CN106441107A - Method for automatic detection of steel rail abrasion - Google Patents

Method for automatic detection of steel rail abrasion Download PDF

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Publication number
CN106441107A
CN106441107A CN201610765942.8A CN201610765942A CN106441107A CN 106441107 A CN106441107 A CN 106441107A CN 201610765942 A CN201610765942 A CN 201610765942A CN 106441107 A CN106441107 A CN 106441107A
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image
rail
laser
abrasion
laser image
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CN106441107B (en
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张秀峰
谢春利
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Dalian Minzu University
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Dalian Nationalities University
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Priority to CN201610765942.8A priority Critical patent/CN106441107B/en
Priority to CN201810556247.XA priority patent/CN109059775B/en
Priority to CN201810556266.2A priority patent/CN108830841B/en
Priority to CN201810555967.4A priority patent/CN108662983B/en
Priority to CN201810556239.5A priority patent/CN108731599B/en
Publication of CN106441107A publication Critical patent/CN106441107A/en
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B11/00—Measuring arrangements characterised by the use of optical techniques
    • G01B11/02—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness
    • G01B11/028—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness by measuring lateral position of a boundary of the object
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61K—AUXILIARY EQUIPMENT SPECIALLY ADAPTED FOR RAILWAYS, NOT OTHERWISE PROVIDED FOR
    • B61K9/00—Railway vehicle profile gauges; Detecting or indicating overheating of components; Apparatus on locomotives or cars to indicate bad track sections; General design of track recording vehicles
    • B61K9/08—Measuring installations for surveying permanent way
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B11/00—Measuring arrangements characterised by the use of optical techniques
    • G01B11/22—Measuring arrangements characterised by the use of optical techniques for measuring depth
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection
    • G06T7/001—Industrial image inspection using an image reference approach
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/13—Edge detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10004—Still image; Photographic image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30108—Industrial image inspection
    • G06T2207/30164—Workpiece; Machine component

Abstract

The invention discloses a method for automatic detection of steel rail abrasion, and belongs to the field of detection. The method is used for optimizing the detection and calculation process of the steel rail abrasion. The technical main points of the method comprises the steps: collecting a laser image of a steel rail, and comparing the laser image with a complete steel rail laser light band image, so as to judge whether the steel rail is abraded or not; selecting and extracting a laser image characteristic quantity related with the abrasion of the steel rail if the steel rail is abraded, so as to calculate the abrasion depth and/or width of the steel rail. The beneficial effects of the invention are that the method employs an idea of firstly judging the abrasion and secondly carrying out the quantitative calculation, and selects the characteristic quantity in the calculation process, so as to optimize the calculation process of the abrasion depth and width.

