CN103034861B - The recognition methods of a kind of truck brake shoe breakdown and device - Google Patents

Abstract

The invention discloses the recognition methods of a kind of truck brake shoe breakdown, including: from present image, extract the segmentation feature of three angles；Segmentation feature according to described three angles determines the lorry brake shoe characteristic area of present image；The feature of lorry brake shoe is extracted from the lorry brake shoe characteristic area of present image；Use support vector machine (SVM) algorithm that the feature calculation of lorry brake shoe draws the eigenvalue of present image, judge whether lorry brake shoe exists fault according to described eigenvalue and preset Fault Identification value.The invention also discloses the identification device of a kind of truck brake shoe breakdown, use the present invention to be avoided that manually-operated error, it is ensured that the discrimination of fault, Accident prevention occurs timely, thus ensures operation security.

Description

The recognition methods of a kind of truck brake shoe breakdown and device

Technical field

The present invention relates to image processing field, particularly relate to recognition methods and the device of a kind of truck brake shoe breakdown.

Background technology

At present, lorry Block brake is the tread brake mode that domestic railway freight-car is commonly used, the lock on lorry brake shoe Watt pricker is mainly used to prevent lorry brake shoe from coming off, and once brake shoe drill is lost, and in lorry running, lorry brake shoe is the most very likely Come off, cause brake failure, if more seriously lorry brake shoe just comes off on rail, then can cause derailment accident.In recent years Coming, the Ministry of Railways widelys popularize lorry operation troubles image dynamic detection system (TFDS) key position each to operating train and enters Row imaging, and completed Fault Identification by artificial image browsing.This be there is poor efficiency by the mode manually carrying out Fault Identification, know The problems such as rate is unstable, cannot meet the demand for development of safe train operation.

Visible, prior art uses TFDS carry out brake shoe breakdown identification, excessively rely on manual operation, therefore cannot ensure The discrimination of fault, and then cannot occur by Accident prevention timely, thus operation security cannot be ensured.

Summary of the invention

In view of this, it is an object of the invention to provide recognition methods and the device of a kind of truck brake shoe breakdown, be avoided that Manually-operated error, it is ensured that the discrimination of fault, Accident prevention occurs timely, thus ensures operation security.

For reaching above-mentioned purpose, the technical scheme is that and be achieved in that:

The invention provides the recognition methods of a kind of truck brake shoe breakdown, the method includes:

The segmentation feature of three angles is extracted from present image；

Segmentation feature according to described three angles determines the lorry brake shoe characteristic area of present image；

The feature of lorry brake shoe is extracted from the lorry brake shoe characteristic area of present image；

Use support vector machine (SVM, Support Vector Machine) algorithm that the feature calculation of lorry brake shoe is obtained Go out the eigenvalue of present image, judge whether lorry brake shoe exists event according to described eigenvalue and preset Fault Identification value Barrier.

In such scheme, the described segmentation feature extracting three angles from present image, including: from TDFS periodically Extracting present image, the Gray Projection that present image carries out three angles obtains three drop shadow curves；To all drop shadow curves It is filtered, using the maximum in each drop shadow curve filtered all as the segmentation feature of three angles of present image.

In such scheme, the described segmentation feature according to three angles determines the lorry brake shoe characteristic area of present image, Including: use the marginal information of Canny operator extraction present image, according to the segmentation feature that angle is zero degree, determine lorry lock The left margin of watt characteristic area and the coordinate figure of right margin；According to the segmentation feature that angle is-25 ° and 25 °, determine lorry brake shoe The coboundary of characteristic area and the coordinate figure of lower boundary.

In such scheme, the described feature extracting lorry brake shoe from the lorry brake shoe characteristic area of present image, including: Use the method estimated based on background area, the image in the lorry brake shoe characteristic area of present image is split and obtains lorry lock Watt region binary picture；Use element marking method, from the binary picture of described lorry brake shoe region, extract largest connected district Territory；Use Canny operator, the binary picture from largest connected region extracts lorry brake shoe edge contour；According to described Lorry brake shoe edge contour extracts the feature of lorry brake shoe.

In such scheme, the feature of described lorry brake shoe includes: the smooth features value of lorry brake shoe edge contour, lorry lock Watt convex and concave feature value of edge contour, the jaggedness of lorry brake shoe edge contour, lorry brake shoe edge contour solid by being worth, lorry The compactness of brake shoe edge contour, the circle of lorry brake shoe edge contour, the length-width ratio of lorry brake shoe edge contour, lorry lock The area of watt edge contour and the girth of lorry brake shoe edge contour.

In such scheme, before the described segmentation feature extracting three angles from present image, the method also includes: make Positive and negative samples training is set up respectively with the lorry brake shoe drill image that there is fault with the lorry brake shoe drill image that there is not fault Collection；The feature of all images in extraction positive and negative samples training set, nine Wei Te that composition positive and negative samples training set is corresponding respectively Levying vector, the value that the positive and negative samples that uses SVM algorithm to calculate is corresponding, using value corresponding for negative sample training set as fault Discre value.

Present invention also offers the identification device of a kind of truck brake shoe breakdown, this device includes: characteristic extracting module and knowledge Other module；Wherein,

Characteristic extracting module, for extracting the segmentation feature of three angles, according to described three angles from present image Segmentation feature determine the lorry brake shoe characteristic area of present image, from the lorry brake shoe characteristic area of present image, extract goods The feature of brake watt, is sent to identification module by the feature of described lorry brake shoe；

Identification module, for using the feature calculation of described lorry brake shoe that characteristic extracting module sent by SVM algorithm to obtain Go out the eigenvalue of present image, judge whether lorry brake shoe exists event according to described eigenvalue and preset Fault Identification value Barrier.

