CN115610479A - Railway line state inspection system and method - Google Patents
Railway line state inspection system and method Download PDFInfo
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- CN115610479A CN115610479A CN202211170733.0A CN202211170733A CN115610479A CN 115610479 A CN115610479 A CN 115610479A CN 202211170733 A CN202211170733 A CN 202211170733A CN 115610479 A CN115610479 A CN 115610479A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
- B61L23/00—Control, warning or like safety means along the route or between vehicles or trains
- B61L23/04—Control, warning or like safety means along the route or between vehicles or trains for monitoring the mechanical state of the route
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Abstract
The invention discloses a railway line state inspection system and a method, wherein the system comprises: the speed detection module is arranged at the axle end of a wheel of the train or on a motor gear box and is used for detecting the speed of the train; the turnout detection module comprises a plurality of turnout cameras, is arranged at the bottom of the train and is used for respectively acquiring an inner rail image and an outer rail image of the turnout at the railway turnout according to the speed and prestored kilometer sign information; and the image processing module is used for judging whether the rail inner side and/or the rail outer side of the turnout have faults or not according to the acquired rail inner side image and rail outer side image of the turnout and a pre-stored normal image and by adopting a preset deep learning target detection model. By the technical scheme provided by the invention, the inspection in an unattended mode can be realized, various defects of a detection line can be quickly and correctly identified, the rechecking is convenient and fast, and a decision basis can be provided for fault hidden danger early warning and scientific maintenance plan making.
Description
Technical Field
The invention relates to a railway line state inspection technology, in particular to a railway line state inspection system and a railway line state inspection method.
Background
At present, the patrol inspection operation conditions and operation quality records of turnouts and loop lines mainly depend on manual work, the traceability of the mode is low, the turnouts and loop line states are influenced by factors such as the outside world, abrasion and the like, and various defects easily appear on railway lines. If the defects of turnouts and loop lines cannot be found in time in the running process of the vehicle, serious potential safety hazards are likely to be caused. As for manual detection, the tracking information is likely to be confused due to the professional and careless effects of the inspection worker, and therefore, the mode of maintenance work has been difficult to meet the increasingly enhanced requirements of the security and management of the railway line.
In summary, the inspection analysis and the state condition of the turnout and loop images are manually detected at present, the current inspection mode occupies a lot of manpower and material resources, and meanwhile, the quality of manual detection cannot be guaranteed.
Disclosure of Invention
The invention aims to provide a railway line state inspection system and a railway line state inspection method, which are used for solving the problem that the manual inspection quality cannot be guaranteed in the prior art.
In order to achieve the above object, the present invention provides a railway line state inspection system, including: the speed detection module is arranged at the axle end of a wheel of the train or on a motor gear box and is used for detecting the speed of the train; the turnout detection module comprises a plurality of turnout cameras, is arranged at the bottom of the train and is used for respectively acquiring the rail inner side image and the rail outer side image of the turnout at the railway turnout according to the train speed and the prestored kilometer sign information; and the image processing module is used for judging whether the rail inner side and/or the rail outer side of the turnout have faults or not according to the acquired rail inner side image and rail outer side image of the turnout and a pre-stored normal image and by adopting a preset deep learning target detection model.
Preferably, the railway line state inspection system further includes: the loop detection module comprises a loop camera, is arranged at the bottom of the train and is used for acquiring loop images in real time; the image processing module is further used for judging whether the loop has defects or not according to the loop image acquired in real time and the pre-stored initial state of the loop.
Preferably, the switch detection module includes: the train position judging unit is used for judging the position of the train according to the speed and the kilometer sign information so as to determine the time of the train reaching the turnout position; and the turnout image acquisition unit comprises a plurality of turnout cameras and is used for acquiring the rail inner side image and the rail outer side image of the turnout according to the time of the train reaching the turnout position under the condition that the train reaches the turnout position.
Preferably, the switch image acquisition unit includes: the first turnout camera is used for acquiring an image of the outer side of a first steel rail of the turnout; the second turnout camera is used for acquiring an image of the inner side of the first rail of the turnout; the third turnout camera is used for acquiring an image of the inner side of the second steel rail of the turnout; and the fourth turnout camera is used for acquiring an image of the outer side of the second steel rail of the turnout.
Preferably, the railway line state inspection system further includes: and the alarm module is used for alarming under the condition that faults exist on the inner side and/or the outer side of the rail of the turnout and alarming under the condition that the loop line has defects.
