CN107945165B - Textile flaw detection method based on peak value coverage values and areal calculation - Google Patents
Textile flaw detection method based on peak value coverage values and areal calculation Download PDFInfo
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Abstract
The present invention provides a kind of textile flaw detection method based on peak value coverage values and areal calculation, this method analyzes the Pixel of Digital Image grayscale information based on textile surface flat under lighting source, divide the image into the grid not overlapped, the area of each grid is calculated, according to area distribution automatic positioning textile surface flaw.The present invention is especially suitable for the textile surface flaws being automatically identified in the textile flat surfaces gray-scale image acquired under steady illumination light source.
Description
Technical Field
The invention relates to the technical field of textile flaw detection, in particular to a textile flaw detection method based on peak coverage value and area calculation.
Background
The traditional manual identification accuracy of textile flaws is only 60-75% (see the documents: K.Srinivasan, P.H.Datotor, P.Radhakrishnaiah, et al. FDAS: a Wireless-based frame work analysis of defects in woven textiles structures, J.Text.Inst.83(1992) 431-. Digital image samples of flat textile surfaces (hereinafter referred to as textile images) belong to two-dimensional textures which have been proven to be generated according to a Pattern arrangement method defined by 17 Wallpaper groups (see the documents: h.y.t.ngan, g.k.h.ping, n.h.c.yung.motion-based detection for patterned fabric, patterned recognitions (2008) 1878-. Most automatic detection methods for textile defects can only process textile images of the type p 1in wallpaper groups (see document H.Y.T.Ngan, G.K.H.Pang, N.H.C.Yung.automatic textile defect detection-A view, Image and video computing 29(7) (2011)), and only a few methods can process textile images other than the type p1 (see document H.Y.T.Ngan, G.K.H.Pang, N.H.C.Yung.Motif-based textile defect detection for textile defects, Pattern recognition 1878) (see document H.Y.T.N.P.P.P.P.P.H.P.C.Y.P.P.D. for textile defects), such as Wavelet pre-processing-based reference Image difference method (see document H.W.3, G.J. detection-10) for textile defects (see document H.W.3, G.G.G.K.J. Pat. No. (G.3) and the same method can process detection-B.D. only process images of the type p 894) (see document H.Y.H.Y.T.T.P.P.P.H.P.P.P.P.P.A. 4. detection-by using Image detection method (G.K.K.K.K.3. K.A. K.P.A. 3. detection method) for textile defects in wallpaper groups (see document H.K.3. 12. C.A. K.A. 3. detection method for textile defects, G.A. K.A. 3. detection, G.A. 3, G.C.C.A. 12, G.C.C.A. 1. detection method for textile defects, G.A. by using the same method of textile defects (see document H.A. K., the Bolliger tape method (BB, hereinafter) (see H.Y.T.Ngan, G.K.H.Pang, Novel method for patterned woven tape using the Bolliger tape, Opti.Eng.45 (8) (2006)087202-1-087202-15.), the regular tape method (RB, hereinafter) (see H.Y.T.Ngan, G.K.H.Pang, Regrtinyana for patterned woven tape, IEEE Trans.Autom.Sci.Eng.6(1) (2009) 144.), the Elo evaluation method (ER, hereinafter) (see C.S.C.C.Y.Eng, H.Y.En.6 (1) (2009) 144.), the Elo evaluation method (ER, hereinafter) (see C.S.C.C.C.Y.N.H.Yb.F.F.H.51, Regulation, T.N.F.F.F.N.51, K.F.F.F.10, etc.). Although these methods can handle textile images other than p1, their computational methods are mostly based on a manually selected grid-like pattern (hereinafter referred to as a grid). For example, WGIS requires manual selection of the size and texture of a grid, and BB, RB and ER require manual definition of the grid size. These a priori knowledge reduce to some extent the automation of the machine to identify textile defects.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: in order to improve the automation degree of machine identification of textile flaws, the invention provides a textile flaw detection method based on peak coverage value and area calculation.
For clarity of presentation, reference will now be made in detail to some of the symbols and concepts related to the present invention.
Representing a set of positive integers.
Representing a set of integers including zero.
Representing a set of positive real numbers including zero.
Representing a set of real numbers including zero.
Indicates the number of elements asThe real vector of (2).
Representing a set of complex numbers.