In such scheme, described characteristic extracting module, specifically for periodically extracting present image from the TDFS at place, The Gray Projection that present image carries out three angles obtains three drop shadow curves；All drop shadow curves are filtered, will filter The maximum in each drop shadow curve after ripple is all as the segmentation feature of three angles of present image.

In such scheme, described characteristic extracting module, specifically for using the edge letter of Canny operator extraction present image Breath, according to the segmentation feature that angle is zero degree, determines left margin and the coordinate figure of right margin of lorry brake shoe characteristic area；According to Angle is the segmentation feature of-25 ° and 25 °, determines coboundary and the coordinate figure of lower boundary of lorry brake shoe characteristic area.

In such scheme, described characteristic extracting module, the method estimated based on background area specifically for use, will be current Image segmentation in the lorry brake shoe characteristic area of image obtains lorry brake shoe region binary picture；Use element marking method, Largest connected region is extracted from the binary picture of described lorry brake shoe region；Use Canny operator, from largest connected region Binary picture in extract lorry brake shoe edge contour；The spy of lorry brake shoe is extracted according to described lorry brake shoe edge contour Levy.

In such scheme, described characteristic extracting module, specifically for extract lorry brake shoe edge contour smooth features value, The convex and concave feature value of lorry brake shoe edge contour, the jaggedness of lorry brake shoe edge contour, consolidating of lorry brake shoe edge contour lean on Value, the compactness of lorry brake shoe edge contour, the circle of lorry brake shoe edge contour, the length and width of lorry brake shoe edge contour The girth of ratio, the area of lorry brake shoe edge contour and lorry brake shoe edge contour is as the feature of lorry brake shoe.

In such scheme, described identification module, it is also used for there is not the lorry brake shoe drill image of fault and there is event The lorry brake shoe drill image of barrier sets up positive and negative samples training set respectively；Extract all images in positive and negative samples training set respectively Feature, composition nine dimensional feature vectors corresponding to positive and negative samples training set, use the positive and negative samples pair that SVM algorithm calculates The value answered, using value corresponding for negative sample training set as Fault Identification value.

The recognition methods of truck brake shoe breakdown provided by the present invention and device, can extract three automatically from present image The segmentation feature of angle；Segmentation feature according to described three angles determines the lorry brake shoe characteristic area of present image；From working as The lorry brake shoe characteristic area of front image extracts the feature of lorry brake shoe；And then according to the feature of described lorry brake shoe, and Fault Identification condition determines whether lorry brake shoe exists fault.So, it becomes possible to avoid manually-operated error, it is ensured that fault Discrimination, Accident prevention occurs timely, thus ensures operation security.

Accompanying drawing explanation

Fig. 1 is the recognition methods schematic flow sheet of the truck brake shoe breakdown of the present invention；

Fig. 2 is the identification device composition structural representation of the truck brake shoe breakdown of the present invention；

Fig. 3 is test result table.

Detailed description of the invention

The basic thought of the present invention is: extract the segmentation feature of three angles from present image；According to described three angles The segmentation feature of degree determines the lorry brake shoe characteristic area of present image；Extract from the lorry brake shoe characteristic area of present image The feature of lorry brake shoe；SVM algorithm is used the feature calculation of lorry brake shoe to draw the eigenvalue of present image, according to described spy Value indicative and preset Fault Identification value judge whether lorry brake shoe exists fault.

Below in conjunction with the accompanying drawings and specific embodiment the present invention is further described in more detail.

The recognition methods of the truck brake shoe breakdown of the present invention, as it is shown in figure 1, comprise the following steps:

Step 101: extract the segmentation feature of three angles from present image.

Concrete, from TDFS, periodically extract present image, the Gray Projection that present image carries out three angles obtains To three drop shadow curves；All drop shadow curves are filtered, using equal for the maximum in each drop shadow curve filtered as The segmentation feature of three angles of present image；

Here, described three angles are-25 ° 0 ° and 25 ° of three angles set；

Described Gray Projection includes: be extended by present image, the image after being expanded；Image after extension is entered Row projection calculates, and carries out discrete obtaining drop shadow curve to projecting calculated image.

Described being extended by present image as mending gray scale in image border is the pixel of zero, it is to avoid image limit during projection The error that edge is caused, it is possible to use below equation calculates:

$\∀(i,j)\∈R^2,$ $I^{\′}(i,j)=\left\{\begin{array}{cc}I(i,j)& (i,j)\∈\mathrm{\Ω}\\ 0& (i,j)\∉\mathrm{\Ω}\end{array}\right.;$ Wherein, Ω represents infinitely great.

Wherein, described projection calculating can use formula:

Wherein, $i=\sqrt{r^2+l^2}\cos [\mathrm{\θ}+a\tan \left(\frac{l}{r}\right)],$ $j=\sqrt{r^2+l^2}\sin [\mathrm{\θ}+a\tan \left(\frac{l}{r}\right)],$ Assume to set up Coordinate system, zero is o, if present image I is highly m, width is n,For projecting direction, two-dimensional image I will be projected to Projection line Starting point is o,WithIt is mutually perpendicular to.R is any point on projection line, and p was r point, and withParallel straight line, l For any point on it, (i, j) from projection lineVertical dimension, I (i, j) be image point (i, j) grey scale pixel value at place, If projection line and x-axis angle theta are projecting direction angle, θ is-25 ° 0 ° or 25 °.

Described carry out discrete following formula to be used to calculate to projecting calculated image:

Wherein,θ is-25 ° 0 ° and 25 °,Currently Image I is highly m, and width is n, δ_θR () represents image Gray Projection on the θ of projecting direction angle, it can reflect that image is being thrown The grey scale change in shadow deflection θ direction.