Correspondingly, the invention also provides a railway line state inspection method, which comprises the following steps: detecting the speed of the train; acquiring a rail inner side image and a rail outer side image of the turnout at a railway turnout by adopting a plurality of turnout cameras according to the vehicle speed and prestored kilometer sign information; and judging whether the inner side and/or the outer side of the turnout rail has a fault or not according to the acquired inner side image and outer side image of the turnout rail and a pre-stored normal image by adopting a preset deep learning target detection model.
Preferably, the railway line state inspection method further includes: collecting a loop image in real time; and judging whether the loop has defects according to the loop image acquired in real time and the pre-stored initial state of the loop.
Preferably, the respectively acquiring the rail inner side image and the rail outer side image of the turnout at the railway turnout according to the vehicle speed and the prestored kilometer post information comprises: judging the position of the train according to the speed and the kilometer sign information, thereby determining the time when the train reaches the turnout position; and acquiring the rail inner side image and the rail outer side image of the turnout by adopting a plurality of turnout cameras according to the time of the train reaching the turnout position under the condition that the train reaches the turnout position.
Preferably, the acquiring the inside-rail image and the outside-rail image of the turnout by using the plurality of turnout cameras includes: acquiring a rail outer side image of a first steel rail positioned on a turnout by adopting a first turnout camera; acquiring an image of the inner side of a first steel rail of the turnout by adopting a second turnout camera; acquiring a rail inner side image of a second steel rail positioned on the turnout by adopting a third turnout camera; and acquiring the rail outer side image of a second steel rail positioned on the turnout by adopting a fourth turnout camera.
Preferably, the railway line state inspection method further includes: and alarming under the condition that faults exist on the inner side and/or the outer side of the rail of the turnout, and alarming under the condition that a loop line has defects.
The invention realizes the acquisition of the rail inner side image and the rail outer side image of the turnout at the railway turnout through the train speed obtained by detection and the prestored kilometer sign information, and judges whether the rail inner side and/or the rail outer side of the turnout have faults or not by utilizing a deep learning target detection algorithm according to the acquired rail inner side image and the rail outer side image of the turnout and the prestored normal image.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the embodiments of the invention and not to limit the embodiments of the invention. In the drawings:
FIG. 1 is a block diagram of a railway line status inspection system provided by the present invention;
FIG. 2 is a block diagram of another railway line status inspection system provided by the present invention; and
fig. 3 is a flow chart of the railway line state inspection method provided by the invention.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present invention, are intended for purposes of illustration and explanation only and are not intended to limit the scope of the invention.
Fig. 1 is a block diagram of a railway line state inspection system provided by the present invention, and as shown in fig. 1, the system includes a speed detection module 10, a switch detection module 20, and an image processing module 30.
The speed detection module 10 is arranged at the axle end of a wheel of a train or on a motor gear box and is used for detecting the speed of the train. The speed detection module may utilize a sensor, such as a speed sensor.
A plurality of switch cameras arranged at the bottom of the train are required to be arranged at positions where the images of the inner side and the images of the outer side of the rail of the switch can be shot, and the switch cameras are specifically arranged at which positions of the bottom of the train and can be set according to actual conditions.
The image processing module 30 is configured to determine whether a fault exists on the rail inner side and/or the rail outer side of the turnout by using a deep learning target detection model according to the acquired rail inner side image and rail outer side image of the turnout and a pre-stored normal image. The invention relates to a method for detecting turnout faults, which comprises the steps of obtaining a rail inner side image and a rail outer side image of a known turnout in a fault-free condition, and judging whether the turnout has faults or not, such as whether the turnout structure appearance is abnormal or not, whether foreign matters exist or not, and the like by comparing and analyzing the acquired rail inner side image and the rail outer side image of the turnout with the rail inner side image and the rail outer side image in the fault-free condition by adopting a deep learning target detection model.
Those skilled in the art should understand that the deep learning target detection model is obtained by using a deep learning algorithm and through training, the image processing module may pre-train the deep learning target detection model by using training parameters, where the training parameters include rail inner side images and rail outer side images of a turnout with a known fault and normal images (i.e., rail inner side images and rail outer side images with a known fault), and after the deep learning target detection model is trained by using the training parameters, the deep learning target detection model for determining whether a fault exists on the rail inner side and/or the rail outer side of the turnout is obtained, and the deep learning algorithm, the deep learning target detection model and the training process are well-known technologies in the art and are not described herein.