Indicates the number of elements asThe complex vector of (2).
T denotes a matrix or vector transpose.
Represents a real matrix of n x m size, wherein
Represents a real matrix of k × n × m size, wherein
If it isAnd isThen A isi，：Represents the ith row of matrix A, A：，jRepresenting the jth column of matrix a.
If it isAnd isThen A isl，：，：Denotes the l-th layer matrix of size n × m in A, Al，i，：Denotes the ith row of the l-th layer matrix of size n × m in A, Al，：，jRepresents the jth column of the l-th layer matrix of size n × m in a.
Presentation ratioSmall maximum integers, e.g.
{aiDenotes an element a determined by an index iiA set of constituents or a multiple set.
| S | represents the number of elements in the set S, and if S is a vector, | S | represents the number of elements included in the vector, | S | is referred to as a vector length.
avg (S) or mean (S): and calculating the mean value of the set or the multiple set S, wherein elements of S are real numbers.
std (S): and calculating the standard deviation of the set or the multiple set S, wherein the elements of S are real numbers.
med (S): and (4) calculating median values of the set or the multiple set S, wherein elements of S are real numbers.
mod (S): and calculating the mode of the multiple set S, wherein the elements of S are real numbers.
max (S) denotes finding the maximum value of an element of a set or multiple sets S, e.g. max (I)c) Substitute for Chinese traditional medicineTABLE IcThe maximum gray value of the middle pixel.
max (s' condition) indicates finding the conditionIs measured.
min (S) denotes finding the minimum of an element of a set or multiple set S, e.g. min (I)c) Represents IcThe minimum gray value of the middle pixel.
args∈S(C (s)) represents the value of s when the condition C is true.
arg maxsf(s) represents s in the range of values of the variable s within the domain of definition of the function f, such that the function f(s) takes the maximum value.
arg minsf(s) represents s in the range of values of the variable s within the domain of definition of the function f, such that the function f(s) takes the minimum value.
arg maxsf1(s)，f2(s) is expressed in function f1And f2In the value range of the variable s in the intersection of the definition domains, so that the function f1(s) and f2(s) s is taken as the maximum value.
argf(s) represents the variable s in the domain of the function f(s)1And s2S in such a range that the function f(s) takes the maximum value1And s2。
arg modi({ai}) represents the corresponding multiple set { aiMode ({ a) } mode mod ({ a)i}).
dimx(I) Representing the total number of lines, dim, of the two-dimensional image Iy(I) Indicates the total number of columns of I.
Image origin: the position in the image where the pixel row-column index starts is assumed to be in the upper left corner of the image and has the value (1, 1).
I (x, y) denotes a pixel value having a row-column index (x, y) in the two-dimensional image I. Line indexStarting from the original point of the image and increasing downwards by taking 1 as step length, wherein x is more than or equal to 1 and is less than or equal to dimx(I) (ii) a Column indexStarting from the original point of the image and increasing rightward by taking 1 as a step length, and y is more than or equal to 1 and less than or equal to dimy(I)。
Image boundary: with line index dimx(I) Row and column index dimy(I) The column (c).
Cartoon component I of textile imagec: applying a Relative Total Variation (RTV) model (Xu L., Yan Q., Xia Y., Jia J., Structure Extraction from Texture vision Relative Variation, ACMTransductions on Graphics 31(6)2012 Articule 139) to a grayed textile image I to generate a grayed image I with clear edges and fuzzy Texture based on Ic，IcCalled the textile image cartoon component.
Binary textile image Itc: binarization of I Using the Bradley method (Bradley D., Roth G., Adaptive Thresholding Using the Integrated Image, Journal of Graphics Tools 12(2)200713-21)cAnd denoising the binarized Ic according to the step 1.1, and deleting the abnormal area binary object to obtain a binary image, wherein the foreground pixel value is 1, and the background pixel value is 0.
Binary object centroid: i istcAnd the average value of the line indexes and the average value of the column indexes of the foreground pixel image contained in the binary object.
