calibrated sample picture;
and training an original neural network model through the calibration sample picture to obtain a locomotive derailment identification model.
Further, the training of the original neural network model through the calibration sample picture to obtain a locomotive derailment identification model comprises:
constructing an original neural network model by adopting a convolutional neural network;
training the original neural network model by using a pre-configured deep learning library and the calibration sample picture to obtain trained model parameters;
and generating a locomotive derailment identification model according to the model parameters and the original neural network model.
Further, the acquiring the picture to be measured between the wheels of the rail locomotive and the rail comprises:
acquiring a picture to be detected, which is attached to a rail, of a wheel set of a rail locomotive by using a high-definition wide-angle camera; the high-definition wide-angle camera is installed at the head position of each carriage of the rail locomotive.
Further, the determining whether the rail locomotive is derailed according to the locomotive on-track state includes:
determining the continuous derailment time of the rail locomotive according to the on-rail state of the locomotive;
judging whether the continuous derailment time is greater than a preset time threshold value or not;
if so, determining that the rail locomotive is derailed, and executing the output locomotive derailing alarm information.
A second aspect of an embodiment of the present application provides a rail locomotive derailment detection device, including:
the building unit is used for building a locomotive derailment identification model in advance;
the acquisition unit is used for acquiring a picture to be detected between wheels of the rail locomotive and a rail;
the derailment detection unit is used for performing locomotive derailment detection on the picture to be detected through the locomotive derailment identification model to obtain the on-orbit state of the locomotive;
the judging unit is used for judging whether the rail locomotive is derailed or not according to the on-rail state of the locomotive;
and the output unit is used for outputting locomotive derailment alarm information when the track locomotive is judged to be derailed.
In the implementation process, the device can pre-construct a locomotive derailment identification model through a construction unit; acquiring a picture to be detected between wheels of a rail locomotive and a rail through an acquisition unit; performing locomotive derailment detection on the picture to be detected through a locomotive derailment identification model through a derailment detection unit to obtain the on-track state of the locomotive; judging whether the rail locomotive is derailed or not according to the on-track state of the locomotive through a judging unit; and then when the derailed locomotive of the rail is judged through the output unit, locomotive derailing alarm information is output. Therefore, the device can detect the derailment of the locomotive in time, save the manufacturing cost, reduce the complexity of the system and have wider adaptability.
Further, the construction unit includes:
the acquisition subunit is used for acquiring sample pictures of the train wheel set and the track; wherein the pictures comprise derailed pictures and non-derailed pictures;
the calibration subunit is used for calibrating the sample picture to obtain a calibration sample picture;
and the training subunit is used for training the original neural network model through the calibration sample picture to obtain a locomotive derailment identification model.
Further, the training subunit includes:
the building module is used for building an original neural network model by adopting a convolutional neural network;
the training module is used for training the original neural network model by using a pre-configured deep learning library and the calibration sample picture to obtain trained model parameters;
and the generating module is used for generating a locomotive derailment identification model according to the model parameters and the original neural network model.
Further, the acquisition unit is specifically used for acquiring a to-be-detected picture of the joint of the wheel set of the rail locomotive and the rail by using a high-definition wide-angle camera; the high-definition wide-angle camera is installed at the head position of each carriage of the rail locomotive.
Further, the judging unit includes:
a determining subunit, configured to determine a continuous derailment time of the rail locomotive according to the locomotive on-rail state;
the judging subunit is used for judging whether the continuous derailment time is greater than a preset time threshold value;
and the determining subunit is further configured to determine that the rail locomotive is derailed and execute the output locomotive derailing alarm information when the continuous derailing time is greater than a preset time threshold.
A third aspect of the embodiments of the present application provides an electronic device, including a memory and a processor, where the memory is used for storing a computer program, and the processor runs the computer program to make the electronic device execute the method for detecting a derailment of a rail locomotive according to any one of the first aspect of the embodiments of the present application.
A fourth aspect of the embodiments of the present application provides a computer-readable storage medium storing computer program instructions, which when read and executed by a processor, perform the method for detecting a derailment of a rail locomotive according to any one of the first aspect of the embodiments of the present application.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a schematic flow chart of a method for detecting derailment of a rail vehicle according to an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of a rail locomotive derailment detection device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
Example 1
Referring to fig. 1, fig. 1 is a schematic flow chart illustrating a method for detecting a derailment of a rail vehicle according to this embodiment. The method for detecting the derailment of the rail locomotive comprises the following steps:
s101, obtaining sample pictures of a train wheel set and a track; wherein the pictures comprise a derailed picture and a non-derailed picture.
And S102, calibrating the sample picture to obtain a calibrated sample picture.
In this embodiment, the method may use a high-definition wide-angle camera to collect locomotive wheel pair information. Specifically, the camera can be installed at the head position of each carriage, images of all wheel pairs and rail attachment of the carriage can be collected as long as the camera is adjusted, and the installation position can be adjusted according to actual conditions.
