CN109059775B - Steel rail abrasion detection method with image edge extraction step - Google Patents
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Abstract
The divisional application discloses a steel rail abrasion detection method with an image edge extraction step, belongs to the detection field, is used for optimizing the detection and calculation process of steel rail abrasion, and has the technical key points that: taking a median filtering brightness curve in which pixel points are distributed along the horizontal direction, respectively taking continuous points with the maximum brightness gradient change at two sides of the maximum peak value of the curve, and taking midpoints p and q of the two groups of continuous points, and taking the distance between p and q as the diameter of a detection template; the effect is to achieve image edge extraction.
Description
The application is a divisional application with the name of 'automatic detection method for rail abrasion' on application number 201610765942.8, application date 2016-08-30.
Technical Field
The invention belongs to the field of detection, relates to a steel rail abrasion automatic detection method, and particularly relates to a steel rail abrasion automatic detection method which is based on a word line laser image processing and a microprocessor and can effectively detect the surface abrasion depth and width of a steel rail head.
Background
The railway is a main artery for transportation, and compared with other transportation modes, the heavy haul railway transportation is rapidly developed all over the world due to the characteristics of large transportation volume and low cost. In rail equipment, rails are the most important components to directly bear the load of a train and guide the running of wheels. Whether the technical state of the steel rail is intact directly influences whether the train can run safely, stably and uninterruptedly at the specified speed. Railway locomotives transmit driving and braking forces through friction between wheel and rail, which can cause rail wear. Along with the high-speed, heavy-load and high-density operation of a locomotive, the abrasion of a steel rail is increased rapidly, and particularly the abrasion of the inner side surface of a small-radius curve outer rail is serious.
The detection technology of the rail abrasion goes through the processes of simple and visual detection of ruler tools, detection of a digitizer and the like. At present, in China, main methods such as contact clamp measurement, eddy current detection and optical triangulation are available in the aspect of rail abrasion detection, detection results often depend on attitudes of detection workers and experience of instrument use, and the methods have the problems of low detection efficiency, low detection precision and the like, and can not meet the development requirements of high speed at present. Although a detection device for detecting the abrasion of the surface of the rail by using laser is available at present, accurate step-by-step detection cannot be realized, and other detection methods only stay at the theoretical research level.
Disclosure of Invention
In order to optimize the detection and calculation process of the steel rail abrasion, the invention provides an automatic detection method of the steel rail abrasion, which has the technical key points that: the method comprises the steps of collecting a laser image of a steel rail, comparing the laser image with a complete steel rail laser light band image to judge whether the steel rail is abraded or not, judging whether the steel rail is abraded or not, selecting and extracting laser image characteristic quantities related to the abrasion quantity of the steel rail, and calculating to obtain the abrasion depth and/or width of the steel rail.
Has the advantages that: the laser image of the steel rail collected by the invention is compared with the complete image to judge whether the steel rail in the collected image is worn or not, when the steel rail is judged to be worn, the characteristic quantity is further selected to calculate to obtain the wear, the wear is judged in a fixed manner, then the thought of quantitatively calculating the depth and the width of the wear is carried out, and in the calculation process, the characteristic quantity is selected to optimize the calculation process of the depth and the width of the wear.
Drawings
Fig. 1 is a block diagram showing the structure of an automatic rail wear detection device according to embodiment 2;
FIG. 2 is a schematic view of a non-abrasive laser image;
FIG. 3 is a schematic view of a laser image with wear;
FIG. 4 is a graph showing the luminance curve and the diameter of the disk;
FIG. 5 is a labeled diagram of data points and feature quantities.