Representing vectors concatenated in operand order, e.g. scalar v11 and vector v2＝[2 3]T， For scalar s1＝8，s2＝1，s3＝5，For vector v1＝[2 3]T，v2＝[5 0 4]T，
Representing element-by-element vector multiplication, e.g. vector v1＝[5 0.9 4]T，v2＝[1 0 1]TThen, then
Wherein
Graph index (i)r，ic): after the image is divided into non-overlapping grids, each grid has a unique grid row index i according to the arrangement position of the grid in the imagerAnd a unique grid index icThe index of the upper left grid in the image is (1, 1), the index of the right grid next to the grid is (1, 2), the index of the lower grid next to the grid with the index of (1, 1) is (2, 1), and so on.Indicates a grid index of (i)r，ic) In the drawing grid of (1), wherein L1，1Referred to as the first frame.
Grid pixel index: the grid is made up of pixels, so the grid is an image, and the definition of the origin of the image and the row-column index of the pixels also applies to the grid pixel index.
Size of the grid: the grid includes a number of rows and a number of columns of pixels.
The type of the texture of the graph is as follows: the type of lattice texture is generated based on the lattice segmentation and the textile grayscale image.
A grid matrix: the matrix is in the unit of a grid, that is, each element in the matrix is a grid.
Characteristic matrix: and calculating the characteristic vector of each element in the grid matrix by using a characteristic extraction method, and forming the matrix by taking the characteristic vector as a unit, namely, each element in the matrix is the characteristic vector of one grid, and the index of the element in the matrix is the same as the index of the corresponding grid in the grid matrix.
Training a sample set: n sub-pictures I1，I2...INThe resolution of the images is the same, training samples of all the images with the same type and number of the image textures generated according to the image grid segmentation are flawless images, the training sample set only contains flawless images, and the flawless images only appear in the training sample set.
Testing a sample set: similar to the training sample set, all images have the same resolution, and the types and the number of the texture of the image generated according to the image grid segmentation are the same, the arrangement mode of each image grid is consistent with that described in the training sample set definition, and different from the training sample set, the images in the test sample set contain irregular areas which have random positions and the texture does not belong to the texture type of the image grid, and the areas are defined as flaws. The images in the test sample set are called test samples, the test samples are defective images, and the images in the test sample set are all defective images.
The feature extraction method name ordered set T: representation feature extraction method f1，f2...f|T|E.g., T ═ { HOG, LBP }, then | T | ═ 2 and f1Denotes the HOG method, f2The LBP method is shown.
On the basis of the above definition, the technical scheme adopted by the invention for solving the technical problems is as follows: a textile flaw detection method based on peak coverage value and area calculation comprises two stages: a training phase and a testing phase. The training phase is based on a series of images of flawless textiles (hereinafter referred to as "flawless textiles")A flawless image) to calculate parameters required by image segmentation and flaw identification; and in the testing stage, carrying out pattern segmentation on a set of textile image according to the parameters obtained in the training stage, judging whether the pattern contains flaws or not, and finally marking the pattern containing flaws. The training phase comprises three steps: step 1, calculating graph division parameters, step 2, training sample graph division, and step 3, calculating a flawless area interval. The test phase comprises two steps: step 4, testing sample grid segmentation, and step 5, identifying flaws. The method of the invention assumes that the textile image has the following characteristics: relative to the rows and columns of the textile image, the pattern grids are transversely arranged in the direction of the image rows and longitudinally arranged in the direction of the columns; in IcIn (2), part of the grid has a geometrical shape and has a significant difference in gray level from the background pixels.
Step 1, calculating a graph division parameter. This step comprises three sub-steps, step 1.1 background pixel projection, step 1.2 calculating the peak coverage value and step 1.3 calculating the ideal size of the grid.