Described all drop shadow curves are filtered using formula:

$h\left(x\right)=\left\{\begin{array}{ccc}1& x_2\&le;x\&le;x_1& \\ -1& x_3\&le;x<x_1\&cup;& x_2<x\&le;x_4\end{array}\right.;$ Wherein, x₁, x₂, x₃, x₄For constant, meet $\left\{\begin{array}{c}|x_1-x_2|=H\\ |x_3-x_4|=2H\end{array}\right.,$ H estimates for segmentation characteristic width.

Step 102: determine the lorry brake shoe characteristic area of present image according to the segmentation feature of described three angles.

Particularly as follows: use the marginal information of Canny operator extraction present image, according to the segmentation feature that angle is zero degree, Determine left margin and the coordinate figure of right margin of lorry brake shoe characteristic area；According to the segmentation feature that angle is-25 ° and 25 °, really The coboundary of order brake watt characteristic area and the coordinate figure of lower boundary.

Here, described according to the segmentation feature that angle is zero degree, determine left margin and the right of lorry brake shoe characteristic area The coordinate figure on boundary includes: using angle is that the marginal information of present image is divided into two subgraphs in left and right by the segmentation feature of zero degree Picture, calculates the grey scale pixel value sum of two subimages respectively, compares grey scale pixel value sum big of said two subimage Little, if the grey scale pixel value sum of left side subimage is relatively big, then the coordinate figure of the right margin of lorry brake shoe characteristic area is equal to zero Degree segmentation feature abscissa value, deduct preset right margin distance value, the coordinate of the left margin of lorry brake shoe characteristic area Value deducts preset width value equal to the coordinate figure of its right margin；Otherwise, the coordinate figure of the left margin of lorry brake shoe characteristic area Equal to the abscissa value of the segmentation feature of zero degree, add preset left margin distance value, the right margin of lorry brake shoe characteristic area Coordinate figure adds preset width value equal to the coordinate figure of its left margin；

Such as, it is assumed that the marginal information of present image is I, angle is that the segmentation of zero degree is characterized as l in figure₂, will currently scheme Marginal information I of picture is divided into two subimages in left and right and is respectively I₁And I₂；Calculate two subimage I respectively₁And I₂Pixel ash Angle value sum edge1 and edge2；If edge1 ＞ edge2, then B_rigth=l₂-h₁, B_left=B_rigth-N；As edge1 ＜ edge2 Time, B_left=l₂+h₂, B_right=B_left+N；Wherein, l₂For the abscissa value of the segmentation feature of zero degree, h₁For preset right margin Distance value, h₂For preset left margin distance value, N is preset width value；

Wherein, $\mathrm{edge}1=\underset{j=0}{\overset{N_1}{\mathrm{\&Sigma;}}}\underset{i=0}{\overset{M_1}{\mathrm{\&Sigma;}}}{I}_1(i,j),$ $\mathrm{edge}2=\underset{j=0}{\overset{N_2}{\mathrm{\&Sigma;}}}\underset{i=0}{\overset{M_2}{\mathrm{\&Sigma;}}}{I}_2(i,j);$ Wherein I₁(i j) is subimage I₁Point (i, J) grey scale pixel value at place, M₁, N₁For I₁Height and width；I₂(i j) is subimage I₂Point (i, j) grey scale pixel value at place, M₂, N₂For subimage I₂Height and width.

Described according to the segmentation feature that angle is-25 ° and 25 °, determine that the coboundary of lorry brake shoe characteristic area is with following The coordinate figure on boundary, including: set angle straight line l corresponding to the segmentation feature of-25 °₁With image coboundary intersection point abscissa value For top1, lower boundary intersection point abscissa value is bot1；Angle is the straight line l corresponding to segmentation feature of 25 °₃With image coboundary Intersection point abscissa value is top3, and lower boundary intersection point abscissa value is bot3, calculating ratio ratio=| (top1-top3)/ (bot1-bot3)|；As ratio ＜ 1, lorry brake shoe drill region is positioned at the tip position of present image；As ratio ＞ 1, Lorry brake shoe drill region is positioned at the bottom position of present image；According to the frame for movement feature in lorry brake shoe drill region, according to pine Relaxation principle extracts the coordinate figure of coboundary and lower boundary.Wherein, described lax principle is prior art, is used for ensureing selected goods Brake watt characteristic area can completely represent lorry brake shoe, and its implementation does not repeats.

Step 103: extract the feature of lorry brake shoe from the lorry brake shoe characteristic area of present image.

Concrete, use the method estimated based on background area, by the figure in the lorry brake shoe characteristic area of present image As segmentation obtains lorry brake shoe region binary picture；Use element marking method, from described lorry brake shoe region binary picture The largest connected region of middle extraction；Use Canny operator, the binary picture from largest connected region extracts lorry brake shoe limit Edge profile；The feature of lorry brake shoe is extracted according to described lorry brake shoe edge contour.

Here, the method that described background area is estimated is prior art, does not repeats；Element marking method is existing skill Art, does not repeats；Canny operator is prior art, does not repeats.

The feature of described lorry brake shoe includes: the smooth features value of lorry brake shoe edge contour, lorry brake shoe edge contour Convex and concave feature value, the jaggedness of lorry brake shoe edge contour, lorry brake shoe edge contour solid by being worth, lorry brake shoe edge wheel The compactness of exterior feature, the circle of lorry brake shoe edge contour, the length-width ratio of lorry brake shoe edge contour, lorry brake shoe edge contour Area and the girth of lorry brake shoe edge contour.