Detailed Description
Example 1: a rail abrasion automatic detection method comprises the steps of collecting a laser image of a rail, comparing the laser image with a complete rail laser light band image to judge whether the detected rail is abraded or not, judging whether the rail is abraded or not, carrying out laser image processing on the collected laser image, wherein the laser image processing comprises image preprocessing and image edge extraction, and after the laser image processing, selecting and extracting laser image characteristic quantities related to rail abrasion quantity to calculate rail abrasion depth and width. Wherein: the extracted laser image characteristic quantity related to the rail abrasion quantity is more than one of the following characteristic quantities:
1) length l of two linear portions of laser imageAAnd lB；
2) The width difference e of the two linear laser images;
3) longitudinal position difference z of the two linear laser images;
4) length l of transition section between two linear laser imagesC；
5) The inclination angle theta of a transition section between two sections of linear laser images;
the wear width and the wear depth are collectively called as the characteristic quantity of the rail wear, and one or more laser image characteristic quantities are selected and used for calculating the depth and the width of the rail wear;
when the depth and the width of the rail wear are calculated, not all the laser image characteristic quantities are selected for calculation, in order to optimize the calculation process, a combination of the laser image characteristic quantities is selected as basic calculation data for calculating the depth and the width of the rail wear, and the method for selecting the combination is as follows: firstly, laser image characteristic quantities related to the wear characteristic are determined, a preferred characteristic quantity is selected from the laser image characteristic quantities, correlation coefficients of the other laser image characteristic quantities and the preferred characteristic quantity are calculated, an average value of the correlation coefficients is obtained, the average value is a threshold beta selected by the characteristic quantity, if the absolute value | rTij | ≧ beta of the correlation coefficient between two laser image characteristics, the two laser image characteristics are related redundant characteristics, and only one of the two laser image characteristics is selected as the laser image characteristic quantity for rail wear judgment. When the combination of the laser image characteristic quantities used for judging is selected, the assumption is that M is a characteristic sample set collected at fixed points on a worn steel rail, the set comprises laser image characteristic quantities of reaction wear of N fixed points, a correlation coefficient is selected as a measurement parameter, the parameter reflects the similarity between the characteristics, two laser image characteristics are related redundant characteristics, only one of the two laser image characteristics is selected as the laser image characteristic quantity used for judging the wear width and depth of the steel rail, and the combination is used for searching the minimum laser image characteristic quantity combination capable of effectively judging the wear width and depth of the steel rail.
As an embodiment, the specific method for representing the correlation between the features by using the correlation coefficient as a measurement parameter is as follows: two different sets of laser image characteristics are used: t isi＝{tikK is 1,2, …, n and Tj＝{tjkAnd k is 1,2, …, n, where k denotes the kth test point and n test points are total, the correlation coefficient of the two groups of laser image features is defined as follows:
in the formula (I), the compound is shown in the specification,
and
are respectively two groups of characteristics TiAnd TjAverage value of (d):
and
correlation coefficient rTijReflects two groups of characteristics TiAnd TjDegree of correlation of (2), rTijWhen the value of (A) is negative, the negative correlation of the two characteristics is shown; rTijWhen the value of (a) is positive, the positive correlation of the two characteristics is shown; when rTijWhen 0, there is no correlation between the two wear characteristics, when rTijThe closer to 1 the absolute value of (A) is, the higher the correlation between the two laser image features and the greater the redundancy produced, and the greater the redundancy in the laser image feature set reflecting rail wearSetting a threshold value beta by utilizing a correlation coefficient between the characteristic quantities of the laser images, wherein if the absolute value | rT of the correlation coefficient between certain two laser image characteristicsijAnd | ≧ beta, the two laser image characteristics are related redundant characteristics, and only one of the two laser image characteristics is selected as the laser image characteristic quantity for judging the rail wear.
In another embodiment, the method for determining the threshold β is as follows: for a single laser image characteristic quantity, selecting the single laser image characteristic quantity as a preferred characteristic, and judging the possibility that the other laser image characteristic quantities are redundant characteristics, the method comprises the following steps: after the preferred characteristic is determined, a correlation coefficient between the preferred characteristic quantity and the rail abrasion width and depth related laser image characteristic set is obtained through calculation, the mean value of the group of correlation coefficient data is set as a threshold value beta, and the determination method of the threshold value beta is as follows:
wherein: where c is the number of feature quantities, l is the number of preferred feature quantities, and j is the number of candidate feature quantities.
Therefore, in the above embodiment, the correlation coefficient between the features is obtained to obtain the size of the possibility of redundancy between the features, the average value of the set of correlation coefficient data is set as the threshold β, and the threshold is used as the basis for judging whether the features are redundant, so that when two features are judged to be redundant, only one of the redundant features is selected as the laser image feature for calculating the depth and width of wear, so as to optimize the calculation process, thereby obtaining the minimum feature combination.
As an example, a method of calculating the wear depth and width is specifically disclosed: length l of two linear portions of laser imageAAs a preferred feature for abrasion width detection, the longitudinal position difference z of the two linear laser images is used as a preferred feature for abrasion depth detection; calculating the length l of two linear parts of the laser image according to the correlation coefficient of the two groups of laser image characteristicsAAnd lBThe difference z between the longitudinal positions of two linear laser images as the characteristic amount of wear width detectionAs a characteristic quantity of the wear depth detection, a wear width calculation formula is as follows:
wherein l is the width of the unworn steel rail;
the abrasion depth calculation formula is as follows:
V＝z·tan 60°
as an embodiment, the image preprocessing comprises the steps of:
firstly, graying an image, drawing a histogram of a grayscale image, and finding out a grayscale concentration range;
then, the gray level of the gray level image is enhanced by using the following formula, so that the image is clearer;
wherein: a. b are respectively the left and right boundary points of the gray value centralized distribution in the gray image histogram, and x and y respectively represent the gray values before and after the gray enhancement.
