Durability test method and system for pantograph contact power grid for road truck

Abstract

The invention discloses a durability test method and system for a pantograph contact power grid for a road truck, and the method comprises the steps: S1, collecting pantograph images through a high-definition area-array camera, and recognizing a to-be-detected pantograph image in the pantograph images through a Gabor filter and a support vector machine; s2, extracting a pantograph edge image from the to-be-detected pantograph image by using an improved edge detection algorithm; s3, positioning the contour of the pantograph based on the pantograph edge image, and obtaining the contour similarity of the pantograph through a similarity calculation formula by using an invariant moment; and S4, obtaining the wear degree of the pantograph contact power grid for the road truck based on the contour similarity, and judging the durability of the pantograph contact power grid based on the wear degree. The pantograph image is identified by using the Gabor filter and the support vector machine (SVM), so that the shape information of the pantograph can be effectively extracted, and the identification accuracy of the pantograph image to be detected is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of pantograph detection, and in particular relates to a method and system for testing the durability of a pantograph contact power grid for a highway truck. Background Art

[0002] There is a key technical difficulty in the durability test of the pantograph and the contact grid of highway trucks. When the pantograph is driving at high speed, the dynamic contact force between the pantograph and the contact grid will fluctuate and change dramatically. This instability of contact force may cause excessive wear or even damage to the pantograph carbon plate. At the same time, the fluctuation of contact force may also cause arc discharge between the pantograph and the contact grid. Arc discharge will not only cause certain damage to the pantograph and the contact grid, but may also affect the stability of the power supply system.

[0003] In addition, under complex and changing road conditions and climatic conditions, the dynamic height of the pantograph will also change frequently. If the height control of the pantograph is not accurate and stable enough, it may lead to a decrease in the contact quality with the contact grid, or even a loss of contact. This will not only affect the continuity of power supply, but also may cause mechanical damage to the pantograph and the contact grid. Summary of the invention

[0004] In order to solve the above problems, the present invention proposes a method for testing the durability of a pantograph contact grid for a highway truck, the method comprising:

[0005] Step S1, using a high-definition area array camera to collect a pantograph image, and using a Gabor filter and a support vector machine to identify a pantograph image to be detected in the pantograph image;

[0006] Step S2, using the improved edge detection algorithm to extract the pantograph edge image from the pantograph image to be detected;

[0007] Step S3, locating the contour of the pantograph based on the pantograph edge image, and obtaining the pantograph contour similarity by using the invariant moment through the similarity calculation formula;

[0008] Step S4: acquiring the degree of wear of the pantograph contact network for highway trucks based on the contour similarity, and determining the durability of the pantograph contact network based on the degree of wear.

[0009] Optionally, in step S1, the process of using a Gabor filter and a support vector machine to identify the pantograph image to be detected in the pantograph image specifically includes:

[0010] Using a multi-scale directional Gobar filter to filter the pantograph image to obtain shape information of the pantograph;

[0011] Convolution is performed on the shape information of the pantograph to extract a pantograph feature map, the pantograph feature map is divided into a plurality of grids, and the mean and variance of each grid are calculated;

[0012] The calculated mean and variance are connected in series to form a feature descriptor, and a support vector machine is used to judge the pantograph based on the feature descriptor.

[0013] Optionally, the calculation formula for obtaining the shape information of the pantograph after filtering the pantograph image using a multi-scale directional Gobar filter is:

[0014] g(x,y)=s(x,y)×w(x,y)

[0015] Among them, s(x,y) is the sine function, w(x,y) is the Gaussian kernel function, and x and y are the horizontal and vertical pixel coordinates of the pantograph image.

[0016] Optionally, the Gaussian kernel function is:

[0017]

[0018] Optionally, in step S2, the improved edge detection algorithm is specifically:

[0019] Using a self-median filter and a guided filter to perform image denoising on the pantograph image to be detected, a denoised image is obtained;

[0020] Calculating pixel partial derivatives using the Sobel operator, and convolving the obtained pixel partial derivatives with the denoised image to obtain image gradient information;

[0021] The image gradient information is compared with two adjacent interpolations with the same gradient to obtain selected edge points, and the edge of the pantograph image to be detected is obtained based on the selected edge points.