In this embodiment, the sample picture and the calibration sample picture may be transmitted through a wireless network.
In this embodiment, the method may adopt an industrial WiFi or 5G network scheme to transmit the high-definition images of all the camera nodes to other modules.
S103, constructing an original neural network model by adopting a convolutional neural network.
And S104, training the original neural network model by using a pre-configured deep learning library and a calibration sample picture to obtain trained model parameters.
And S105, generating a locomotive derailment identification model according to the model parameters and the original neural network model.
S106, acquiring a picture to be detected of the joint of a wheel set of the rail locomotive and a rail by using a high-definition wide-angle camera; wherein, high definition wide angle camera installs in the head position of each section carriage of track locomotive.
S107, performing locomotive derailment detection on the picture to be detected through the locomotive derailment identification model to obtain the on-track state of the locomotive.
And S108, determining the continuous derailing time of the rail locomotive according to the on-rail state of the locomotive.
S109, judging whether the continuous derailment time is larger than a preset time threshold value, if so, executing a step S110; if not, the flow is ended.
S110, determining that the rail locomotive is derailed, and outputting locomotive derailing alarm information.
In this embodiment, the method may identify whether the locomotive wheel pair is off-track based on deep learning. The execution subject of the method may be a hardware device, and the hardware device may be a system having an image processing capability (GPU).
For example, the method may comprise the following four steps:
(1) Image data preprocessing: in the early stage, a large number of pictures of the train wheel set and the rail are acquired and calibrated, wherein the pictures comprise derailed pictures and non-derailed pictures, and the pictures are marked.
(2) Training a neural network: training is carried out by using a deep learning library tensorflow, a convolutional neural network CNN is adopted for picture recognition, and training parameters are output by continuously adjusting network parameters and a network structure until a recognition effect meeting requirements is achieved.
(3) Image recognition: and processing the image data sent by the network transmission module in real time by using the parameters trained in the second step, and identifying the on-track state of the locomotive.
(4) And (3) alarm output of the recognition result: when the locomotive derailment is identified and the locomotive derailment state is kept for N milliseconds, a locomotive derailment alarm is sent out and is used for a manual or automatic driving system to take emergency measures.
In the embodiment, the parameters in the method can be configured according to the actual test condition, so that accidental false alarm can be prevented.
In this embodiment, the execution subject of the method may be a computing device such as a computer and a server, and is not limited in this embodiment.
In this embodiment, an execution subject of the method may also be an intelligent device such as a smart phone and a tablet computer, which is not limited in this embodiment.
It can be seen that, by implementing the method for detecting derailment of a rail locomotive described in this embodiment, the derailment information of the locomotive can be collected only by using a camera, so that only one camera is needed for each carriage, and the effects of saving cost and facilitating installation and debugging are achieved. Meanwhile, the condition of locomotive derailment can be accurately identified by using deep learning, so that the accuracy is improved.
Example 2
Referring to fig. 2, fig. 2 is a schematic structural diagram of a derailment detection device for a rail locomotive according to this embodiment. As shown in fig. 2, the derailment detection apparatus of the rail car includes:
the building unit 210 is configured to pre-build a locomotive derailment identification model;
the acquiring unit 220 is configured to acquire a to-be-detected picture between a wheel of a rail locomotive and a rail;
the derailment detection unit 230 is used for performing locomotive derailment detection on the picture to be detected through the locomotive derailment identification model to obtain the on-orbit state of the locomotive;
the judging unit 240 is used for judging whether the rail locomotive is derailed according to the on-track state of the locomotive;
and the output unit 250 is used for outputting locomotive derailment alarm information when the derailed locomotive is judged.
As an alternative embodiment, the building unit 210 includes:
the obtaining subunit 211 is configured to obtain sample pictures of the train wheel set and the track; wherein the pictures comprise a derailed picture and a non-derailed picture;
a calibration subunit 212, configured to calibrate the sample picture to obtain a calibrated sample picture;
and the training subunit 213 is configured to train the original neural network model through the calibration sample picture to obtain a locomotive derailment identification model.
As an alternative embodiment, the training subunit 213 includes:
the building module is used for building an original neural network model by adopting a convolutional neural network;
the training module is used for training an original neural network model by using a pre-configured deep learning library and a calibration sample picture to obtain trained model parameters;
and the generating module is used for generating a locomotive derailment identification model according to the model parameters and the original neural network model.
As an optional implementation manner, the obtaining unit 220 is specifically configured to use a high-definition wide-angle camera to collect a to-be-detected picture of the rail locomotive, which is formed by the wheel set and the rail being attached to each other; the high-definition wide-angle camera is installed at the head position of each carriage of the rail locomotive.