Wherein, the calculating of the smooth features value of described lorry brake shoe edge contour includes: calculate lorry brake shoe edge The central point of profile, is expressed as polar coordinate by lorry brake shoe edge contour centered by described central point；It is expressed as pole to described The lorry brake shoe edge contour of coordinate form carries out discretization calculating, obtains the lorry brake shoe edge contour of discretization；To described After the lorry brake shoe edge contour of discretization is normalized calculating, it is ranked up according to preset rule, then obtains after sequence To result carry out wavelet decomposition and obtain wavelet coefficient, use wavelet coefficient to calculate smooth features value；

The described central point calculating lorry brake shoe edge contour may include that hypothesis lorry brake shoe edge contour be x, then with Relative arc length s is parameter, then lorry brake shoe edge contour is represented by:Wherein 0≤s≤1,For lorry Brake shoe edge contour；Then the central point computing formula of lorry brake shoe edge contour is $\stackrel{\&RightArrow;}{x_0}={\&Integral;}_0^1\stackrel{\&RightArrow;}{x\left(s\right)}\mathrm{ds}=({\&Integral;}_0^{1}x\left(s\right)\mathrm{ds},{\&Integral;}_0^{1}y\left(s\right)\mathrm{ds}),$ Wherein 0≤s≤1.

Described centered by central point, lorry brake shoe edge contour be expressed as polar coordinate and can be expressed as:

$\left\{\begin{array}{c}\mathrm{\&theta;}\left(s\right)=\&angle;(\stackrel{\&RightArrow;}{x}\left(s\right)-\stackrel{\&RightArrow;}{x_0})\\ r\left(s\right)={\left|\right|\stackrel{\&RightArrow;}{x}\left(s\right)-\stackrel{\&RightArrow;}{x_0}\left|\right|}_2\\ \stackrel{\&RightArrow;}{z}\left(t\right)=(\mathrm{\&theta;}\left(s\right),\left(r\left(s\right)\right).\end{array}\right.,$ Wherein, the polar angle of vector under ∠ represents polar coordinate system.

Described the described lorry brake shoe edge contour being expressed as polar form is carried out discretization calculate can be to obtain Polar coordinate (r (the s of each discrete point on profile_i), θ (s_i)), whereinI=0,1 ... N-1, N are discrete point number.

Described normalization calculates and can use formula:

Described it is ranked up may include that note θ ' (s according to preset rule_i) it is θ ' (i), r ' (s_i) it is r ' (i), to r ' I () is resequenced: R (i)=r ' (η (i)) so that rightThere is θ (η (i)) ＜ θ (η (j))；

The described wavelet decomposition that carries out the result that obtains after sequence obtains wavelet coefficient and may include that $d\left(i\right)=r^{\&prime;}\left(\mathrm{\&eta;}\left(i\right)\right)\&CircleTimes;\mathrm{\&psi;}\left(i\right);$

Described use wavelet coefficient calculates smooth features value: E=| | d (i) | |₂。

The formula used that calculates of the convex and concave feature value of described lorry brake shoe edge contour is:Its In, c (n), n=1, the sequence of points of 2 N lorry brake shoe edge contours, c (n)=| | c (n)-c₀| |, n=1,2 ..., N, $c_0=\frac{1}{N}\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}c\left(n\right);$

The rectangular degree computing formula of described lorry brake shoe edge contour can be:Wherein A₀It it is the face of profile Long-pending, A_MERBeing the area of the minimum enclosed rectangle of profile, acquisition methods is prior art, does not repeats.

Admittedly the property computing formula of leaning on of described lorry brake shoe edge contour can be:Wherein, A represents the face of profile Long-pending, CA is the area of its minimal convex polygon, and acquisition methods is prior art, does not repeats.

The compactness computing formula of described lorry brake shoe edge contour can be:Wherein, P represents the week of profile Long, A represents that area, acquisition methods are prior art, does not repeats.

The circle computing formula of described lorry brake shoe edge contour can be:

Wherein,μ_RIt it is the average distance from regional barycenter to boundary point.δ_RIt it is the distance mean square deviation from regional barycenter to boundary point.

The width of the minimum enclosed rectangle that length-width ratio is lorry brake shoe edge contour of described lorry brake shoe edge contour and length Ratio；

The area of described lorry brake shoe edge contour can be: the number of pixels in the range of lorry brake shoe edge contour is carried out Statistics obtains.

The girth of described lorry brake shoe edge contour can be: the boundary length of lorry brake shoe edge contour.

Step 104: use SVM algorithm the feature calculation of lorry brake shoe to draw the eigenvalue of present image, according to described Eigenvalue and preset Fault Identification value judge whether lorry brake shoe exists fault.

Here, the determination method of described Fault Identification value is: uses and there is not the lorry brake shoe image of fault and there is event The lorry brake shoe image of barrier sets up positive and negative samples training set respectively；Extract all images in positive and negative samples training set respectively Feature, nine dimensional feature vectors that composition positive and negative samples training set is corresponding, the positive and negative samples using SVM algorithm to calculate is corresponding Value, using value corresponding for negative sample training set as Fault Identification value.Wherein, described SVM algorithm be embodied as prior art, this In do not repeat.

Described according to eigenvalue and preset Fault Identification value judges whether lorry brake shoe exists fault and include: if current The eigenvalue of image is equal to Fault Identification value, it is determined that lorry brake shoe breaks down；Otherwise, it determines there is not event in lorry brake shoe Barrier.

As in figure 2 it is shown, the invention provides the identification device of a kind of truck brake shoe breakdown, this device includes: feature extraction Module 21 and identification module 22；Wherein,

Characteristic extracting module 21, for extracting the segmentation feature of three angles, according to described three angles from present image The segmentation feature of degree determines the lorry brake shoe characteristic area of present image, extracts from the lorry brake shoe characteristic area of present image The feature of lorry brake shoe, is sent to identification module 22 by the feature of described lorry brake shoe；

Identification module 22, based on the feature using described lorry brake shoe that characteristic extracting module 21 sent by SVM algorithm Calculate the eigenvalue drawing present image, judge whether lorry brake shoe exists according to described eigenvalue and preset Fault Identification value Fault.