Step 1.1, calculating cartoon components of a gray level image I of a textile according to an RTV model, binarizing the cartoon components by using a Bradley method, denoising the binarized cartoon components by morphological corrosion and expansion operation, and obtaining the binarized I by using a Mo0re-Neighbor tracking algorithmcCalculating the area of the binary object in the binary cartoon component, and deleting the area not in the interval ((1-alpha) · m)a，(1+α)·ma) Binary object of (where maIs a binary object area median value,alpha is more than 0 and less than 1) to obtain a binaryzation textile image Itc. Calculation of ItcThe background pixel number of each line is arranged according to the ascending order of the line index to obtain the background pixel line projectionArranging the background pixel number of each column in ascending order according to the column index to obtain the background pixel row projection
Step 1.2 calculates the peak coverage value. Calculating the projection of the rows of pixels of the background of a grey-scale image I of a textileAt the peak value of (2), the peak value is atThe indexes in the sequence are arranged in an ascending order to obtain a peak value sequenceFor prTo middlePeak valueCalculated according to the following formulaCoverage value of
Similarly, calculatePeak sequence ofComputingWherein1≤ipc(ii) a Calculating prMedium peak coverage valueOrdered set of The medium elements are arranged in descending order according to size; for theThe first element of (1) Satisfy in a sequence of peaksIs/are as followsThe ordered set is called the l-th order peakThe elements in the l-th order peak are as followsThe indexes in (1) are arranged in an ascending order; for the l-th order peak, each peak is calculated to be in the same place as the previous peakThe median of the absolute values is calculatedAnd the number of occurrences thereof Composition ofCollection Composition set Middle element value groupSimilarly, according toAnd pc computing satisfiesIs/are as followsOrdered set "peak of level l"Calculate the front and back elements in the l' th peakThe absolute value of the difference between the indexes in (1) and the median value thereofAnd number of occurrences of median value Form a multiple set Form a multiple set Middle element value group
Step 1.3 calculates the ideal size of the grid. I on training sample set1，I2...INThe ith ofTraining sample IiCalculating I according to step 1.2iIs/are as followsAnd computingValue setIiIdeal number of lines ofIs defined by the following formula.
Wherein δ is a Dirac δ functiondelta function)。I.e. m isA member of (1), IiIdeal number of columns ofCalculation andsimilarly, it is sufficient to replace the term with the subscript r in the above formula with the corresponding term with the subscript c, for exampleIs replaced byThe ideal size of the grid is defined asMedian ofAndmedian of
Step 2 for training sample set I1，I2...INAnd carrying out graph grid segmentation. For the ith training sample IiThis step comprises three substeps: step 2.1 background pixel projection, step 2.2 calculation of initial segmentation position and step 2.3 calculation of final segmentation position.
The calculation process of step 2.1 comprises step 1.1 and step 1.2.
Step 2.2 calculates the initial segmentation position. For the ith training sampleIiCalculated according to step 2.1Andcalculating what is defined in step 1.2Andandandcalculated according to step 1.3Andcalculated by the following formulaThe most frequently occurringPeak value of stage
Similarly, can calculateThe most frequently occurringStep peak, i.e. replacing the term with the lower subscript r in the above formula by a valueWith corresponding items having subscript c, e.g.Is replaced bySuppose thatThere is a string of consecutive peaks in the stage peaks and each peak is at the previous peakThe absolute value of the difference between the indexes in (1) is close toThe series of peaks is atThe index in (1) is defined as the line initial division position SrThe peak value of the string is atThe indices in the level peaks conform to the following definition.
Wherein d isj+kIs shown asTwo peaks indexed j + k and j + k-1 in the level peaksThe absolute value of the difference between the medium indices,beta is more than 0 and less than 1 as parameters. Column initial dividing position ScRelated toAndis defined byAndsimilarly, it is sufficient to replace the term with the subscript r in the above formula with the corresponding term with the subscript c, for exampleIs replaced byAnd d isj+kAt this time, it representsTwo peaks indexed j + k and j + k-1 in the level peaksThe absolute value of the difference between the indices. Parameter beta pairAndthe calculation of (2) is general.
Step 2.3 calculate the final segmentation position, i.e. the line segmentation positionAnd column division positionFor the ith training sample Ii，Andare respectively the I calculated in step 2.2iLine initial dividing position SrAnd column initial division position Sc. Will be provided withThe elements in (1) are arranged in ascending order of size, and the minimum element in the elements is foundAnd maximum elementFour predicted positions are calculated as followsAnd
obtaining I by step 1.1iBinary textile imageAnd updated according to the following three conditionsAnd
in the first case: if it isComputingThe middle row index x satisfiesAverage value of the two-valued object centroidAndthe middle row index x satisfiesAverage value of the two-valued object centroidThenIs added asAnd become a new elementRecalculation by definitionAnd
in the second case: if it isComputingThe middle row index x satisfiesAverage value of the two-valued object centroidThenIs added asAnd become a new elementRecalculation by definition
In the third case: if it isThe calculation is terminated.