Description

Rail wear automatic testing method
Technical field
The invention belongs to detection field, relate to a kind of rail wear automatic testing method, swash particularly to based on a wordline Light image process and microprocessor can effectively to detect a kind of rail wear of rail head of rail surface abrasion depth and width automatic Detection method.
Background technology
Railway is the main artery of communications and transportation, compares other means of transportation, and heavy haul railway transport is big, low cost with freight volume Feature develops rapidly all over the world.In track equipment, rail is most important building block, directly bears train and carries Lotus simultaneously guides wheel to run.Whether the state of the art of rail is intact, and can directly affect train safe by the speed of regulation, flat Steady and continual operation.Railway locomotive be by wheel track between frictional force transmit driving force and brake force, and between wheel track Friction then can cause the generation of rail wear.As the high speed of locomotive, heavy duty, high density are run, the abrasion of rail will be quick The increase of degree, particularly sharp radius curve outer rail medial surface abrasion are particularly acute.
The detection technique of rail wear have passed through that monocular of conforming to the principle of simplicity measures ruler class tool detection, digitized instrument detection waited Journey.At present, China has the main method such as contact fixture measurement, EDDY CURRENT, optical triangulation in terms of Rail Abrasion Detection System, The experience that testing result is often depending on detecting the attitude of workman and instrument uses, these methods also exist that detection efficiency is low, inspection Survey the not high problems of precision, can not meet the development need of current high speed.Although occurring in that now that a kind of utilization is swashed The detection equipment of light detection rail surface abrasion, but step-by-step movement detection accurately can not be realized, other detection methods are also simply stopped Stay theoretical research aspect.
Content of the invention
In order to optimize detection and the calculating process of rail wear, the present invention proposes a kind of rail wear side of detection automatically Method, is characterized in that：Gather the laser image of rail, and carry out image comparison with complete rail laser light belt image, to sentence Whether disconnected detection rail has abrasion, it is judged that rail has abrasion, selects and extracts the laser image spy related to rail wear amount The amount of levying, to be calculated the rail wear degree of depth and/or width.
Beneficial effect：Rail laser image and complete image that the present invention just gathers are compared, to judge whether to gather There is abrasion in rail in image, and when being judged as abrasion, selects characteristic quantity to be calculated abrasion further, first qualitative sentences Disconnected abrasion, more quantitatively calculate the thinking of abrasion depth and width, and during calculating, selecting characteristic quantity, with excellent Change the calculating process of abrasion depth and width.
Brief description
Fig. 1 is the structured flowchart of rail wear automatic detection device described in embodiment 2；
Fig. 2 is for without abrasion laser image schematic diagram；
Fig. 3 is for there being abrasion laser image schematic diagram；
Fig. 4 is brightness curve and disk diameter schematic diagram；
Fig. 5 is the mark schematic diagram of data point and characteristic quantity.
Detailed description of the invention
Embodiment 1：A kind of rail wear automatic testing method, gathers the laser image of rail, and with complete rail laser Light belt image carries out image comparison, to judge whether detection rail has abrasion, it is judged that rail has abrasion, to the laser figure gathering As carrying out laser image process, described laser image processes and includes Image semantic classification and Edge extraction, laser image process After, select and extract the laser image characteristic quantity related to rail wear amount, to be calculated rail wear depth and width.Its In：The described extraction laser image characteristic quantity related to rail wear amount is more than one in following characteristics amount：
1) length l of two sections of straight line portioies of laser imageAAnd lB；
2) the stand out e of two sections of linear laser images；
3) lengthwise position difference z of two sections of linear laser images；
4) length l of changeover portion between two sections of linear laser imagesC；
5) inclination angle theta of changeover portion between two sections of linear laser images；
Abrasion width and the abrasion degree of depth are referred to as the characteristic quantity of rail wear, select above-mentioned in one or more laser images Characteristic quantity, for calculating the depth and width of rail wear；