Described characteristic extracting module 21, specifically for periodically extracting present image, to current figure from the TDFS at place Gray Projection as carrying out three angles obtains three drop shadow curves, is filtered all drop shadow curves, by filtered respectively Maximum in individual drop shadow curve is all as the segmentation feature of three angles of present image.Wherein, described three angles are for setting -25 ° 0 ° and 25 ° of three angles.

Described characteristic extracting module 21, the image specifically for present image is extended, after being expanded；Will extension After image carry out projection and calculate, carry out discrete obtaining drop shadow curve to projecting calculated image.

Described characteristic extracting module 21, is the pixel of zero specifically for mending gray scale in image border, it is to avoid scheme during projection The error caused as edge, is extended present image, it is possible to use below equation calculates:

$\&ForAll;(i,j)\&Element;R^2,$ $I^{\&prime;}(i,j)=\left\{\begin{array}{cc}I(i,j)& (i,j)\&Element;\mathrm{\&Omega;}\\ 0& (i,j)\&NotElement;\mathrm{\&Omega;}\end{array}\right.;$ Wherein, Ω represents infinitely great.

Described characteristic extracting module 21, calculates specifically for using below equation to carry out projection:

Wherein, $i=\sqrt{r^2+l^2}\cos [\mathrm{\&theta;}+a\tan \left(\frac{l}{r}\right)],$ $j=\sqrt{r^2+l^2}\sin [\mathrm{\&theta;}+a\tan \left(\frac{l}{r}\right)],$ Assume to set up Coordinate system, zero is o, if present image I is highly m, width is n,For projecting direction, two-dimensional image I will be projected to Projection line Starting point is o,WithIt is mutually perpendicular to.R is any point on projection line, and p was r point, and withParallel straight line, l For any point on it, (i, j) from projection lineVertical dimension, I (i, j) be image point (i, j) grey scale pixel value at place, if Projection line and x-axis angle theta are projecting direction angle, and θ is-25 ° 0 ° or 25 °.

Described characteristic extracting module 21, specifically for using following formula to carry out discrete to projecting calculated image:

Wherein,θ is-25 ° 0 ° and 25 °,Currently Image I is highly m, and width is n, δ_θR () represents image Gray Projection on the θ of projecting direction angle, it can reflect that image is being thrown The grey scale change in shadow deflection θ direction.

Described characteristic extracting module 21, specifically for using below equation that all drop shadow curves are filtered:

$h\left(x\right)=\left\{\begin{array}{ccc}1& x_2\&le;x\&le;x_1& \\ -1& x_3\&le;x<x_1\&cup;& x_2<x\&le;x_4\end{array}\right.;$ Wherein, x₁, x₂, x₃, x₄For constant, meet $\left\{\begin{array}{c}|x_1-x_2|=H\\ |x_3-x_4|=2H\end{array}\right.,$ H estimates for segmentation characteristic width.

Described characteristic extracting module 21, specifically for using the marginal information of Canny operator extraction present image, according to angle Degree is the segmentation feature of zero degree, determines left margin and the coordinate figure of right margin of lorry brake shoe characteristic area；According to angle be- The segmentation feature of 25 ° and 25 °, determines coboundary and the coordinate figure of lower boundary of lorry brake shoe characteristic area.

Described characteristic extracting module 21, believes the edge of present image specifically for the segmentation feature using angle to be zero degree Breath is divided into two subimages in left and right, calculates the grey scale pixel value sum of two subimages respectively, compares said two subimage The size of grey scale pixel value sum, if the grey scale pixel value sum of left side subimage is relatively big, then lorry brake shoe characteristic area The coordinate figure of right margin is equal to the abscissa value of the segmentation feature of zero degree, deducts preset right margin distance value, and lorry brake shoe is special The coordinate figure of the left margin levying region deducts preset width value equal to the coordinate figure of its right margin；Otherwise, lorry brake shoe feature The coordinate figure of the left margin in region is equal to the abscissa value of the segmentation feature of zero degree, adds preset left margin distance value, lorry lock The coordinate figure of the right margin of watt characteristic area equal to the coordinate figure of its left margin plus preset width value；

Such as, it is assumed that the marginal information of present image is I, angle is that the segmentation of zero degree is characterized as l in figure₂, will currently scheme Marginal information I of picture is divided into two subimages in left and right and is respectively I₁And I₂；Calculate two subimage I respectively₁And I₂Pixel ash Angle value sum edge1 and edge2；If edge1 ＞ edge2, then B_rigth=l₂-h₁, B_left=B_rigth-N；As edge1 ＜ edge2 Time, B_left=l₂+h₂, B_right=B_left+N；Wherein, l₂For the abscissa value of the segmentation feature of zero degree, h₁For preset right margin Distance value, h₂For preset left margin distance value, N is preset width value；

Wherein, $\mathrm{edge}1=\underset{j=0}{\overset{N_1}{\mathrm{\&Sigma;}}}\underset{i=0}{\overset{M_1}{\mathrm{\&Sigma;}}}{I}_1(i,j),$ $\mathrm{edge}2=\underset{j=0}{\overset{N_2}{\mathrm{\&Sigma;}}}\underset{i=0}{\overset{M_2}{\mathrm{\&Sigma;}}}{I}_2(i,j);$ Wherein I₁(i j) is subimage I₁Point (i, J) grey scale pixel value at place, M₁, N₁For I₁Height and width；I₂(i j) is subimage I₂Point (i, j) grey scale pixel value at place, M₂, N₂For subimage I₂Height and width.