Fig. 2 is a block diagram of another railway line status inspection system provided by the present invention, and as shown in fig. 2, the system further includes a loop detection module 40.
The loop detection module 40 comprises a loop camera, is arranged at the bottom of the train and is used for acquiring loop images in real time; the image processing module 30 is further configured to determine whether a loop has a defect according to the loop image acquired in real time and a pre-stored initial state of the loop. The position that the loop line camera set up can gather the optional position of loop line height for the train bottom.
The loop is a cable with a height and a relief between two steel rails of the railway line, it is easy to understand that the distances between the loop and the ground are different at different positions, and the pre-stored initial state of the loop refers to the state of the loop when the loop is arranged, that is, an image under the condition that the loop is not damaged and the height of the loop is not changed after the loop is arranged. The detection of whether the loop line has defects or not implemented by the loop line detection module 40 in the invention includes whether the loop line cable is damaged or not, and also includes whether the distance between the loop line and the ground at any position is changed compared with the initial state or not.
The loop image at any position is obtained by real-time shooting of the loop camera in the process of train advancing, the loop image obtained by real-time shooting of the loop camera is compared with the initial state of the loop by the image processing module 30, and the loop is considered to have defects under the following two conditions: in the first case, there is a leaky cable condition; in the second case, the loop line is at a different height from the initial state at a certain position.
As shown in fig. 2, the switch detection module 20 includes a train position determination unit 21 and a switch image acquisition unit 22.
The train position determination unit 21 is configured to determine the position of the train according to the speed and the kilometer sign information, so as to determine the time when the train reaches the switch position. Those skilled in the art will appreciate that the kilometer sign information is a known information in advance, a signal is also sent when the train reaches a specific position of the kilometer sign, and the position of the switch is fixed, so that the time when the train reaches the position of the switch can be determined according to the kilometer sign information and the speed of the train.
The switch image acquisition unit 22 includes a plurality of switch cameras for acquiring the inside and outside rail images of the switch when the train arrives at the switch position according to the time when the train arrives at the switch position. The technical scheme provided by the invention is that whether the rail at the turnout position has a fault or not is judged, so the turnout image acquisition unit 22 acquires the rail inner side image and the rail outer side image of the turnout according to the time of the train reaching the position of the turnout, which is obtained by the train position judgment unit 21.
More specifically, the switch image acquisition unit 22 includes four switch cameras, which are a first switch camera, a second switch camera, a third switch camera and a fourth switch camera, respectively.
The first turnout camera is used for acquiring an image of the outer side of a first steel rail of the turnout; the second turnout camera is used for acquiring an image of the inner side of the first steel rail of the turnout; the third turnout camera is used for acquiring an image of the inner side of a second steel rail of the turnout; and the fourth turnout camera is used for acquiring an image of the outer side of the second steel rail positioned on the turnout.
The four switch cameras are arranged at any appropriate position at the bottom of the train, more specifically, the first switch camera is arranged at a position where the image of the outer side of the first steel rail of the switch can be shot at the bottom of the train, the second switch camera is arranged at a position where the image of the inner side of the first steel rail of the switch can be shot at the bottom of the train, the third switch camera is arranged at a position where the image of the inner side of the second steel rail of the switch can be shot at the bottom of the train, and the fourth switch camera is arranged at a position where the image of the outer side of the second steel rail of the switch can be shot at the bottom of the train.
The railway line state inspection system provided by the invention further comprises an alarm module (not shown in the figure), wherein the alarm module is used for alarming under the condition that faults exist on the inner side and/or the outer side of the rail of the turnout and alarming under the condition that the loop line has defects. Specifically, an alarm is given when there is a problem with information displayed on a certain picture.
Fig. 3 is a flowchart of a method for inspecting a status of a railway line according to the present invention, and as shown in fig. 3, the method includes:
step S301, detecting the speed of the train;
step S302, a plurality of turnout cameras are adopted at a railway turnout according to the speed and prestored kilometer sign information to respectively acquire an inside image and an outside image of the turnout;
and step S303, judging whether the inner side of the rail and/or the outer side of the rail of the turnout has a fault or not by adopting a preset deep learning target detection model according to the acquired inner side image and outer side image of the rail of the turnout and a prestored normal image.