And when calculating the depth and width of rail wear, and non-selection whole above-mentioned laser image characteristic quantity is counted Calculate, in order to optimize calculating process, select the base as the depth and width calculating rail wear for the combination of laser image characteristic quantity Plinth calculates data, selects method during this combination to be：First the laser image characteristic quantity related to this wear characteristics is determined, and from Laser image characteristic quantity selects preferred features amount, calculates the degree of correlation system of remaining each laser image characteristic quantity and preferred features amount Number, and seeks its mean value, and this mean value is the threshold value beta that characteristic quantity selects, if wherein between certain two laser image feature Absolute value | rTij | >=β of coefficient correlation, this two laser image features are relevant redundancy features, only select one of them conduct The laser image characteristic quantity that rail wear judges.I.e. when selecting the combination for the laser image characteristic quantity judging, it is assumed that M is The rail have abrasion pinpoints the feature samples set collecting, swashing of the reaction abrasion that this set comprises N number of fixing point Light image characteristic quantity, select correlation coefficient as metric parameter, this parameter be characterized by between similitude, two laser figures As feature is relevant redundancy feature, only select one of them laser image characteristic quantity judging as rail wear, be used for finding Can effectively judge the combination of the minimum laser image characteristic quantity of rail wear width and the degree of depth.
As a kind of embodiment, above-mentioned middle correlation coefficient is as metric parameter, to be characterized by the concrete of a correlation Method is：Use sets two groups of different laser image features and is respectively：Ti={ tik, k=1,2 ..., n} and Tj={ tjk, k=1, 2 ..., n}, wherein k represents k-th test point, and total n test point, then the coefficient correlation of two groups of laser image features defines such as Under：
In formula,WithIt is respectively two stack features TiAnd TjMean value：With
Correlation coefficient r TijReflect two stack features TiAnd TjDegree of correlation, rTijValue for, when negative, representing two features Negative correlation；rTijValue be timing, represent two feature positive correlations；Work as rTijIt when=0, is incoherent between two wear characteristics, Work as rTijAbsolute value when being closer to 1, the degree of correlation of two laser image features is higher, and the redundancy of generation is bigger, in reaction In the laser image characteristic set of rail wear, utilize the coefficient correlation between each laser image characteristic quantity, threshold value beta is set, if The wherein absolute value of the coefficient correlation between certain two laser image feature | rTij| >=β, this two laser image features are related Redundancy feature, only selects one of them laser image characteristic quantity judging as rail wear.
In another embodiment, the determination method for threshold value beta described above is：Special for certain single laser image The amount of levying, chooses it as preferred features, it is judged that remaining laser image characteristic quantity is that the method for the possibility of redundancy feature is：Determine After preferred features, by calculate obtain in rail wear width and degree of depth associated laser characteristics of image set with preferred features amount it Between correlation coefficient, the average of this group correlation coefficient data is set to threshold value beta, the determination method of threshold value beta is：
Wherein：The quantity of the c amount of being characterized in formula, l is the sequence number of first-selected characteristic quantity, and j is the sequence number of alternative features amount.
Thus, above-described embodiment, tries to achieve the coefficient correlation between feature to obtain the possibility size of redundancy between feature, should The average of group correlation coefficient data is set to threshold value beta, using this threshold value as the foundation of judging characteristic whether redundancy, thus is sentencing During disconnected two feature redundancy, only select a laser image feature as the depth and width calculating abrasion between redundancy feature, To optimize calculating process, obtain minimal features combination with this.
As a kind of embodiment, the concrete open computational methods wearing away depth and width：Two sections of line parts of laser image Length l dividedAAs the preferred features of abrasion width detection, lengthwise position difference z of two sections of linear laser images is deep as abrasion The preferred features of degree detection；Obtain two straight line portioies of laser image by the Calculation of correlation factor of two groups of laser image features Length lAAnd lBAs the characteristic quantity of abrasion width detection, lengthwise position difference z of two sections of linear laser images is as abrasion degree of depth inspection The characteristic quantity surveyed, abrasion width calculation formula is：
Wherein, l is the width not wearing away rail；
Abrasion depth calculation formula is：
V=z tan60 °
As a kind of embodiment, described Image semantic classification comprises the steps：
First by image gray processing, draw the histogram of gray level image, find out gray scale and concentrate scope；
Then use following formula, grey level enhancement is carried out to gray level image, make image become apparent from；
Wherein：A, b are respectively the left and right boundary point of gray value integrated distribution in gray level image histogram, and x, y represent respectively Gray value before and after grey level enhancement.