Described characteristic extracting module 21, specifically for setting angle straight line l corresponding to the segmentation feature of-25 °₁With image Coboundary intersection point abscissa value is top1, and lower boundary intersection point abscissa value is bot1；Angle is corresponding to the segmentation feature of 25 ° Straight line l₃Being top3 with image coboundary intersection point abscissa value, lower boundary intersection point abscissa value is bot3, calculating ratio ratio= |(top1-top3)/(bot1-bot3)|；As ratio ＜ 1, lorry brake shoe drill region is positioned at the tip position of present image； As ratio ＞ 1, lorry brake shoe drill region is positioned at the bottom position of present image；Machinery knot according to lorry brake shoe drill region Structure feature, extracts the coordinate figure of coboundary and lower boundary according to lax principle.

Described characteristic extracting module 21, the method estimated based on background area specifically for use, by the goods of present image Image segmentation in brake watt characteristic area obtains lorry brake shoe region binary picture；Use element marking method, from described goods Brake watt region binary picture extracts largest connected region；Use Canny operator, the binary system from largest connected region Image extracts lorry brake shoe edge contour；The feature of lorry brake shoe is extracted according to described lorry brake shoe edge contour.

Here, the method that described background area is estimated is prior art, does not repeats；Element marking method is existing skill Art, does not repeats；Canny operator is prior art, does not repeats.

Described characteristic extracting module 21, specifically for calculating the central point of lorry brake shoe edge contour, in described Centered by heart point, lorry brake shoe edge contour is expressed as polar coordinate；To the described lorry brake shoe edge being expressed as polar form Profile carries out discretization calculating, obtains the lorry brake shoe edge contour of discretization；Lorry brake shoe edge wheel to described discretization After exterior feature is normalized calculating, it is ranked up according to preset rule, then the result that obtains after sequence is carried out wavelet decomposition obtains To wavelet coefficient, wavelet coefficient is used to calculate smooth features value；

Wherein, described in calculate the central point of lorry brake shoe edge contour and may include that hypothesis lorry brake shoe edge contour is x, Then with relative arc length s as parameter, then lorry brake shoe edge contour is represented by:Wherein 0≤s≤1,For goods Brake watt edge contour；Then the central point computing formula of lorry brake shoe edge contour is $\stackrel{\&RightArrow;}{x_0}={\&Integral;}_0^1\stackrel{\&RightArrow;}{x\left(s\right)}\mathrm{ds}=({\&Integral;}_0^{1}x\left(s\right)\mathrm{ds},{\&Integral;}_0^{1}y\left(s\right)\mathrm{ds}),$ Wherein 0≤s≤1.

Described centered by central point, lorry brake shoe edge contour be expressed as polar coordinate and can be expressed as:

$\left\{\begin{array}{c}\mathrm{\&theta;}\left(s\right)=\&angle;(\stackrel{\&RightArrow;}{x}\left(s\right)-\stackrel{\&RightArrow;}{x_0})\\ r\left(s\right)={\left|\right|\stackrel{\&RightArrow;}{x}\left(s\right)-\stackrel{\&RightArrow;}{x_0}\left|\right|}_2\\ \stackrel{\&RightArrow;}{z}\left(t\right)=(\mathrm{\&theta;}\left(s\right),\left(r\left(s\right)\right).\end{array}\right.,$ Wherein, the polar angle of vector under ∠ represents polar coordinate system.

Described the described lorry brake shoe edge contour being expressed as polar form is carried out discretization calculate can be to obtain Polar coordinate (r (the s of each discrete point on profile_i), θ (s_i)), whereinI=0,1 ... N-1, N are discrete point number.

Described normalization calculates and can use formula:

Described it is ranked up may include that note θ ' (s according to preset rule_i) it is θ ' (i), r ' (s_i) it is r ' (i), to r ' I () is resequenced: R (i)=r ' (η (i)) so that rightThere is θ (η (i)) ＜ θ (η (j))；

The described wavelet decomposition that carries out the result that obtains after sequence obtains wavelet coefficient and may include that $d\left(i\right)=r^{\&prime;}\left(\mathrm{\&eta;}\left(i\right)\right)\&CircleTimes;\mathrm{\&psi;}\left(i\right);$

Described use wavelet coefficient calculates smooth features value: E=| | d (i) | |₂。

The formula used that calculates of the convex and concave feature value of described lorry brake shoe edge contour is:Its In, c (n), n=1, the sequence of points of 2 N lorry brake shoe edge contours, c ' (n)=| | c (n)-c₀| |, n=1, 2, N, $c_0=\frac{1}{N}\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}c\left(n\right);$

The rectangular degree computing formula of described lorry brake shoe edge contour can be:Wherein A₀It it is the face of profile Long-pending, A_MERIt it is the area of the minimum enclosed rectangle of profile.

Admittedly the property computing formula of leaning on of described lorry brake shoe edge contour can be:Wherein, A represents the face of profile Long-pending, CA is the area of its minimal convex polygon.

The compactness computing formula of described lorry brake shoe edge contour can be:Wherein, P represents the week of profile Long, A represents area.

The circle computing formula of described lorry brake shoe edge contour can be:

Wherein,μ_RIt it is the average distance from regional barycenter to boundary point.

δ_RIt it is the distance mean square deviation from regional barycenter to boundary point.

The width of the minimum enclosed rectangle that length-width ratio is lorry brake shoe edge contour of described lorry brake shoe edge contour and length Ratio；

The area of described lorry brake shoe edge contour can be: the number of pixels in the range of lorry brake shoe edge contour is carried out Statistics obtains.