Repeating the calculation of the three cases untilAnduntil no further change occurs. Similarly, the update is performed in the following three casesAnd
in the first case: if it isComputingThe middle row index x satisfiesAverage value of the two-valued object centroidAndthe middle row index x satisfiesAverage value of the two-valued object centroidThenIs added asAnd become a new elementRecalculation by definitionAnd
in the second case: if it isComputingThe middle row index x satisfiesAverage value of the two-valued object centroidThenIs added asAnd become a new elementRecalculation by definition
In the third case: if it isThe calculation is terminated.
Repeating the calculation of the three cases untilAnduntil no further change occurs, at which pointThe calculation of (2) is ended.Is calculated similarlyThat is to say, theThe elements in (1) are arranged in ascending order of size, and the minimum element in the elements is foundAnd maximum elementAccording toAndupdating three cases involvedAndi.e. replacing the superscript r of each of the three cases by c, e.g.Is replaced byWhile replacing x in the inequality and formula by y, e.g.Is replaced byAccording toAndupdating three cases involvedAndi.e. replacing the superscript r of each of the three cases by c, e.g.Is replaced byWhile replacing x in the inequality and formula by y, e.g.Is replaced byAccording toAndrespectively include row and column indices, IiThe division is performed according to the row and the column where the indexes are located, and the rectangular area obtained by the division is a graph grid, which is defined as follows.
WhereinAnd an index indicating the position of the grid arranged in I.
And 3, calculating a flawless area interval. If a training sample I is set, the cartoon component I is binarizedtcIs 0, then the gridArea ai，jIs defined asNumber of foreground pixels contained therein, whereinAnd isNamely:
for training sample set I1，I2...INCarrying out grid segmentation on each image and calculating the area of the grid, and respectively recording the minimum value and the maximum value of the obtained areas as a0(i) And a1(i) Then the lower boundary a of the flawless area interval0And an upper bound a1Is defined by the following formula.
WhereinAndrespectively represent a0(i) And a1(i) Linear ordered set (linear ordered set) of (1, 2.. N), i.e., NAndthe elements in (1) are arranged in ascending order of the magnitude of the value, l andare respectively asAndthe index of the element(s) of (c),
step 4, testing sample grid segmentation. Repeating the calculations of steps 2.1 to 2.3 for a given test sample, with the difference that the training samples involved in the calculation are replaced by test samples, resulting in the row segmentation locations of the test samplesAnd column division positionAnd according toAndthe test sample is divided into grids.
And 5, identifying flaws. For a set of test samples I, carrying out grid segmentation and calculating grid area, and calculating a grid area histogramLet t denoteThe scale of the horizontal axis, i.e. the range of the area of the grid, h (t) representsOn the vertical axis, i.e. a in Ii，jOf tThe number of the edge is calculated by the following equation, and the notch value t 'and the cliff value t' are calculated.
If t 'is present, let t' be a0Otherwise, see if t 'exists, if so, set t' as a0. For I, any grid area is less than a0Or greater than a1The grid of (a) is marked as a flaw.
The invention has the beneficial effects that: the invention provides a textile flaw detection method based on peak coverage value and area calculation. The invention is particularly suitable for automatically identifying textile surface flaws in a digital image of the gray scale of the textile flat surface acquired under a stable illumination light source.
Drawings
The invention is further illustrated by the following figures and examples.
FIG. 1 is a general flow diagram of the present invention;
FIG. 2 is a schematic flow chart of the background pixel projection of step 1.1 of the present invention;
FIG. 3 is a schematic flow chart of the present invention for calculating the column initial segmentation position in step 1.2;
FIG. 4 is a schematic flow chart of the present invention for calculating the column final segmentation position in step 1.4;
FIG. 5 is a flow chart of the step 1.1 background pixel projection algorithm of the present invention;
FIG. 6 is a flowchart of the step 1.2 algorithm for calculating the peak coverage value of the present invention;
FIG. 7 is a flow chart of the step 1.3 calculation grid ideal size algorithm of the present invention;
FIG. 8 is a flowchart of the step 2.1 algorithm of the present invention to calculate the background pixel projection and coverage values;
FIG. 9 is a flowchart of the step 2.2 algorithm of calculating the initial segmentation location of the present invention;
FIG. 10 is a flow chart of the step 2.3 final segmentation location calculation algorithm of the present invention;
FIG. 11 is a flowchart of the step 4 test sample grid segmentation algorithm of the present invention;
FIG. 12 is a flow chart of a step 5 fault identification algorithm of the present invention.