As a kind of embodiment, the method for described Edge extraction, comprise the steps：
Appoint the medium filtering brightness curve taking a distribution image vegetarian refreshments in the horizontal direction, divide in this curve peak-peak both sides Qu Chu not change maximum continuity point by brightness step, take the spacing of midpoint p and q, p and q of this two groups of continuity points as detection Template diameter；
If the brightness of image is that (i, j), (c, r) as detection template, wherein c is circle to f to take a round s in picture field The heart, its coordinate is (ic,jc), r is radius；
Definition s (c, r) in the set of pixel, and remember the brightness of pixel in round s and be：
Moving in making the detection template center of circle little scope in the horizontal direction, in calculating each position detection template, each pixel is bright Degree is with in the range of being somebody's turn to do, brightness and maximum template home position, be a Pixel-level roof edge point of this bright wisp, utilizes Little square law fitting a straight line, this straight line is a wordline laser image center line, and described little scope is a some left side centered on the center of circle The image of right each 2 times of radiuses is interval, to obtain length l of two sections of straight line portioies of laser imageAAnd lB, two sections of linear laser figures Length l of changeover portion between XiangC.
Embodiment 2：Supplementing as embodiment 1 technical scheme, or as a kind of single embodiment：Abrasion mainly go out The head of present rail, abrasion include that end face wears away and side wear, the two numerical value must be detected during detection simultaneously, combine Close the wear intensity judging rail.The present embodiment utilizes high intensity narrow beam one word laser beam, laser instrument and a wordline light beam institute Being 60 ° of angles in plane and tested Rail Surface, high-resolution Array CCD sensor is positioned at the surface shooting of laser image Laser image.Occur in that bending at the Rail Surface light beam image having abrasion, the position being occurred by bending point and degree of crook Determine width and the degree of depth of rail wear.
Rail wear automatic detection device includes：One word line laser device, ccd image sensor, microprocessor, execution list Unit, display and acousto-optic warning unit and interface unit.Ccd image sensor gathers laser image, the image information being obtained It is transferred to microprocessor to be analyzed processing, extract image border and center fitting a straight line, form complete rail laser Light belt image outline, converts image information into rail profile parameter, the characteristic quantity of storage rail profile, and joins with complete rail Number is compared, it is judged that whether rail exists abrasion.Abrasion are not had to proceed subsequent point detection；Have abrasion, further determine that Abrasion loss, including the depth and width of abrasion.Performance element accepts the control signal of microprocessor, the traveling of control detection device Direction and speed, regulation ccd image sensor orientation, the output of microprocessor respectively with LCD display and sound and light alarm System connects, and LCD display is for showing current location and the wear intensity of rail, and acoustooptic alarm system is used for pointing out rail to work as There are abrasion in front position, needs to repair.Interface unit is for exchanging information with host computer, and host computer can be further to abrasion position The further fine processing of image put, determines accurately abrasion loss.
Processing stage Image semantic classification is the early stage of laser image edge extracting, first by image gray processing, draw gray scale The histogram of image, finds out gray scale and concentrates scope, and (wherein a, b are respectively gray value in gray level image histogram to utilize formula (1) The left and right boundary point of integrated distribution, x, y represent grey level enhancement respectively before and after gray value) grey level enhancement is carried out to gray level image, Image is made to become apparent from.
The rim detection of one wordline laser image uses " ridge-shaped " edge detection method.Based on single pixel brightness Edge detection method noise resisting ability is poor, in order to reduce the interference of picture noise, pixel brightness each in a certain region and As " ridge-shaped " edge distinguishing rule.Owing to circle has each to same tropism, do not affected by ridge-shaped edge direction, therefore, The present invention uses plate way " ridge-shaped " edge detection method.By disk detection template suitable for size at a wordline laser image Move in the certain limit of both sides, when the graded of the brightness sum of pixel each in template meets certain requirements, template Central point is ridge-shaped marginal point.
Appoint the medium filtering brightness curve taking a distribution image vegetarian refreshments in the horizontal direction, divide in this curve peak-peak both sides Qu Chu not change maximum continuity point by brightness step, take the spacing of midpoint p and q, p and q of this two groups of continuity points as detection Template diameter, as shown in Figure 3.
