CN114659457A - Track gauge dynamic measurement method based on structured light - Google Patents
Track gauge dynamic measurement method based on structured light Download PDFInfo
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Abstract
The invention discloses a dynamic measuring method of a track gauge based on structured light, which comprises the steps of obtaining a steel rail outline image, obtaining a clear steel rail outline curve and obtaining a dynamic value of the track gauge by establishing a nonlinear relation of space postures between a left camera and a right camera. The track gauge dynamic measurement method based on the structured light disclosed by the invention detects the track gauge of the test line by using the structured light and machine vision methods, realizes accurate and efficient non-contact dynamic measurement, obviously improves the speed of on-line calculation and the anti-interference capability of a detection system during operation, ensures the performance of a track gauge dynamic detection system, and has better practicability.
Description
Technical Field
The invention relates to the technical field of rail transit, in particular to a dynamic measuring method for a track gauge based on structured light.
Background
With the increasing demand of urban public transportation, rail transit is also developing at a rapid pace in recent years as a main transportation means. Urban rail transit plays an important role in the way people travel in many densely populated big cities in the world, including first-line cities of Beijing, Shanghai, Guangzhou, and the like in China. However, while urban rail transit brings convenience to people, the health state of vehicles and rail systems is also influenced, for example: long-term overload operation during early and late peak hours can damage major operating components of the metro vehicle and can quickly deteriorate the irregularity of the operating track. The subway construction in China is in the peak time of construction and operation, and as long as 2020, the Shanghai subway reaches 830 kilometers, and the recent planning reaches 1000 kilometers; by 2025, the total mileage of Beijing subway operation is expected to reach 1600 km. From the above figures, the safety issues of subway operations should be more and more emphasized.
The rapid development of the rail transit industry puts new requirements on the uneven state of the steel rail running by the metro vehicle. At present, the unevenness state of the monitoring steel rail can be quickly and timely monitored in the maintenance work of the subway maintenance department on the subway steel rail, and the monitoring system has a decisive effect on the safe operation of subway vehicles and the maintenance and repair of an operation line.
The track geometry refers to the geometry, relative position and basic size of each part of the track, and the basic components comprise track gauge, level, height, track direction, triangular pits and the like. The track gauge is an important component of rail irregularity, and refers to the minimum distance between two steel rail action edges within 16mm below the rail surface of a railway steel rail, and the deviation of the track gauge can cause the train to block or derail. The overlarge gauge can cause the derailment of the train, and the undersize gauge can accelerate the abrasion between the track and the wheel set, thereby causing serious traffic accidents and economic loss. Therefore, in order to ensure the safe and accurate test of the rail train, the gauge of the test line must be accurately detected to ensure the stable and safe operation of the train.
Currently, the most widely used method in the field of vision-based non-contact three-dimensional measurement is a structured light system, which generally consists of a camera and a projector of structured light, and the measurement principle is similar to that of a stereoscopic vision system. However, the method is only applied to the dynamic measurement of the wear of the vehicle-mounted steel rail, and no corresponding measurement method exists for the gauge.
Therefore, it is an urgent need to provide a method for high-precision and dynamic detection of the track gauge.
Disclosure of Invention
In order to overcome the problems, the inventor of the invention carries out intensive research and designs a dynamic measuring method of the track gauge based on structured light, the method utilizes a dynamic detecting device of the track gauge to obtain a steel rail outline image, a clear steel rail outline curve is obtained by carrying out filtering, form opening operation and skeleton extraction on the outline image, meanwhile, a method for positioning the track gauge point of the steel rail outline curve is provided according to the characteristics of the outline curve by establishing the nonlinear relation of the space postures between a left camera and a right camera, and finally the dynamic value of the track gauge is obtained. The method ensures high precision and robustness of real-time dynamic detection of the track gauge and ensures stable and safe operation of the train, thereby completing the invention.
Specifically, the invention aims to provide a track gauge dynamic measurement method based on structured light, which comprises the following steps:
and step 3, obtaining the track gauge.