[0022] Optionally, the calculation formula of the guided filter is:

[0023]

[0024] Among them, a k 、b k For the window w k When the center of is located at k, the constant coefficient of the linear function, S is the guide image value, and i and k are pixel indexes.

[0025] Optionally, in step S3, the process of locating the contour of the pantograph based on the pantograph edge image and obtaining the pantograph contour similarity by using the invariant moment through the similarity calculation formula specifically includes:

[0026] Calculate the geometric moment expression of the pantograph edge image in a discrete state when the order is p+q;

[0027] Calculate the center moment expression based on the geometric moment expression, and calculate the center distance normalization formula based on the center distance expression;

[0028] Based on the normalized formula, the pantograph profile similarity is calculated using the similarity formula using the invariant moment.

[0029] Optionally, the geometric moment expression is:

[0030]

[0031] Wherein, M and N are the maximum values ​​of the horizontal and vertical coordinates of the pantograph image to be detected.

[0032] The present invention also discloses a pantograph contact grid durability testing system for highway trucks, the system comprising:

[0033] An image acquisition module, used for acquiring a pantograph image using a high-definition area array camera, and identifying a pantograph image to be detected in the pantograph image using a Gabor filter and a support vector machine;

[0034] An edge acquisition module, used for extracting a pantograph edge image from the pantograph image to be detected using an improved edge detection algorithm;

[0035] A similarity calculation module, used for locating the contour of the pantograph based on the pantograph edge image, and obtaining the pantograph contour similarity by using an invariant moment through a similarity calculation formula;

[0036] The durability determination module is used to obtain the degree of wear of the pantograph contact network for highway trucks based on the contour similarity, and determine the durability of the pantograph contact network based on the degree of wear.

[0037] Optionally, in the image acquisition module, the process of using a Gabor filter and a support vector machine to identify the pantograph image to be detected in the pantograph image specifically includes:

[0038] Using a multi-scale directional Gobar filter to filter the pantograph image to obtain shape information of the pantograph;

[0039] Convolution is performed on the shape information of the pantograph to extract a pantograph feature map, the pantograph feature map is divided into a plurality of grids, and the mean and variance of each grid are calculated;

[0040] The calculated mean and variance are connected in series to form a feature descriptor, and a support vector machine is used to judge the pantograph based on the feature descriptor.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] The present invention can effectively extract the shape information of the pantograph and improve the recognition accuracy of the pantograph image to be detected by using Gabor filter and support vector machine (SVM) to identify the pantograph image. The improved edge detection algorithm is combined with the median filter and the guide filter for image denoising, which enhances the robustness of the system under different lighting and noise conditions. The pixel partial derivative is calculated using the Sobel operator, and the edge image of the pantograph can be accurately extracted in combination with the image gradient information, providing accurate basic data for subsequent contour positioning. The contour of the pantograph is located based on the pantograph edge image, and the pantograph contour similarity is obtained by using the invariant moment through the similarity calculation formula, which can accurately evaluate the degree of wear of the pantograph contact network. Based on the evaluation of the degree of wear, the durability of the pantograph contact network can be effectively judged, providing a scientific basis for maintenance and replacement. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0044] Figure 1 The present invention is a method step diagram of a method for testing the durability of a pantograph contact grid for a highway truck according to an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0046] First, some specific terms in this embodiment are explained and illustrated:

[0047] Pantograph, pantograph is an electrical device that electric traction locomotives use to obtain electrical energy from the contact network. It is installed on the roof of the locomotive or EMU.

[0048] Area array camera: The target surface is rectangular or square. This embodiment uses a plurality of area array cameras to photograph the pantograph, thereby making the image more three-dimensional and less distorted.

[0049] Embodiment 1

[0050] like Figure 1 As shown, in this embodiment, a method for testing the durability of a pantograph contact grid for a highway truck is provided, and the method includes:

[0051] Step S1, using a high-definition area array camera to collect a pantograph image, using a Gabor filter and a support vector machine to identify a pantograph image to be detected in the pantograph image, specifically comprising: using a multi-scale Gobar filter to filter the pantograph image to obtain shape information of the pantograph; convolving the shape information of the pantograph to extract a pantograph feature map, dividing the pantograph feature map into a number of grids, and calculating the mean and variance of each grid; connecting the calculated mean and variance in series into a feature descriptor, and using a support vector machine to judge the pantograph based on the feature descriptor.