As an embodiment, the method for image edge extraction includes the following steps:
taking a median filtering brightness curve in which pixel points are distributed along the horizontal direction, respectively taking continuous points with the maximum brightness gradient change at two sides of the maximum peak value of the curve, and taking midpoints p and q of the two groups of continuous points, and taking the distance between p and q as the diameter of a detection template;
setting the brightness of the image as f (i, j), and taking a circle s (c, r) in the image field as a detection template, wherein c is the center of the circle and the coordinate is (i, j)c,jc) R is a radius;
defining a set of pixel points in s (c, r), and recording the brightness sum of the pixel points in the circle s as:
moving the circle center of the detection template in a small range in the horizontal direction, calculating the brightness sum of each pixel in each position detection template, wherein the brightness sum in the range is the maximum template circle center position, namely a pixel-level ridge edge point of the bright strip, fitting a straight line by using a least square method, wherein the straight line is a central line of a laser image of a word line, and the small range is an image interval with the circle center as the central point and 2 times of radius respectively, so as to obtain the length l of two straight line parts of the laser imageAAnd lBLength l of transition section between two linear laser imagesC。
Example 2: as a supplement to the solution of embodiment 1, or as a separate embodiment: the abrasion mainly occurs on the head of the steel rail, the abrasion comprises top surface abrasion and side surface abrasion, and the two numerical values must be detected simultaneously during detection to comprehensively judge the abrasion degree of the steel rail. In the embodiment, a high-intensity narrow-beam linear laser beam is utilized, an angle of 60 degrees is formed between the plane where the laser and the linear laser beam are located and the surface of the measured steel rail, and the high-resolution area array CCD image sensor is located right above the laser image to shoot the laser image. The light beam image shows bending on the surface of the worn steel rail, and the width and the depth of the rail wear are determined by the position and the bending degree of the bending point.
The rail wearing and tearing automatic checkout device includes: the device comprises a linear laser, a CCD image sensor, a microprocessor, an execution unit, a display and acousto-optic alarm unit and an interface unit. The CCD image sensor collects laser images, the obtained image information is transmitted to the microprocessor for analysis and processing, the edge and the center of the image are extracted, a straight line is fitted, a complete steel rail laser light band image outline is formed, the image information is converted into steel rail outline parameters, the characteristic quantity of the steel rail outline is stored and compared with the complete steel rail parameters, and whether the steel rail has abrasion or not is judged. Continuing to detect the next point when no abrasion is generated; and (4) determining abrasion loss, including the depth and width of abrasion loss. The execution unit receives a control signal of the microprocessor, controls the advancing direction and the speed of the detection device, and adjusts the orientation of the CCD image sensor, the output end of the microprocessor is respectively connected with the LCD display and the acousto-optic alarm system, the LCD display is used for displaying the current position and the abrasion degree of the steel rail, and the acousto-optic alarm system is used for prompting that the current position of the steel rail is abraded and needs to be repaired. The interface unit is used for exchanging information with an upper computer, and the upper computer can further finely process the image of the abrasion position and determine the accurate abrasion amount.
The image preprocessing is a preprocessing stage of laser image edge extraction, firstly graying an image, drawing a histogram of the grayscale image, finding out a grayscale concentration range, and performing grayscale enhancement on the grayscale image by using a formula (1) (wherein a and b are respectively left and right boundary points of the grayscale value concentration distribution in the histogram of the grayscale image, and x and y respectively represent grayscale values before and after grayscale enhancement) so as to make the image clearer.
The edge detection of the laser image of the first line adopts a roof ridge edge detection method. The edge detection method based on the brightness of a single pixel point has poor noise resistance, and in order to reduce the interference of image noise, the brightness sum of each pixel point in a certain area is used as a ridge-shaped edge judgment basis. Because the circle has isotropy and is not influenced by the ridge edge direction, the invention adopts a ridge edge detection method by a disc method. And moving a disc detection template with a proper size in a certain range at two sides of the laser image of the same line, wherein when the brightness and gradient change of each pixel point in the template meet certain requirements, the central point of the template is a ridge-shaped edge point.
Any one median filtering brightness curve with pixel points distributed along the horizontal direction is taken, continuous points with the maximum brightness gradient change are taken out from two sides of the maximum peak value of the curve respectively, the middle points p and q of the two groups of continuous points are taken, and the distance between the points p and q is taken as the diameter of the detection template, as shown in fig. 3.