The invention has the advantages that:
(1) the track gauge dynamic measurement method based on the structured light provided by the invention detects the track gauge of the test circuit by using the structured light and machine vision methods, thereby realizing accurate and efficient non-contact dynamic measurement;
(2) the structured light-based dynamic gauge measuring method can quickly find the characteristic points of the steel rail profile, and obviously improves the speed of online calculation and the anti-interference capability of a detection system during operation;
(3) the structured light-based dynamic measuring method for the track gauge ensures high precision and robustness of real-time dynamic detection of the track gauge and also ensures stable and safe operation of a train.
Drawings
Fig. 1 shows a process diagram of a dynamic track gauge detection method according to a preferred embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating a process of rail profile image processing according to a preferred embodiment of the present invention;
FIG. 3 is a diagram illustrating the effect of processing a rail profile image according to a preferred embodiment of the present invention;
FIG. 4 illustrates a schematic diagram of left and right side structured light vision sensor calibration in accordance with a preferred embodiment of the present invention;
fig. 5 is a graph showing a measurement result and an error in an experimental example of the present invention.
Detailed Description
The invention is explained in more detail below with reference to the figures and examples. The features and advantages of the present invention will become more apparent from the description.
The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.
The invention provides a track gauge dynamic measurement method based on structured light by combining the structured light and machine vision, as shown in figure 1, the method comprises the following steps:
and step 3, obtaining the track gauge.
The structured light based dynamic gauge measurement method is further described below:
According to a preferred embodiment of the present invention, the rail profile image is obtained by a dynamic gauge detection device, as shown in figure 1,
the dynamic track gauge detection device comprises a structured light vision sensor and a high-speed image acquisition system.
In a further preferred embodiment, the structured light vision sensor has two groups for acquiring the surface profile of the left rail and the surface profile of the right rail respectively,
preferably, the structured light vision sensor comprises a camera and a laser.
More preferably, the camera is an area-array camera and the laser is a line laser.
Wherein, each group of structured light vision sensors preferably comprises an area-array camera and a line laser.
In the present invention, the area-array camera and the line laser are common devices in the prior art, and the model thereof is not particularly limited.
In a further preferred embodiment, the image capture range of the area-array camera covers at least a part below 75% of the rail surface,
the irradiation range of the line laser covers the bottom surface of the steel rail.
In the invention, the shooting ranges of the area-array cameras of the structured light visual sensors on the left side and the right side are preferably consistent, the irradiation ranges of the line lasers are also consistent, and the surface profiles of the left rail and the right rail are acquired by utilizing the triangulation principle.
Preferably, the structured light vision sensor is mounted on a rail detection beam of a rail detection vehicle for rail detection, and the structured light vision sensor can continuously measure and obtain the profile of the steel rail to be detected along with the movement of the rail detection vehicle along the steel rail.
The area array camera at the left steel rail and the area array camera at the right steel rail are arranged in the same plane.
More preferably, the area-array camera operates synchronously with the line laser.
According to a preferred embodiment of the present invention, the high-speed image acquisition system is a high-speed image acquisition card.
The method comprises the following steps of acquiring a rail image by a high-speed image acquisition card, and transmitting the rail image acquired by an area array camera.
According to a preferred embodiment of the present invention, the dynamic gauge detecting device further comprises an odometer, the odometer comprises a photoelectric encoder and a signal controller,
wherein, the photoelectric encoder is used for converting the rotation angle of the axle into a pulse signal and transmitting the pulse signal to the signal controller,
the signal controller is used for acquiring the running speed of the current train and controlling the area array camera and the line laser to work synchronously.
Preferably, the photoelectric encoder and the signal controller are both arranged on a train axle.
In the invention, when a train runs, a photoelectric encoder converts a detected turning angle of an axle into a pulse signal and sends the pulse signal to a signal controller; the signal controller firstly obtains the running speed of the current train according to the pulse number generated by the photoelectric encoder, and further obtains the position of the train after a period of time.
Specifically, the signal controller firstly calculates the running speed v of the current train according to the pulse number N generated by the photoelectric encoderiAs set forth in the following formula:
wherein D is the diameter (m) of the wheel, and delta is the pulse number generated by the photoelectric encoder for 0.25s when the train runs.