The railway line state inspection method provided by the invention further comprises the following steps: collecting a loop image in real time; and judging whether the loop has defects or not according to the loop image acquired in real time and the pre-stored initial state of the loop.
Wherein, according to the speed of a motor vehicle and the kilometer post information of prestoring gather the inboard image of rail and the outside image of rail of switch respectively including at railway switch: judging the position of the train according to the speed and the kilometer post information, thereby determining the time of the train reaching the turnout position; a plurality of turnout cameras are adopted to acquire the rail inner side image and the rail outer side image of the turnout under the condition that the train arrives at the turnout position according to the time that the train arrives at the turnout position.
Wherein, adopt the inboard image of rail and the outside image of rail that a plurality of switch cameras gathered the switch to include: acquiring a rail outer side image of a first steel rail positioned on a turnout by adopting a first turnout camera; acquiring an image of the inner side of a first steel rail of the turnout by using a second turnout camera; acquiring a rail inner side image of a second steel rail positioned on the turnout by adopting a third turnout camera; and acquiring an image of the outer side of the second steel rail positioned on the turnout by adopting a fourth turnout camera.
The railway line state inspection method provided by the invention further comprises the following steps: the alarm is given in the case of a fault in the inner and/or outer rail side of the switch and is used to give an alarm in the case of a defect in the loop.
It should be noted that the specific details and benefits of the method for inspecting the status of a railway line provided by the present invention are similar to those of the system for inspecting the status of a railway line provided by the present invention, and are not described herein again.
Although the embodiments of the present invention have been described in detail with reference to the accompanying drawings, the embodiments of the present invention are not limited to the details of the above embodiments, and various simple modifications can be made to the technical solutions of the embodiments of the present invention within the technical idea of the embodiments of the present invention, and the simple modifications all belong to the protection scope of the embodiments of the present invention.
It should be noted that the various features described in the above embodiments may be combined in any suitable manner without departing from the scope of the invention. In order to avoid unnecessary repetition, the embodiments of the present invention do not describe every possible combination.
In addition, any combination of various different implementation manners of the embodiments of the present invention is also possible, and the embodiments of the present invention should be considered as disclosed in the embodiments of the present invention as long as the combination does not depart from the spirit of the embodiments of the present invention.
Claims (10)
1. A railway line state inspection system, characterized in that, this system includes:
the speed detection module is arranged at the axle end of a wheel of the train or on a motor gear box and is used for detecting the speed of the train;
the turnout detection module comprises a plurality of turnout cameras, is arranged at the bottom of the train and is used for respectively acquiring the rail inner side image and the rail outer side image of the turnout at the railway turnout according to the train speed and the prestored kilometer sign information; and
and the image processing module is used for judging whether the rail inner side and/or the rail outer side of the turnout have faults or not according to the acquired rail inner side image and rail outer side image of the turnout and a pre-stored normal image and by adopting a preset deep learning target detection model.
2. The railway line status inspection system according to claim 1, further comprising:
the loop detection module comprises a loop camera, is arranged at the bottom of the train and is used for acquiring loop images in real time;
the image processing module is further used for judging whether the loop has defects or not according to the loop image acquired in real time and the pre-stored initial state of the loop.
3. The railway line status inspection system according to claim 1 or 2, wherein the switch detection module includes:
the train position judging unit is used for judging the position of the train according to the speed and the kilometer sign information so as to determine the time of the train reaching the turnout position; and
and the turnout image acquisition unit comprises a plurality of turnout cameras and is used for acquiring the rail inner side image and the rail outer side image of the turnout according to the time when the train arrives at the turnout position under the condition that the train arrives at the turnout position.
4. The railway line status inspection system according to claim 3, wherein the switch image acquisition unit includes:
the first turnout camera is used for acquiring an image of the outer side of a first steel rail of the turnout;
the second turnout camera is used for acquiring an image of the inner side of the first steel rail of the turnout;
the third turnout camera is used for acquiring an image of the inner side of the second steel rail of the turnout; and
and the fourth turnout camera is used for acquiring an image of the outer side of the second steel rail of the turnout.
5. The railway line status inspection system according to claim 2, further comprising:
and the alarm module is used for alarming under the condition that faults exist on the inner side and/or the outer side of the rail of the turnout and alarming under the condition that a loop line has defects.