[0052] The calculation formula of the pantograph shape information obtained by filtering the pantograph image using a multi-scale Gobar filter is:

[0053] g(x,y)=s(x,y)×w(x,y)

[0054] Among them, s(x,y) is the sine function, w(x,y) is the Gaussian kernel function, and x and y are the horizontal and vertical pixel coordinates of the pantograph image.

[0055] The sine function is:

[0056] s(x,y)=exp(i2πf 0 (xcosθ+ysinθ)).

[0057] The Gaussian kernel function is:

[0058]

[0059] The pantograph image (in this embodiment, because the area array camera is installed above the pantograph, the linear array image of the roof pantograph is obtained) is convolved with the pantograph shape information g(x, y) to obtain the pantograph feature map:

[0060] F(x,y)=I(x,y)×g(x,y).

[0061] The obtained feature map is divided into three types of grids: 1×1, 2×2, and 4×4. The mean and variance in each grid are calculated respectively. The calculated mean and variance are connected in series to form a feature descriptor, and the support vector machine is used to judge the pantograph based on the feature descriptor.

[0062] Step S2: using the improved edge detection algorithm to extract the pantograph edge image from the pantograph image to be detected.

[0063] The improved edge detection algorithm is specifically as follows: using a self-median filter and a guided filter to perform image denoising on the pantograph image to be detected to obtain a denoised image; using a Sobel operator to calculate pixel partial derivatives, and convolving the obtained pixel partial derivatives with the denoised image to obtain image gradient information;

[0064] The calculation formula of the guided filter is:

[0065]

[0066] Among them, a k , b k For the window w k When the center of is located at k, the constant coefficient of the linear function, S is the guide image value, and i and k are pixel indexes.

[0067] By minimizing the difference between the output image and the input image, the cost function is minimized and the constant coefficient a is obtained. k , b k :

[0068]

[0069] Among them, t is the input image, ε is the penalty parameter, which is calculated using the least squares method:

[0070]

[0071] Among them, u k , is the filter window w k The mean and variance of all pixels in the filter window, |w| is the total number of pixels in the filter window.

[0072] The image gradient information is compared with two adjacent interpolations with the same gradient to obtain selected edge points, and the edge of the pantograph image to be detected is obtained based on the selected edge points.

[0073] This embodiment uses the Sobel operator in four directions to calculate partial derivatives:

[0074]

[0075]

[0076] Among them, P x (i,j),P y (i,j),P 45 (i,j) and P 135 (i,j) is the partial derivative of pixel point (i,j) in four directions.

[0077] The gradient amplitude is calculated based on the above partial derivatives and the pantograph edge image:

[0078]

[0079] Step S3, locating the contour of the pantograph based on the pantograph edge image, and obtaining the pantograph contour similarity by using the invariant moment through the similarity calculation formula, specifically comprising:

[0080] Calculate the geometric moment expression of the pantograph edge image in a discrete state when the order is p+q; calculate the center moment expression based on the geometric moment expression, and calculate the center distance normalization formula based on the center distance expression; based on the normalization formula, calculate the pantograph contour similarity using the invariant moment through the similarity formula.

[0081] The geometric moment expression is:

[0082]

[0083] Wherein, M and N are the maximum values ​​of the horizontal and vertical coordinates of the pantograph image to be detected.

[0084] The central moment expression is:

[0085]

[0086] The pantograph profile similarity is calculated as:

[0087]

[0088] Step S4: acquiring the degree of wear of the pantograph contact network for highway trucks based on the contour similarity, and determining the durability of the pantograph contact network based on the degree of wear.

[0089] The degree of wear is indicated based on the similarity result, and the smaller the similarity result, the smaller the degree of wear.

[0090] Embodiment 2

[0091] A pantograph contact grid durability test system for highway trucks, the system comprising:

[0092] The image acquisition module is used to use a high-definition area array camera to collect pantograph images, and use Gabor filters and support vector machines to identify the pantograph images to be detected in the pantograph images, specifically including: using a multi-scale Gobar filter to filter the pantograph image to obtain the shape information of the pantograph; convolving the shape information of the pantograph to extract the pantograph feature map, dividing the pantograph feature map into a number of grids, and calculating the mean and variance of each grid; connecting the calculated mean and variance in series into a feature descriptor, and using a support vector machine to judge the pantograph based on the feature descriptor.