At a certain location S0The starting point of the train is the position of the train after the time T is as follows:
in a further preferred embodiment, the signal controller generates a trigger signal to control the area-array camera to work synchronously with the line laser.
According to a preferred embodiment of the present invention, the dynamic gauge detecting device further includes an upper computer for receiving and storing the rail profile transmitted by the high-speed image acquisition system.
The outline of the steel rail to be detected, which is acquired by the structured light vision sensor, is sent to an upper computer through a high-speed image acquisition card, and the light bar image is captured by the area array camera for subsequent processing.
In a further preferred embodiment, the signal controller is in communication connection with an upper computer.
The dynamic gauge detection device controls the camera and the laser to work synchronously through the signal controller, continuously obtains the outline of the steel rail to be detected, and transmits the outlines of the left and right steel rails obtained by the camera to the upper computer for storage through the high-speed image acquisition card.
And 2, processing the acquired contour image.
In the invention, the steel rail profile map which is obtained and stored in an upper computer is shown as the following formula:
where i, j denote the reference numerals of the rows and columns of pixels, respectively.
Preferably, as shown in fig. 2, step 2 comprises the following sub-steps:
and 2-1, filtering the background of the steel rail outline image.
According to a preferred embodiment of the invention, a large part of the background of the rail contour image is filtered out by means of graying.
Wherein, preferably, the [250- & ltSUB & gt, 255- & ltSUB & gt ] pixel value range of the image laser light band concentration is selected for filtering, namely, the R component image is taken as the original gray image.
The color is synthesized by three channels of RGB, when three components are displayed separately, only one channel is provided, and the image display is not color.
In a further preferred embodiment, graying is performed by a method represented by the following formula:
H(x,y)＝a*g(x,y)r
h (x, y) represents a grayed rail contour digital image, a is a coefficient, and r is an index.
In the present invention, in order to further extract information of the light band, it is preferable to process the image by a graying method of Exponential index conversion.
And 2-2, processing the edge of the steel rail outline image.
The inventors have found that speckle noise due to diffuse reflection of light still exists at the edge of the track profile after the graying, and therefore, it is preferable to further filter the image to smooth the profile.
According to a preferred embodiment of the present invention, the filtering process for the rail contour image is performed according to the following formula:
g (x, y) is a digitized image of the steel rail profile after Gaussian filtering, and sigma determines the width of a Gaussian function.
In the invention, a Gaussian filter is adopted to further filter the rail outline image to smooth the outline, namely, a gray level image and a Gaussian kernel GσAnd performing convolution.
And 2-3, denoising the steel rail contour image.
According to a preferred embodiment of the present invention, the noise is removed from the rail profile image according to the following method:
wherein S (x, y) represents a digitized image of the rail contour after image opening operation, theta and theta
Respectively representing erosion operation and dilation operation, and B representing an open operation structural element.
In the invention, the noise of the image is further removed by utilizing the image morphology opening operation, and the operation firstly carries out corrosion operation and then carries out expansion operation on the image.
And 2-4, thinning the contour line of the steel rail contour image to obtain the center line of the contour line.
According to a preferred embodiment of the present invention, the center position of the contour line is obtained by:
wherein x is0Is the central coordinate of a Gaussian curve, a1And a2Obtained by the following process:
order to
Then there are:
wherein F (x) lnf (x),
n indicates that there are n sample points on the profile,
a is the gray value of the section of the profile, x0Is the central coordinate of the gaussian curve and ρ is the width of the contour line.
In the invention, the contour line after the opening operation is refined by a Gaussian fitting method, the parameter to be estimated is obtained by using Householder transformation to solve, and the central position of the contour line is obtained
The steel rail outline can be refined by utilizing the outline center.
In the invention, fig. 3 shows a rail contour diagram after graying, gaussian filtering, opening operation and skeleton extraction, which illustrates that a clear rail contour curve can be obtained after machine vision processing.
And step 3, obtaining the track gauge.
Preferably, step 3 comprises the following sub-steps:
and 3-1, calibrating the space attitude coordinate of the track gauge.
In the invention, the mapping relation of the pixel coordinate systems of the images obtained by the left and right side structured light vision sensors is required to be established so as to calibrate the space attitude coordinate of the track gauge.