If the brightness of image is that (i, j), (c, r) as detection template, wherein c is circle to f to take a round s in picture field The heart, its coordinate is (ic,jc), r is radius.Definition s (c, r) in the set of pixel：
And remember the brightness of pixel in round s and be：
Moving in making the detection template center of circle little scope in the horizontal direction, in calculating each position detection template, each pixel is bright Degree is with in the range of being somebody's turn to do, brightness and maximum template home position, be a Pixel-level roof edge point of this bright wisp.Utilize Little square law fitting a straight line, this straight line is a wordline laser image center line.The marginal point detecting and the straight line of matching are such as Shown in Fig. 4.
Extract further and rail wear amount associated laser image feature amount, including the system of selection of characteristic quantity and threshold value Determine.
The width of the rail wear that the invention mainly relates to and the degree of depth, may determine that steel by the degree of crook of laser image The width of rail abrasion and the degree of depth, have following characteristic quantity to can be used for selecting：
1) length l of two sections of straight line portioies of laser imageAAnd lB；
2) the stand out e of two sections of linear laser images；
3) lengthwise position difference z of two sections of linear laser images；
4) length l of changeover portion between two sections of linear laser imagesC；
5) inclination angle theta of changeover portion between two sections of linear laser images.
One or more characteristic quantity can be selected for judging the depth and width of rail wear, selecting for judgement During the combination of characteristic quantity, it is desirable to inhomogeneity feature has marked difference, it is to avoid redundancy feature interference judges.Assume that M is to have Pinpointing, on the rail of abrasion, the feature samples set collecting, this set comprises the wear characteristics of n fixing point.Select the degree of correlation Coefficient as metric parameter, this parameter can be characterized by between similitude, can effectively judge rail wear width for searching The combination of the minimal features amount of degree and the degree of depth.If two groups of different wear characteristics are respectively：Ti={ tik, k=1,2 ..., n} and Tj={ tjk, k=1,2 ..., n}, wherein k represents k-th test point, total n test point, then the coefficient correlation of two stack features It is defined as follows：
In formula,WithIt is respectively two stack features TiAnd TjMean value：With
Correlation coefficient r TijReflect two stack features TiAnd TjDegree of correlation, rTijValue for, when negative, representing two features Negative correlation；Value is timing, represents two feature positive correlations.Work as rTijIt when=0, is incoherent between two features.Then rT is worked asij Absolute value when being closer to 1, represent that the degree of correlation of two features is higher, now issuable redundancy is bigger.
In the characteristic set of rail wear, utilize the coefficient correlation between each characteristic quantity, threshold value beta be set, if wherein certain The absolute value of the coefficient correlation between two features | rTij| >=β, illustrate that the two feature is relevant redundancy feature, can only select One of them characteristic quantity judging as rail wear.
For certain single features, it is bigger with the direct relation of rail wear depth and width, determination methods is simpler, uses Higher in the feasibility judging abrasion loss, selected possibility is bigger, and selected feature is as preferred features.Judge a certain Being characterized as the possibility of redundancy feature, according to its correlation with preferred features, correlation is higher, then become relevant redundancy feature Possibility bigger.Determining after preferred features, by calculate obtain in rail wear width and degree of depth correlated characteristic set with The average of this group data is set to threshold value beta by the correlation coefficient between preferred features amount, as shown in formula (4):
After determining characteristic quantity, calculate the rail wear amount of detection position, store and show, have and start when transfiniting abrasion Acoustic-optic alarm.
Interface unit is for exchanging information with host computer in off-line case, and host computer can be further to abrasion position The further fine processing of image, determines accurately abrasion loss.Owing to using technique scheme, a kind of steel of the present embodiment offer Rail abrasion automatic detection device has such beneficial effect, owing to using the method for image procossing, in the control of microprocessor Under, the control of PC, device can run under the setting of operating personnel automatically.Equipment has certain integrity and actual effect Property, it is simple to the use of testing staff, not only simple to operate, testing result is accurate, and manufacturing cost is low.
The above, only the invention preferably detailed description of the invention, but the protection domain of the invention is not Being confined to this, any those familiar with the art is in the technical scope that the invention discloses, according to the present invention The technical scheme created and inventive concept thereof in addition equivalent or change, all should cover the invention protection domain it In.