6. A railway line state inspection method is characterized by comprising the following steps:
detecting the speed of the train;
acquiring a rail inner side image and a rail outer side image of the turnout at a railway turnout by adopting a plurality of turnout cameras according to the vehicle speed and prestored kilometer sign information; and
and judging whether the inner side and/or the outer side of the turnout rail has a fault or not by adopting a preset deep learning target detection model according to the acquired inner side image and outer side image of the turnout rail and a prestored normal image.
7. The railway line status inspection method according to claim 6, further comprising:
collecting a loop image in real time; and
and judging whether the loop has defects or not according to the loop image acquired in real time and the pre-stored initial state of the loop.
8. The railway line state inspection method according to claim 6 or 7, wherein the respectively acquiring the rail inside image and the rail outside image of the switch at the railway switch according to the vehicle speed and the pre-stored kilometer post information comprises:
judging the position of the train according to the speed and the kilometer sign information so as to determine the time of the train reaching the turnout position; and
and acquiring the rail inner side image and the rail outer side image of the turnout by adopting a plurality of turnout cameras according to the time when the train arrives at the turnout position under the condition that the train arrives at the turnout position.
9. The railway line state inspection method according to claim 8, wherein the acquiring of the rail inside image and the rail outside image of the turnout with the plurality of turnout cameras comprises:
acquiring a rail outer side image of a first steel rail positioned on a turnout by adopting a first turnout camera;
acquiring an image of the inner side of a first steel rail of the turnout by adopting a second turnout camera;
acquiring a rail inner side image of a second steel rail positioned on the turnout by adopting a third turnout camera; and
and acquiring the rail outside image of a second steel rail positioned on the turnout by adopting a fourth turnout camera.
10. The railway line status inspection method according to claim 6, further comprising:
and alarming under the condition that faults exist on the inner side and/or the outer side of the rail of the turnout, and alarming under the condition that a loop line has defects.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110064273A1 (en) * | 2009-09-11 | 2011-03-17 | Harsco Corporation | Automated turnout inspection |
| CN202854908U (en) * | 2012-10-12 | 2013-04-03 | 昆明铁路局 | Railway line intelligent inspection system |
| CN105501248A (en) * | 2016-02-16 | 2016-04-20 | 株洲时代电子技术有限公司 | Railway line inspection system |
| CN106794851A (en) * | 2014-10-22 | 2017-05-31 | Hp3真实有限责任公司 | Method for measuring and showing the track geometry shape of track equipment |
| CN110248858A (en) * | 2016-12-07 | 2019-09-17 | 西门子交通有限责任公司 | For in rail traffic the image analysis based on track, in particular for method, equipment and the rail vehicle of the image analysis based on rail in railway traffic, especially rolling stock |
| CN112960014A (en) * | 2021-02-02 | 2021-06-15 | 南京效秀自动化技术有限公司 | Rail transit operation safety online real-time monitoring and early warning management cloud platform based on artificial intelligence |
| CN113104063A (en) * | 2021-06-09 | 2021-07-13 | 成都国铁电气设备有限公司 | Comprehensive detection system and method for network rail tunnel |
-
2022
- 2022-09-23 CN CN202211170733.0A patent/CN115610479B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110064273A1 (en) * | 2009-09-11 | 2011-03-17 | Harsco Corporation | Automated turnout inspection |
| CN202854908U (en) * | 2012-10-12 | 2013-04-03 | 昆明铁路局 | Railway line intelligent inspection system |
| CN106794851A (en) * | 2014-10-22 | 2017-05-31 | Hp3真实有限责任公司 | Method for measuring and showing the track geometry shape of track equipment |
| CN105501248A (en) * | 2016-02-16 | 2016-04-20 | 株洲时代电子技术有限公司 | Railway line inspection system |
| CN110248858A (en) * | 2016-12-07 | 2019-09-17 | 西门子交通有限责任公司 | For in rail traffic the image analysis based on track, in particular for method, equipment and the rail vehicle of the image analysis based on rail in railway traffic, especially rolling stock |
| CN112960014A (en) * | 2021-02-02 | 2021-06-15 | 南京效秀自动化技术有限公司 | Rail transit operation safety online real-time monitoring and early warning management cloud platform based on artificial intelligence |
| CN113104063A (en) * | 2021-06-09 | 2021-07-13 | 成都国铁电气设备有限公司 | Comprehensive detection system and method for network rail tunnel |
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