According to a preferred embodiment of the invention, the spatial attitude coordinates of the gauge are calibrated according to a method comprising the following steps:
and 3-1-1, obtaining a conversion relation between image pixel coordinates corresponding to the left camera and the right camera.
In the present invention, since the cameras at the left rail and the right rail are installed in the same plane, the coordinate systems of the left and right cameras can be respectively represented as Oc1xc1yc1zc1And Oc2xc2yc2zc2The image pixel coordinate system can be respectively represented as Op1xp1yp1zp1And Op2xp2yp2zp2As shown in fig. 4.
Since the image pixel coordinates of the left and right cameras are in a spatial plane, according to a preferred embodiment of the present invention, the image pixel coordinate (x) corresponding to any point in the measurement space in the left camera is obtained by the following formulap1,yp1) Conversion relation (x) between image pixel coordinates corresponding to right camerap2,yp2)：
Wherein beta is1～β6Are conversion coefficients.
And 3-1-2, calibrating the distortion of the image pixel coordinates obtained by the camera.
The inventor researches and discovers that the lens of the camera has distortion, and distortion calibration needs to be carried out on the pixel coordinates of the image.
According to a preferred embodiment of the invention, the distortion of the image pixel coordinates is calibrated by:
wherein the distorted pixel coordinates are (X, Y), Xp，ypRepresenting the pixel coordinates before distortion.
r2＝X2+Y2
Wherein k is1、k2Represents a radial distortion coefficient; b1、b2Representing a thin edge diameter distortion coefficient; s1、s2Representing the off-center distortion coefficient.
In the invention, the distortion types considered in the calibration process are second-order radial distortion, twill distortion and eccentric distortion.
And 3-1-3, obtaining the mapping relation of the pixel coordinate systems of the camera images on the left side and the right side.
According to a preferred embodiment of the present invention, through the coordinate transformation and distortion calibration, the mapping relationship between the left and right camera image pixel coordinate systems is obtained as shown in the following formula:
wherein, there are 18 unknown parameters (k respectively) in the above formula11，k12，k21，k22，b11，b12，b21，b22，s11，s12，s21，s22，β1-β6) According to the freedom degree constraint, the number of the needed calibration data pairs is more than the number of the parameters, and 20 pairs are preferred.
Preferably, the present invention utilizes a least squares fit method to obtain values for the position parameters from a sufficient number of pairs of calibration data.
In the invention, the values of unknown parameters are obtained through the steps, and further the mapping relation of the image pixel coordinate systems of the cameras at the left side and the right side of the steel rail is established, so that the spatial attitude coordinate of the steel rail image can be converted.
And 3-2, positioning a track gauge point to obtain the track gauge.
Preferably, step 3-2 comprises the following sub-steps:
and 3-2-1, obtaining a characteristic distance curve of the steel rail profile.
In the invention, the steel rail profile curve refined in the step 2 is shown as the following formula:
l (i) (x (i)), y (i)), i (1-M)), and M is the number of curve data points, and the distance between any point on the steel rail contour curve and the front and back M data points is defined as the characteristic distance of the contour curve.
According to a preferred embodiment of the invention, the characteristic distance of the profile curve is obtained by:
D(j)＝||l(j-M)-l(j+M)||,j∈[1+M,N-M]
in a further preferred embodiment, the characteristic distance curve of the rail profile is obtained by the following formula:
T(j)＝(j,D(j)),j∈[1+M,N-M]
in the invention, each characteristic distance of the rail contour curve forms a characteristic distance curve.
And 3-2-2, positioning the gauge points of the outline curves of the steel rails on the left side and the right side.
In the present invention, considering that a smaller characteristic distance indicates a closer relationship between the point and two points before and after the point, according to a preferred embodiment of the present invention, the point (T) corresponding to the minimum value of the characteristic distance curve is set to be the point (T)-1(min[T(j)]),min[T(j)]) As characteristic points of the curve profile.
Preferably, the characteristic curve of the actual rail profile is obtained by:
l＝(x((T-1(min[T(j)])),y((T-1(min[T(j)])))
in a further preferred embodiment, the coordinates of the characteristic points (i.e., the gauge points) of the rail profile curves on the left and right sides are obtained by the following formula:
and k is the number of the position points of the feature points in the pixel coordinate systems of the left camera and the right camera.