Claims (10)

1. a rail wear automatic testing method, it is characterised in that gather rail laser image, and with complete rail laser Light belt image carries out image comparison, to judge whether detection rail has abrasion, it is judged that rail has abrasion, selects and extracts and steel The related laser image characteristic quantity of rail abrasion loss, to be calculated the rail wear degree of depth and/or width.
2. rail wear automatic testing method as claimed in claim 1, it is characterised in that described related to rail wear amount Laser image characteristic quantity is more than one in following characteristics amount,
1) length l of two sections of straight line portioies of laser imageAAnd lB；
2) the stand out e of two sections of linear laser images；
3) lengthwise position difference z of two sections of linear laser images；
4) length l of changeover portion between two sections of linear laser imagesC；
5) inclination angle theta of changeover portion between two sections of linear laser images.
3. rail wear automatic testing method as claimed in claim 2, it is characterised in that the characteristic quantity of rail wear includes mill Consumption width and the abrasion degree of depth, select one or more laser image characteristic quantity, for calculating the degree of depth and/or the width of rail wear Degree, when selecting the combination for the laser image characteristic quantity judging, it is assumed that M is to pinpoint to collect on the rail have abrasion Feature samples set, this set comprise N number of fixing point reaction abrasion laser image characteristic quantity, select correlation coefficient make For metric parameter, two laser image features are relevant redundancy features, only select one of them to judge as rail wear Laser image characteristic quantity, effectively being judged the combination of the minimum laser image characteristic quantity of rail wear width and the degree of depth, and It is calculated a certain characteristic quantity of rail wear with the combination of this feature amount.
4. rail wear automatic testing method as claimed in claim 3, it is characterised in that two laser image spies of described judgement Levy is that the method for relevant redundancy feature is：Select preferred features amount from laser image characteristic quantity, calculate remaining each laser image Characteristic quantity and the correlation coefficient of preferred features amount, and seek its mean value, this mean value is the threshold that laser image characteristic quantity selects Value β, if absolute value | rTij | >=β of the coefficient correlation wherein between certain two laser image feature, then this two laser images Feature is relevant redundancy feature, only selects one of them laser image characteristic quantity judging as rail wear.
5. the rail wear automatic testing method as described in claim 3 or 4, it is characterised in that calculate described correlation coefficient Method be：
If two groups of different laser image features are respectively：Ti={ tik, k=1,2 ..., n} and Tj={ tjk, k=1,2 ..., N}, wherein k represents k-th test point, and total n test point, then the coefficient correlation of two groups of laser image features is defined as follows：
rT i j = Σ k = 1 n ( t i k - t i ‾ ) ( t j k - t j ‾ ) Σ k = 1 n ( t i k - t i ‾ ) 2 Σ k = 1 n ( t j k - t j ‾ ) 2
In formula,WithIt is respectively two stack features TiAnd TjMean value：With
Correlation coefficient r TijReflect two stack features TiAnd TjDegree of correlation, rTijValue for, when negative, representing two feature negatives Close；rTijValue be timing, represent two feature positive correlations；Work as rTijIt when=0, is incoherent between two wear characteristics, when rTijAbsolute value when being closer to 1, the degree of correlation of two laser image features is higher, and the redundancy of generation is bigger.
6. rail wear automatic testing method as claimed in claim 4, it is characterised in that the method calculating threshold value beta is：
β = 1 c - 1 Σ j = 2 c - 1 | rT 1 j |
Wherein：The quantity of the c amount of being characterized in formula, l is the sequence number of first-selected characteristic quantity, and j is the sequence number of alternative features amount.
7. rail wear automatic testing method as claimed in claim 4, it is characterised in that two sections of straight line portioies of laser image Length lAAs the preferred features of abrasion width detection, lengthwise position difference z of two sections of linear laser images is as the abrasion degree of depth The preferred features of detection；Obtain the length of two straight line portioies of laser image by the Calculation of correlation factor of two groups of laser image features Degree lAAnd lBAs the characteristic quantity of abrasion width detection, lengthwise position difference z of two sections of linear laser images is as abrasion depth detection Characteristic quantity, abrasion width calculation formula be：
W = l B + 2 3 | l - ( l A + l B ) |
Wherein, l is the width not wearing away rail；
Abrasion depth calculation formula is：
V=z tan60 °.
8. rail wear automatic testing method as claimed in claim 2, it is characterised in that the laser image of the rail of collection, Before extracting the laser image characteristic quantity related to rail wear amount, there is the step that laser image is processed, described laser image Process includes Image semantic classification and Edge extraction.
9. rail wear automatic testing method as claimed in claim 8, it is characterised in that described Image semantic classification includes as follows Step：
First by image gray processing, draw the histogram of gray level image, find out gray scale and concentrate scope；
Then use following formula, grey level enhancement is carried out to gray level image, make image become apparent from；
y = 255 × ( x - a ) ( b - a )
Wherein：A, b are respectively the left and right boundary point of gray value integrated distribution in gray level image histogram, and x, y represent gray scale respectively Gray value before and after enhancing.
10. rail wear automatic testing method as claimed in claim 8, it is characterised in that use described Edge extraction Method, comprise the steps：
Appoint the medium filtering brightness curve taking a distribution image vegetarian refreshments in the horizontal direction, take respectively in this curve peak-peak both sides Go out the maximum continuity point of brightness step change, take the spacing of midpoint p and q, p and q of this two groups of continuity points as detection template Diameter；
If the brightness of image be f (i, j), take in picture field a round s (c, r) as detection template, wherein c is the center of circle, its Coordinate is (ic,jc), r is radius；
Definition s (c, r) in the set of pixel, and remember the brightness of pixel in round s and be：
X = Σ ( i , j ) ∈ X f ( i , j )
Move in making the detection template center of circle little scope in the horizontal direction, calculate each pixel intensity in the detection template of each position With in the range of being somebody's turn to do, brightness and maximum template home position, be a Pixel-level roof edge point of this bright wisp, utilizes minimum Square law fitting a straight line, this straight line is a wordline laser image center line, and described little scope is about putting centered on the center of circle The image of each 2 times of radiuses is interval, to obtain length l of two sections of straight line portioies of laser imageAAnd lB, two sections of linear laser images Between length l of changeover portionC.
CN201610765942.8A 2016-08-30 2016-08-30 Rail wear automatic testing method Expired - Fee Related CN106441107B (en)

Priority Applications (5)

Application Number Priority Date Filing Date Title
CN201610765942.8A CN106441107B (en) 2016-08-30 2016-08-30 Rail wear automatic testing method
CN201810556247.XA CN109059775B (en) 2016-08-30 2016-08-30 Steel rail abrasion detection method with image edge extraction step
CN201810556266.2A CN108830841B (en) 2016-08-30 2016-08-30 Method for calculating laser image characteristic quantity selection threshold
CN201810555967.4A CN108662983B (en) 2016-08-30 2016-08-30 Method for detecting and calculating correlation coefficient of steel rail abrasion
CN201810556239.5A CN108731599B (en) 2016-08-30 2016-08-30 Steel rail abrasion depth calculation method

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Application Number Priority Date Filing Date Title
CN201610765942.8A CN106441107B (en) 2016-08-30 2016-08-30 Rail wear automatic testing method

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