The girth of described lorry brake shoe edge contour can be: the boundary length of lorry brake shoe edge contour.

Described identification module 22, specifically for preserving Fault Identification value.

Described identification module 22, if the eigenvalue specifically for present image is equal to Fault Identification value, it is determined that lorry lock Watt break down；Otherwise, it determines lorry brake shoe does not break down.

Described identification module 22, is also used for there is not the lorry brake shoe image of fault and there is the lorry brake shoe of fault Image sets up positive and negative samples training set respectively；The feature of all images in extraction positive and negative samples training set respectively, just forming, Nine dimensional feature vectors that negative sample training set is corresponding, use the value that the positive and negative samples that calculates of SVM algorithm is corresponding, by negative sample Value corresponding to this training set is as Fault Identification value.

Said apparatus can be installed in the management equipment of TDFS as logic module.

The method and device that the application of the invention provides, truck brake shoe breakdown sample image 75, non-event are chosen in test Barrier sample image 75 is set up sample set and is carried out classifier training, for testing the adaptability of recognizer, uses cross validation (cross validation is called again cross validation to thought, and first by training sample v decile, a copy of it is used as test set, additionally v-1 part As training set.Rotation successively, until a test set all made by every part of sample, has i.e. carried out v training and the process of prediction. Therefore, the accuracy rate of cross validation is the meansigma methods of v time) carry out Fault Identification test, make 10 experiments altogether, take experimental result Meansigma methods be final recognition result as shown in Figure 3.Visible, recognition methods and the dress of the truck brake shoe breakdown of present invention offer are provided Postponing, recognition time is less than two seconds, hence it is evident that be less than manually being distinguished by image, and loss, false drop rate are the lowest.

The above, only presently preferred embodiments of the present invention, it is not intended to limit protection scope of the present invention.

Claims (6)

1. the recognition methods of a truck brake shoe breakdown, it is characterised in that the method includes:

The segmentation feature of three angles is extracted from present image；

Segmentation feature according to described three angles determines the lorry brake shoe characteristic area of present image；

The feature of lorry brake shoe is extracted from the lorry brake shoe characteristic area of present image；

Support vector machines algorithm is used the feature calculation of lorry brake shoe to draw the eigenvalue of present image, according to described spy Value indicative and preset Fault Identification value judge whether lorry brake shoe exists fault；

Wherein, before the described segmentation feature extracting three angles from present image, the method also includes: uses and there is not event The lorry brake shoe image of barrier and the lorry brake shoe image that there is fault set up positive and negative samples training set respectively；Extract positive and negative respectively The feature of all images that sample training is concentrated, nine dimensional feature vectors that composition positive and negative samples training set is corresponding, use SVM to calculate The value that positive and negative samples that method calculates is corresponding, using value corresponding for negative sample training set as Fault Identification value；

Wherein, the described segmentation feature extracting three angles from present image, including: from fault picture dynamic detection system Periodically extracting present image in TDFS, the Gray Projection that present image carries out three angles obtains three drop shadow curves；Right All drop shadow curves are filtered, using the maximum in each drop shadow curve filtered all as three angles of present image Segmentation feature；

Described Gray Projection includes: be extended by present image, the image after being expanded；Image after extension is thrown Shadow calculates, and carries out discrete obtaining drop shadow curve to projecting calculated image；

The described segmentation feature according to three angles determines the lorry brake shoe characteristic area of present image, including: use Canny to calculate Son extracts the marginal information of present image, according to the segmentation feature that angle is zero degree, determines the left side of lorry brake shoe characteristic area Boundary and the coordinate figure of right margin；According to the segmentation feature that angle is-25 ° and 25 °, determine the coboundary of lorry brake shoe characteristic area Coordinate figure with lower boundary；

Described according to the segmentation feature that angle is zero degree, determine left margin and the coordinate figure of right margin of lorry brake shoe characteristic area Including: using angle is that the marginal information of present image is divided into two subimages in left and right by the segmentation feature of zero degree, counts respectively Calculate the grey scale pixel value sum of two subimages, compare the size of the grey scale pixel value sum of said two subimage, if the left side The grey scale pixel value sum of subimage is relatively big, then the coordinate figure of the right margin of lorry brake shoe characteristic area is special equal to the segmentation of zero degree The abscissa value levied, deducting preset right margin distance value, the coordinate figure of the left margin of lorry brake shoe characteristic area is right equal to it The coordinate figure on border deducts preset width value；Otherwise, the coordinate figure of the left margin of lorry brake shoe characteristic area is equal to zero degree Splitting the abscissa value of feature, add preset left margin distance value, the coordinate figure of the right margin of lorry brake shoe characteristic area is equal to The coordinate figure of its left margin is plus preset width value；

Described according to the segmentation feature that angle is-25 ° and 25 °, determine the coboundary of lorry brake shoe characteristic area and lower boundary Coordinate figure, including: set angle straight line l corresponding to the segmentation feature of-25 °₁With image coboundary intersection point abscissa value it is Top1, lower boundary intersection point abscissa value is bot1；Angle is the straight line l corresponding to segmentation feature of 25 °₃Hand over image coboundary Point abscissa value is top3, and lower boundary intersection point abscissa value is bot3, calculating ratio ratio=| (top1-top3)/(bot1- bot3)|；As ratio ＜ 1, lorry brake shoe drill region is positioned at the tip position of present image；As ratio ＞ 1, lorry lock Watt pricker region is positioned at the bottom position of present image；According to the frame for movement feature in lorry brake shoe drill region, according to lax principle Extract the coordinate figure of coboundary and lower boundary.