Setting the brightness of the image as f (i, j), and taking a circle s (c, r) in the image field as a detection template, wherein c is the center of the circle and the coordinate is (i, j)c,jc) And r is a radius. The definition of s (c is,r) collection of inner pixel points:
and recording the brightness sum of the pixel points in the circle s as:
and moving the circle center of the detection template in a small range in the horizontal direction, and calculating the brightness sum of each pixel in each position detection template, wherein the position of the circle center of the template with the maximum brightness sum in the range is a pixel-level ridge edge point of the bright strip. And fitting a straight line by using a least square method, wherein the straight line is the central line of the laser image of the word line. The detected edge points and the fitted straight line are shown in fig. 4.
And further extracting characteristic quantities of the laser image related to the rail abrasion loss, including a selection method of the characteristic quantities and determination of a threshold value.
The width and the depth of the rail abrasion, which are mainly related by the invention, can be determined through the bending degree of the laser image, and the following characteristic quantities can be used for selection:
1) length l of two linear portions of laser imageAAnd lB；
2) The width difference e of the two linear laser images;
3) longitudinal position difference z of the two linear laser images;
4) length l of transition section between two linear laser imagesC；
5) And the inclination angle theta of the transition section between the two linear laser images.
One or more characteristic quantities can be selected for judging the depth and the width of the wear of the steel rail, and when the combination of the characteristic quantities for judging is selected, different types of characteristics are required to have obvious difference, so that redundant characteristic interference judgment is avoided. Assume M is a set of feature samples collected at fixed points on a rail with wear, the set containing wear features for n fixed points. And selecting the correlation coefficient as a measurement parameter, wherein the parameter can reflect the similarity between the characteristics and is used for searching the combination of the minimum characteristic quantity which can effectively judge the wear width and the depth of the steel rail. Is provided with twoThe wear characteristics of the different groups were: t isi＝{tikK is 1,2, …, n and Tj＝{tjkAnd k is 1,2, …, n, where k denotes the kth test point and n test points are total, the correlation coefficients of the two sets of features are defined as follows:
in the formula (I), the compound is shown in the specification,
and
are respectively two groups of characteristics TiAnd TjAverage value of (d):
and
correlation coefficient rTijReflects two groups of characteristics TiAnd TjDegree of correlation of (2), rTijWhen the value of (A) is negative, the negative correlation of the two characteristics is shown; and if the value is positive, the positive correlation of the two characteristics is shown. When rTijWhen 0, there is no correlation between the two features. When rT is thenijThe closer the absolute value of (a) is to 1, the higher the degree of correlation between the two features is, and the greater the redundancy that can be generated in this case.
In the feature set of rail wear, a threshold value beta is set by using a correlation coefficient between feature quantities, and if an absolute value | rT of the correlation coefficient between two of the feature quantities is equal toijAnd if the two characteristics are related redundant characteristics, only one of the characteristics can be selected as the characteristic quantity for judging the rail wear.
For a single characteristic, the greater the direct relation between the single characteristic and the wear depth and width of the steel rail, the simpler the judgment method, the higher the feasibility for judging the wear amount, the greater the possibility of selection, and the selected characteristic is taken as the preferred characteristic. And judging the possibility of a certain feature as a redundant feature, wherein the higher the correlation is, the higher the possibility of becoming a relevant redundant feature is according to the correlation of the certain feature and the preferred feature. After the preferred characteristic is determined, calculating to obtain a correlation coefficient between the preferred characteristic quantity and the rail wear width and depth related characteristic set, and setting the average value of the group of data as a threshold value beta, as shown in formula (4):
and after the characteristic quantity is determined, calculating the steel rail abrasion quantity of the detection position, storing and displaying, and starting an audible and visual alarm device when the abrasion exceeds the limit.
Under the off-line condition, the interface unit is used for exchanging information with the upper computer, and the upper computer can further finely process the image of the abrasion position to determine the accurate abrasion amount. By adopting the technical scheme, the automatic steel rail wear detection device provided by the embodiment has the beneficial effects that the image processing method is adopted, the control of the microprocessor is separated from the control of a PC, and the device can automatically run under the setting of an operator. The device has certain perfection and effectiveness, is convenient for detection personnel to use, and has the advantages of simple operation, accurate detection result and low production and manufacturing cost.
The above description is only for the purpose of creating a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can substitute or change the technical solution and the inventive concept of the present invention within the technical scope of the present invention.