As an optional implementation manner, the determining unit 240 includes:
a determining subunit 241 for determining a continuous derailment time of the rail locomotive according to the on-rail state of the locomotive;
a determining subunit 242, configured to determine whether the continuous derailment time is greater than a preset time threshold;
the determining subunit 241 is further configured to determine that the rail locomotive is derailed when the continuous derailing time is greater than a preset time threshold, and output locomotive derailing alarm information.
In the embodiment of the present application, for the explanation of the derailment detection device for the rail vehicle, reference may be made to the description in embodiment 1, and details are not repeated in this embodiment.
It can be seen that, the derailment detection device for the rail locomotive described in the embodiment can acquire the derailment information of the locomotive by only using the camera, so that only one camera is needed for each carriage, and the effects of saving cost and facilitating installation and debugging are achieved. Meanwhile, the condition of locomotive derailment can be accurately identified by using deep learning, so that the accuracy is improved.
The embodiment of the application provides an electronic device, which comprises a memory and a processor, wherein the memory is used for storing a computer program, and the processor runs the computer program to enable the electronic device to execute the derailment detection method of the rail locomotive in the embodiment 1 of the application.
The embodiment of the present application provides a computer-readable storage medium, which stores computer program instructions, and when the computer program instructions are read and executed by a processor, the computer program instructions execute the method for detecting derailment of a rail locomotive in embodiment 1 of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk, and various media capable of storing program codes.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made to the present application by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.
Claims (10)
1. A rail locomotive derailment detection method is characterized by comprising the following steps:
pre-constructing a locomotive derailment identification model;
acquiring a picture to be detected between wheels of a rail locomotive and a rail;
performing locomotive derailment detection on the picture to be detected through the locomotive derailment identification model to obtain the on-track state of the locomotive;
judging whether the rail locomotive is derailed or not according to the on-track state of the locomotive;
if yes, locomotive derailment alarm information is output.
2. The method of claim 1, wherein the pre-constructing a locomotive derailment identification model comprises:
acquiring sample pictures of a train wheel set and a track; wherein the pictures comprise a derailed picture and a non-derailed picture;
calibrating the sample picture to obtain a calibrated sample picture;
and training an original neural network model through the calibration sample picture to obtain a locomotive derailment identification model.
3. The method for detecting the derailment of the railway locomotive according to claim 2, wherein the training of the original neural network model through the calibration sample picture to obtain the locomotive derailment identification model comprises:
constructing an original neural network model by adopting a convolutional neural network;
training the original neural network model by using a pre-configured deep learning library and the calibration sample picture to obtain trained model parameters;
and generating a locomotive derailment identification model according to the model parameters and the original neural network model.
4. The method for detecting derailment of a rail vehicle according to claim 1, wherein the obtaining of the picture to be detected between the wheel of the rail vehicle and the rail includes:
acquiring a picture to be detected of the joint of a wheel set of a rail locomotive and a rail by using a high-definition wide-angle camera; the high-definition wide-angle camera is installed at the head position of each carriage of the rail locomotive.
5. The method of claim 1, wherein said determining whether the rail vehicle is derailed based on the on-track status of the vehicle comprises:
determining the continuous derailment time of the rail locomotive according to the on-rail state of the locomotive;
judging whether the continuous derailment time is greater than a preset time threshold value or not;
if yes, determining that the rail locomotive is derailed, and executing the output locomotive derailing alarm information.
6. A railroad car derailment detection device, comprising:
the building unit is used for building a locomotive derailment identification model in advance;
the acquisition unit is used for acquiring a picture to be detected between wheels of the rail locomotive and a rail;
the derailment detection unit is used for carrying out locomotive derailment detection on the picture to be detected through the locomotive derailment identification model to obtain the on-orbit state of the locomotive;
the judging unit is used for judging whether the rail locomotive is derailed or not according to the on-rail state of the locomotive;
and the output unit is used for outputting locomotive derailment alarm information when the track locomotive is judged to be derailed.
7. The railroad car derailment detection device of claim 6, wherein the construction unit comprises:
the acquisition subunit is used for acquiring sample pictures of the train wheel set and the track; wherein the pictures comprise a derailed picture and a non-derailed picture;
the calibration subunit is used for calibrating the sample picture to obtain a calibration sample picture;
and the training subunit is used for training the original neural network model through the calibration sample picture to obtain a locomotive derailment identification model.
8. The railroad car derailment detection device of claim 7, wherein the training subunit comprises:
the building module is used for building an original neural network model by adopting a convolutional neural network;
the training module is used for training the original neural network model by using a pre-configured deep learning library and the calibration sample picture to obtain trained model parameters;
and the generating module is used for generating a locomotive derailment identification model according to the model parameters and the original neural network model.
9. An electronic device, comprising a memory for storing a computer program and a processor that runs the computer program to cause the electronic device to perform the method of detecting a railroad locomotive derailment of any of claims 1-5.
10. A readable storage medium having stored thereon computer program instructions which, when read and executed by a processor, perform the method of detecting a railroad locomotive derailment of any of claims 1 to 5.
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