Method the most according to claim 1, it is characterised in that described carry from the lorry brake shoe characteristic area of present image The feature of picking brake watt, including: use the method estimated based on background area, by the lorry brake shoe characteristic area of present image In image segmentation obtain lorry brake shoe region binary picture；Use element marking method, enter from described lorry brake shoe region two The imaged largest connected region of middle extraction；Use Canny operator, the binary picture from largest connected region extracts lorry Brake shoe edge contour；The feature of lorry brake shoe is extracted according to described lorry brake shoe edge contour.

Method the most according to claim 2, it is characterised in that the feature of described lorry brake shoe includes: lorry brake shoe edge The smooth features value of profile, the convex and concave feature value of lorry brake shoe edge contour, the jaggedness of lorry brake shoe edge contour, lorry lock Watt edge contour solid by value, the compactness of lorry brake shoe edge contour, the circle of lorry brake shoe edge contour, lorry brake shoe The length-width ratio of edge contour, the area of lorry brake shoe edge contour and the girth of lorry brake shoe edge contour.

4. the identification device of a truck brake shoe breakdown, it is characterised in that this device includes: characteristic extracting module and identification mould Block；Wherein,

Characteristic extracting module, for extracting the segmentation feature of three angles, dividing according to described three angles from present image Cut feature and determine the lorry brake shoe characteristic area of present image, from the lorry brake shoe characteristic area of present image, extract lorry lock Watt feature, the feature of described lorry brake shoe is sent to identification module；

Identification module, works as using the feature calculation of described lorry brake shoe that characteristic extracting module sent by SVM algorithm to draw According to described eigenvalue and preset Fault Identification value, the eigenvalue of front image, judges whether lorry brake shoe exists fault；

Described identification module, is also used for the lorry brake shoe image that there is not fault and divides with the lorry brake shoe image that there is fault Do not set up positive and negative samples training set；The feature of all images in extraction positive and negative samples training set, forms positive and negative samples respectively Nine dimensional feature vectors that training set is corresponding, use the value that the positive and negative samples that calculates of SVM algorithm is corresponding, are trained by negative sample The value of collection correspondence is as Fault Identification value；

Described characteristic extracting module, specifically for periodically extracting present image from the TDFS at place, is carried out present image The Gray Projection of three angles obtains three drop shadow curves；All drop shadow curves are filtered, by each projection filtered Maximum in curve is all as the segmentation feature of three angles of present image；Described Gray Projection includes: entered by present image Row extension, the image after being expanded；Will extension after image carry out projection calculate, to project calculated image carry out from Dissipate and obtain drop shadow curve；

Described characteristic extracting module, is specifically also used for the marginal information of Canny operator extraction present image, according to angle is The segmentation feature of zero degree, determines left margin and the coordinate figure of right margin of lorry brake shoe characteristic area；According to angle be-25 ° and The segmentation feature of 25 °, determines coboundary and the coordinate figure of lower boundary of lorry brake shoe characteristic area；

Described according to the segmentation feature that angle is zero degree, determine left margin and the coordinate figure of right margin of lorry brake shoe characteristic area Including: using angle is that the marginal information of present image is divided into two subimages in left and right by the segmentation feature of zero degree, counts respectively Calculate the grey scale pixel value sum of two subimages, compare the size of the grey scale pixel value sum of said two subimage, if the left side The grey scale pixel value sum of subimage is relatively big, then the coordinate figure of the right margin of lorry brake shoe characteristic area is special equal to the segmentation of zero degree The abscissa value levied, deducting preset right margin distance value, the coordinate figure of the left margin of lorry brake shoe characteristic area is right equal to it The coordinate figure on border deducts preset width value；Otherwise, the coordinate figure of the left margin of lorry brake shoe characteristic area is equal to zero degree Splitting the abscissa value of feature, add preset left margin distance value, the coordinate figure of the right margin of lorry brake shoe characteristic area is equal to The coordinate figure of its left margin is plus preset width value；

Described according to the segmentation feature that angle is-25 ° and 25 °, determine the coboundary of lorry brake shoe characteristic area and lower boundary Coordinate figure, including: set angle straight line l corresponding to the segmentation feature of-25 °₁With image coboundary intersection point abscissa value it is Top1, lower boundary intersection point abscissa value is bot1；Angle is the straight line l corresponding to segmentation feature of 25 °₃Hand over image coboundary Point abscissa value is top3, and lower boundary intersection point abscissa value is bot3, calculating ratio ratio=| (top1-top3)/(bot1- bot3)|；As ratio ＜ 1, lorry brake shoe drill region is positioned at the tip position of present image；As ratio ＞ 1, lorry lock Watt pricker region is positioned at the bottom position of present image；According to the frame for movement feature in lorry brake shoe drill region, according to lax principle Extract the coordinate figure of coboundary and lower boundary.

Device the most according to claim 4, it is characterised in that described characteristic extracting module, specifically for using based on the back of the body The method that scape area is estimated, obtains lorry brake shoe region two by the image segmentation in the lorry brake shoe characteristic area of present image and enters Imaged；Use element marking method, from the binary picture of described lorry brake shoe region, extract largest connected region；Use Canny operator, extracts lorry brake shoe edge contour in the binary picture from largest connected region；According to described lorry brake shoe Edge contour extracts the feature of lorry brake shoe.

Device the most according to claim 4, it is characterised in that described characteristic extracting module, specifically for extracting lorry lock Watt smooth features value of edge contour, the convex and concave feature value of lorry brake shoe edge contour, the jaggedness of lorry brake shoe edge contour, Consolidating by value, the compactness of lorry brake shoe edge contour, the circle of lorry brake shoe edge contour, goods of lorry brake shoe edge contour The girth of the length-width ratio of brake watt edge contour, the area of lorry brake shoe edge contour and lorry brake shoe edge contour is as lorry The feature of brake shoe.