[0093] The calculation formula of the pantograph shape information obtained by filtering the pantograph image using a multi-scale Gobar filter is:

[0094] g(x,y)=s(x,y)×w(x,y)

[0095] Among them, s(x,y) is the sine function, w(x,y) is the Gaussian kernel function, and x and y are the horizontal and vertical pixel coordinates of the pantograph image.

[0096] The sine function is:

[0097] s(x,y)=exp(i2πf 0 (xcosθ+ysinθ)).

[0098] The Gaussian kernel function is:

[0099]

[0100] The pantograph image (in this embodiment, because the area array camera is installed above the pantograph, the linear array image of the roof pantograph is obtained) is convolved with the pantograph shape information g(x, y) to obtain the pantograph feature map:

[0101] F(x,y)=I(x,y)×g(x,y).

[0102] The obtained feature map is divided into three types of grids: 1×1, 2×2, and 4×4. The mean and variance in each grid are calculated respectively. The calculated mean and variance are connected in series to form a feature descriptor, and the support vector machine is used to judge the pantograph based on the feature descriptor.

[0103] The edge acquisition module is used to extract the pantograph edge image from the pantograph image to be detected by using the improved edge detection algorithm.

[0104] The improved edge detection algorithm is specifically as follows: using a self-median filter and a guided filter to perform image denoising on the pantograph image to be detected to obtain a denoised image; using a Sobel operator to calculate pixel partial derivatives, and convolving the obtained pixel partial derivatives with the denoised image to obtain image gradient information;

[0105] The calculation formula of the guided filter is:

[0106]

[0107] Among them, a k , b k For the window w k When the center of is located at k, the constant coefficient of the linear function, S is the guide image value, and i and k are pixel indexes.

[0108] By minimizing the difference between the output image and the input image, the cost function is minimized and the constant coefficient a is obtained. k , b k :

[0109]

[0110] Among them, t is the input image, ε is the penalty parameter, which is calculated using the least squares method:

[0111]

[0112] Among them, u k , is the filter window w k The mean and variance of all pixels in the filter window, |w| is the total number of pixels in the filter window.

[0113] The image gradient information is compared with two adjacent interpolations with the same gradient to obtain selected edge points, and the edge of the pantograph image to be detected is obtained based on the selected edge points.

[0114] This embodiment uses the Sobel operator in four directions to calculate partial derivatives:

[0115]

[0116] Among them, P x (i,j),P y (i,j),P 45 (i,j) and P 135 (i,j) is the partial derivative of pixel point (i,j) in four directions.

[0117] The gradient amplitude is calculated based on the above partial derivatives and the pantograph edge image:

[0118]

[0119] The similarity calculation module is used to locate the contour of the pantograph based on the pantograph edge image, and obtain the pantograph contour similarity by using the invariant moment through the similarity calculation formula, specifically including:

[0120] Calculate the geometric moment expression of the pantograph edge image in a discrete state when the order is p+q; calculate the center moment expression based on the geometric moment expression, and calculate the center distance normalization formula based on the center distance expression; based on the normalization formula, calculate the pantograph contour similarity using the invariant moment through the similarity formula.

[0121] The geometric moment expression is:

[0122]

[0123] Wherein, M and N are the maximum values ​​of the horizontal and vertical coordinates of the pantograph image to be detected.

[0124] The central moment expression is:

[0125]

[0126] The pantograph profile similarity is calculated as:

[0127]

[0128] The durability determination module is used to obtain the wear degree of the pantograph contact network for highway trucks based on the profile similarity, and determine the durability of the pantograph contact network based on the wear degree. The wear degree is represented based on the similarity result, and the smaller the similarity result, the smaller the wear degree.

[0129] The embodiments of the present disclosure are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present disclosure should be included in the scope of protection of the present disclosure.

Claims

1. A method for testing the durability of a pantograph contact grid for a highway truck, characterized in that: The method specifically comprises: Step S1, using a high-definition area array camera to collect a pantograph image, and using a Gabor filter and a support vector machine to identify a pantograph image to be detected in the pantograph image; Step S2, using the improved edge detection algorithm to extract the pantograph edge image from the pantograph image to be detected; Step S3, locating the contour of the pantograph based on the pantograph edge image, and obtaining the pantograph contour similarity by using the invariant moment through the similarity calculation formula; Step S4: acquiring the degree of wear of the pantograph contact network for highway trucks based on the contour similarity, and determining the durability of the pantograph contact network based on the degree of wear.