And 3-2-3, mapping the outline images of the left and right steel rail wheels to the pixel coordinate system on the same side to obtain the rail gauge.
According to a preferred embodiment of the invention, the rail contour image of the right-hand camera is mapped onto the pixel coordinates of the left-hand camera.
Wherein, in the pixel coordinate system, the right rail contour image is recorded as l2' (k) left rail profile image is denoted as l1(k)。
In a further preferred embodiment, the distance between the track pitch points of the rail contour images obtained by the left and right cameras in the same pixel coordinate system is defined as c (k), and the track pitch is obtained by the following formula:
G＝min(c(k+1))
the structured light-based dynamic measuring method for the track gauge not only can realize accurate, efficient and real-time non-contact dynamic measurement of the track gauge, but also can quickly search the characteristic points of the outline of the steel rail, and improves the speed of on-line calculation and the anti-interference capability of a detection system during operation to a certain extent.
Examples
The present invention is further described below by way of specific examples, which are merely exemplary and do not limit the scope of the present invention in any way.
Example 1
The dynamic gauge detection device adopted by the embodiment comprises a structured light vision sensor, a high-speed image acquisition card, a milemeter and an upper computer, wherein,
the structured light vision sensors are provided with two groups which are respectively used for detecting a left steel rail and a right steel rail, each group consists of an area array camera and a line laser, and the two groups work synchronously; the shooting range of the area array camera at least covers the part below 75% of the rail surface, and the irradiation range of the line laser covers the bottom surface of the steel rail; the area-array camera and the line laser are arranged on a rail detection beam of the rail detection vehicle, and the area-array camera at the left steel rail and the area-array camera at the right steel rail are arranged in the same plane;
the odometer comprises a photoelectric encoder and a signal controller which are arranged on a train axle;
the track gauge is detected according to the following steps:
1. acquiring outline images of the steel rails on the left side and the right side through the dynamic gauge detection device, and storing the outline images in an upper computer;
2. performing graying treatment on the steel rail profile map stored in an upper computer by adopting a method shown as the following formula:
h (x, y) represents a grayed rail contour digital image, a is a coefficient, and r is an index;
further filtering the contour line with a gaussian filter to smooth the contour line, as follows:
g (x, y) is a steel rail profile digitized image after Gaussian filtering, and sigma determines the width of a Gaussian function;
and further removing noise points from the image by using an image morphology opening operation, wherein the noise points are represented by the following formula:
wherein S (x, y) represents a digitized image of the rail profile after image opening operation, theta and theta
Respectively representing corrosion operation and expansion operation, and B representing an opening operation structural element;
adopting a Gaussian fitting method to refine the contour line after the opening operation to obtain the center line of the contour line:
the gaussian function is:
in the formula: a is the gray value of the section of the profile, x0Is the central coordinate of the Gaussian curve, rho is the width of the contour line, and the logarithm is taken at the two sides to obtainTo:
let f (x) lnf (x),
n sampling points are arranged on the contour, and the parameter a to be estimated is solved by using a least square method0、a1、a2：
Order to
The equation is obtained:
and (3) solving by using Householder transformation to obtain a parameter to be estimated, wherein the central position of the outline is as follows:
3. the image pixel coordinate (x) corresponding to any point in the measurement space at the left camera is obtained by the following formulap1,yp1) Conversion relation (x) between image pixel coordinates corresponding to right camerap2,yp2)：
The distortion of the image pixel coordinates is calibrated by:
wherein the distorted pixel coordinates are (X, Y),
r2＝X2+Y2；
through the coordinate conversion and the distortion calibration, the mapping relation of the image pixel coordinate systems of the left camera and the right camera is obtained, which is shown as the following formula:
the characteristic distance of the profile curve is obtained by:
D(j)＝||l(j-M)-l(j+M)||,j∈[1+M,N-M]
obtaining a characteristic distance curve of the rail profile by the following formula:
T(j)＝(j,D(j)),j∈[1+M,N-M]；
the point (T) corresponding to the minimum value of the characteristic distance curve-1(min[T(j)]),min[T(j)]) As the characteristic point of the curved profile,
the characteristic curve of the actual rail profile is obtained by the following formula:
l＝(x((T-1(min[T(j)])),y((T-1(min[T(j)])))
the characteristic point coordinates of the rail contour curves on the left side and the right side are obtained by the following formula:
defining the distance between the track distance points of the steel rail outline images obtained by the left and right cameras in the same pixel coordinate system as c (k), and obtaining the track distance according to the following formula:
G＝min(c(k+1))。
examples of the experiments
By adopting the device and the method in the embodiment 1, a track gauge detection field test is carried out by utilizing the track gauge trolley, and track gauge measurement is repeatedly carried out for multiple times by utilizing 20 positions; then, the results of the rechecking with a gaging rule are shown in table 1.