Claims (1)
1. A rail abrasion detection method with an image edge extraction step is characterized in that: implemented by rail wearing and tearing automatic checkout device, detection device includes: the device comprises a linear laser, a CCD image sensor, a microprocessor, an execution unit, a display and acousto-optic alarm unit and an interface unit, wherein the CCD image sensor acquires a laser image, the obtained image information is transmitted to the microprocessor for analysis and processing, the edge and the center position of the image are extracted and a straight line is fitted to form a complete steel rail laser light band image outline, the image information is converted into a steel rail outline parameter, the characteristic quantity of the steel rail outline is stored and compared with the complete steel rail parameter to judge whether the steel rail is worn or not;
the image edge extraction method comprises the following steps:
taking a median filtering brightness curve in which pixel points are distributed along the horizontal direction, respectively taking continuous points with the maximum brightness gradient change at two sides of the maximum peak value of the curve, and taking midpoints p and q of two groups of continuous points, and taking the distance between p and q as the diameter of a detection template;
setting the brightness of the image as f (i, j), and taking a circle s (c, r) in the image field as a detection template, wherein c is the center of the circle and the coordinate is (i, j)c,jc) R is a radius;
defining a set of pixel points in s (c, r), and recording the brightness sum of the pixel points in the circle s as:
moving the circle center of the detection template in a small range in the horizontal direction, calculating the brightness sum of each pixel in each position detection template, wherein the brightness sum in the range is the maximum template circle center position, namely a pixel-level ridge edge point of a bright strip, fitting a straight line by using a least square method, wherein the straight line is a central line of a laser image of a word line, and the small range is an image interval with the circle center as the central point and 2 times of radius respectively, so as to obtain the length l of two straight line parts of the laser imageAAnd lBLength l of transition section between two linear laser imagesC；
The rail abrasion detection comprises the steps of selecting and extracting laser image characteristic quantity related to rail abrasion quantity after laser image processing so as to calculate and obtain rail abrasion depth and width; after the laser image processing, selecting and extracting laser image characteristic quantity related to the rail abrasion quantity to calculate and obtain the rail abrasion depth and width, wherein: the extracted laser image characteristic quantity related to the rail abrasion quantity is more than one of the following characteristic quantities:
1) length l of two linear portions of laser imageAAnd lB；
2) The width difference e of the two linear laser images;
3) longitudinal position difference z of the two linear laser images;
4) length l of transition section between two linear laser imagesC；
5) The inclination angle theta of a transition section between two sections of linear laser images;
the wear width and the wear depth are collectively called as the characteristic quantity of the rail wear, and the combination of one or more laser image characteristic quantities is selected and used for calculating the depth and the width of the rail wear;
the method for selecting the combination is as follows: firstly, determining laser image characteristic quantities related to the characteristic quantities of the rail abrasion, selecting a preferred characteristic quantity from the laser image characteristic quantities, calculating correlation coefficients of the other laser image characteristic quantities and the preferred characteristic quantity, and solving an average value of the correlation coefficients, wherein the average value is a threshold beta selected by the laser image characteristic quantities, if the absolute value | rTij | ≧ beta of the correlation coefficient between two laser image characteristic quantities, the two laser image characteristic quantities are related redundant characteristics, only one laser image characteristic quantity is selected as the laser image characteristic quantity for rail abrasion judgment, when selecting the combination of the laser image characteristic quantities for judgment, assuming that M is a characteristic sample set collected at fixed points on a rail with abrasion, the set comprises the laser image characteristic quantities of the reaction abrasion of N fixed points, the correlation coefficient is selected as a measurement parameter, and the parameter embodies the similarity between the laser image characteristic quantities, and the two laser image characteristic quantities are related redundant characteristics, and only one of the two laser image characteristic quantities is selected as the laser image characteristic quantity for judging the rail abrasion, so as to find the combination of the minimum laser image characteristic quantity which can effectively judge the rail abrasion width and depth.
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| CN108662983B (en) * | 2016-08-30 | 2020-05-01 | 大连民族大学 | Method for detecting and calculating correlation coefficient of steel rail abrasion |
| CN107194921B (en) * | 2017-05-19 | 2020-04-07 | 华夏高铁技术有限公司 | Automatic identification method and device for rail surface light band of steel rail |
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| Publication number | Publication date |
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| CN108662983A (en) | 2018-10-16 |
| CN108662983B (en) | 2020-05-01 |
| CN108830841B (en) | 2021-11-26 |
| CN108731599B (en) | 2021-02-05 |
| CN108830841A (en) | 2018-11-16 |
| CN109059775A (en) | 2018-12-21 |
| CN106441107B (en) | 2019-02-22 |
| CN106441107A (en) | 2017-02-22 |
| CN108731599A (en) | 2018-11-02 |
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