2. The method for testing the durability of a pantograph contact grid for a highway truck according to claim 1, characterized in that: In the step S1, the process of using the Gabor filter and the support vector machine to identify the pantograph image to be detected in the pantograph image specifically includes: Using a multi-scale directional Gobar filter to filter the pantograph image to obtain shape information of the pantograph; Convolution is performed on the shape information of the pantograph to extract a pantograph feature map, the pantograph feature map is divided into a plurality of grids, and the mean and variance of each grid are calculated; The calculated mean and variance are connected in series to form a feature descriptor, and a support vector machine is used to judge the pantograph based on the feature descriptor.

3. The method for testing the durability of the pantograph contact network for highway trucks according to claim 2 is characterized in that: The calculation formula of the pantograph shape information obtained by filtering the pantograph image using a multi-scale Gobar filter is: g(x,y)=s(x,y)×w(x,y) Among them, s(x,y) is the sine function, w(x,y) is the Gaussian kernel function, and x and y are the horizontal and vertical pixel coordinates of the pantograph image.

4. The method for testing the durability of a pantograph contact grid for a highway truck according to claim 3, characterized in that: The Gaussian kernel function is:

5. The method for testing the durability of a pantograph contact grid for a highway truck according to claim 1, characterized in that: In step S2, the improved edge detection algorithm is specifically: Using a self-median filter and a guided filter to perform image denoising on the pantograph image to be detected, a denoised image is obtained; Calculating pixel partial derivatives using the Sobel operator, and convolving the obtained pixel partial derivatives with the denoised image to obtain image gradient information; The image gradient information is compared with two adjacent interpolations with the same gradient to obtain selected edge points, and the edge of the pantograph image to be detected is obtained based on the selected edge points.

6. The method for testing the durability of a pantograph contact grid for a highway truck according to claim 5, characterized in that: The calculation formula of the guided filter is: Among them, a k 、b k For the window w k When the center of is located at k, the constant coefficient of the linear function, S is the guide image value, and i and k are pixel indexes.

7. The method for testing the durability of a pantograph contact grid for a highway truck according to claim 1, characterized in that: In step S3, the process of locating the contour of the pantograph based on the pantograph edge image and obtaining the pantograph contour similarity by using the invariant moment through the similarity calculation formula specifically includes: Calculate the geometric moment expression of the pantograph edge image in a discrete state when the order is p+q; Calculate the center moment expression based on the geometric moment expression, and calculate the center distance normalization formula based on the center distance expression; Based on the normalized formula, the pantograph profile similarity is calculated using the similarity formula using the invariant moment.

8. The method for testing the durability of a pantograph contact grid for a highway truck according to claim 7, characterized in that: The geometric moment expression is: Wherein, M and N are the maximum values ​​of the horizontal and vertical coordinates of the pantograph image to be detected.

9. A pantograph contact grid durability test system for highway trucks, the system applying the method according to any one of claims 1 to 8, characterized in that: The system includes: An image acquisition module, used for acquiring a pantograph image using a high-definition area array camera, and identifying a pantograph image to be detected in the pantograph image using a Gabor filter and a support vector machine; An edge acquisition module, used for extracting a pantograph edge image from the pantograph image to be detected using an improved edge detection algorithm; A similarity calculation module, used for locating the contour of the pantograph based on the pantograph edge image, and obtaining the pantograph contour similarity by using an invariant moment through a similarity calculation formula; The durability determination module is used to obtain the degree of wear of the pantograph contact network for highway trucks based on the contour similarity, and determine the durability of the pantograph contact network based on the degree of wear.

10. The pantograph contact network durability testing system for highway trucks according to claim 9, characterized in that: In the image acquisition module, the process of using Gabor filter and support vector machine to identify the pantograph image to be detected in the pantograph image specifically includes: Using a multi-scale directional Gobar filter to filter the pantograph image to obtain shape information of the pantograph; Convolution is performed on the shape information of the pantograph to extract a pantograph feature map, the pantograph feature map is divided into a plurality of grids, and the mean and variance of each grid are calculated; The calculated mean and variance are connected in series to form a feature descriptor, and a support vector machine is used to judge the pantograph based on the feature descriptor.

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