TABLE 1
Further, fig. 5 shows the comparison and error curves of the measured track gauge value and the measured track gauge value at 20 points measured by the method described in example 1.
As can be seen from table 1 and fig. 5, the apparatus and method according to the embodiment of the present invention can achieve accurate measurement of the gauge.
The invention has been described in detail with reference to specific embodiments and illustrative examples, but the description is not intended to be construed in a limiting sense. Those skilled in the art will appreciate that various equivalent substitutions, modifications or improvements may be made to the technical solution of the present invention and its embodiments without departing from the spirit and scope of the present invention, which fall within the scope of the present invention.
Claims (9)
1. A track gauge dynamic measurement method based on structured light is characterized by comprising the following steps:
step 1, acquiring a steel rail outline image;
step 2, processing the acquired contour image;
and step 3, obtaining the track gauge.
2. The structured light-based dynamic gauge measuring method according to claim 1, wherein in step 1, a rail profile image is obtained through a dynamic gauge detecting device;
the dynamic gauge detection device comprises a structured light vision sensor and a high-speed image acquisition system.
3. The structured light based dynamic gauge measuring method according to claim 2, wherein the structured light vision sensor has two groups for acquiring the surface profile of the left rail and the surface profile of the right rail respectively;
the structured light vision sensor includes a camera and a laser.
4. The structured light-based dynamic gauge measuring method according to claim 2, wherein the dynamic gauge detecting device further comprises an odometer, and the odometer comprises a photoelectric encoder and a signal controller.
5. The structured light-based dynamic gauge measuring method according to claim 4, wherein the photoelectric encoder and the signal controller are both arranged on a train axle.
6. The structured light based dynamic gauge measuring method according to claim 1, wherein step 2 comprises the sub-steps of:
step 2-1, filtering the background of the steel rail outline image;
step 2-2, processing the edge of the steel rail outline image;
step 2-3, denoising the steel rail outline image;
and 2-4, thinning the contour line of the steel rail contour image to obtain the center line of the contour line.
7. The structured light based dynamic gauge measuring method according to claim 1, wherein step 3 comprises the following sub-steps:
step 3-1, calibrating the space attitude coordinate of the gauge;
and 3-2, positioning a track gauge point to obtain the track gauge.
8. The dynamic rail gauge measuring method based on the structured light as claimed in claim 7, wherein the spatial attitude coordinates of the rail gauge are calibrated according to a method comprising the following steps:
step 3-1-1, obtaining a conversion relation between image pixel coordinates corresponding to the left camera and the right camera;
step 3-1-2, calibrating the distortion of the image pixel coordinate obtained by the camera;
and 3-1-3, obtaining the mapping relation of the pixel coordinate systems of the camera images on the left side and the right side.
9. The structured light based dynamic gauge measuring method according to claim 7, wherein step 3-2 comprises the sub-steps of:
step 3-2-1, obtaining a characteristic distance curve of the steel rail profile;
step 3-2-2, positioning gauge points of the outline curves of the steel rails on the left side and the right side;
and 3-2-3, mapping the outline images of the left and right steel rail wheels to the pixel coordinate system on the same side to obtain the rail gauge.
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| CN116182737A (en) * | 2023-02-10 | 2023-05-30 | 同济大学 | Wheel track dynamic position monitoring method and system based on laser contour sensor |
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