Detailed Description
The present invention will now be described in detail with reference to the accompanying drawings. This figure is a simplified schematic diagram, and merely illustrates the basic structure of the present invention in a schematic manner, and therefore it shows only the constitution related to the present invention.
The implementation mode of the computing method is completed by writing a computer program, and a custom algorithm related to a specific implementation process is described by pseudo codes. The program input is a grayed textile image and the program output is a set of panels containing defects. Embodiments of the present invention include five steps, the first three steps being a training phase and the last two steps being a testing phase.
The training phase comprises the steps of:
step 1: calculating parameters required by the graph frame segmentation according to a series of flawless images to determine the ideal size of the graph frame;
step 2: carrying out lattice segmentation on the training sample set according to the ideal size of the lattice obtained in the step 1 to obtain a training sample lattice;
and step 3: calculating the area of the training sample lattices generated by the lattice segmentation in the step 2 so as to obtain a flawless area interval of the flawless images in the training sample set;
the testing phase comprises the following steps:
and 4, step 4: carrying out pattern segmentation on a given test sample according to the method in the step 2 to obtain a test sample pattern;
and 5: and (4) calculating the area of the grids of the test sample according to the method in the step (3), and comparing the calculation result with the area of the grids of the clear image to identify the grids with defects.
The order and logical relationship of the method are shown in detail in fig. 1.
These five steps are explained below.
1. Training phase
In the training stage, parameters required by the graph segmentation are calculated according to a series of gray level images of the flawless textiles, and then the graph segmentation is carried out on the flawless images and the parameters required in the testing stage are calculated. The training phase comprises three steps: step 1: calculating a graph division parameter, and step 2: and (3) dividing the training sample graph, and calculating a flawless area interval in step 3. The graph grid segmentation method provided by the invention segments the graph grid through the steps 2.1 to 2.3 according to the parameters obtained in the step 1.3.
Step 1 is used for calculating a graph division parameter, and specifically includes three substeps, namely step 1.1: background pixel projection; step 1.2: calculating a peak coverage value; step 1.3: and calculating the ideal size of the grid.
Step 1.1, see FIG. 2 for a detailed flow. For a gray level image I of a textile, calculating a cartoon component I according to an RTV modelcFrom the Bradley method to obtain IcFig. 2 shows a schematic diagram of a pixel threshold value calculated by the Bradley method in the binarization process, i.e., IcThe method calculates a local threshold value for each pixel, and obtains a binary I according to the pixel local threshold value binary Ic. Binarized IcNoise reduction is achieved by morphological erosion and dilation operations, and then denoised binarization I is obtained using Moore-Neighbor tracing algorithm (from Jia L., Liang J., Fabric design analysis based on anisotropic segmentation, Journal of the Franklin Institute 354(13 (2017)) 5694-cThe area of the binary object, namely the number of foreground pixels of the binary object, is calculated according to the binary object in (1), namely the 8-connected foreground pixel area. Obtaining an area median value m according to the area distribution of the binary objectaAll areas are not in the interval ((1-. alpha.) m)a，(1+α)·ma) From binary I to binary IcObtaining a binaryzation textile image I by intermediate deletiontc，The value range of the parameter which is manually appointed is more than or equal to 0 and less than or equal to 1, and the value of alpha is 0.6. Calculation of ItcThe background pixel number of each line in the projection image is arranged in ascending order according to the line index to obtain the background pixel line projectionCalculation of ItcThe background pixel number of each column in the image is arranged according to the ascending order of the column indexes to obtain the projection of the background pixel columnThe one-dimensional waveform in FIG. 2 isAndstep 1 the algorithm flow is detailed in figure 5.
Step 1.2, the flow is detailed in fig. 3, for a two-dimensional textile gray scale image, the initial segmentation position includes a row initial segmentation position and a column initial segmentation position, fig. 3 only shows a conceptual flow of calculating the column initial segmentation position, and the calculation process of the row initial segmentation position is similar to that of the column initial segmentation position. According toCalculating the peak value (i.e. theIncreasing to decreasing, or decreasing to increasing values, e.g. dark dots of the one-dimensional waveform of fig. 2, and pressed atThe indexes of the medium peak values are arranged in an ascending order to obtain a peak value sequenceFor prTo middlePeak valueComputingCoverage value ofWhich is defined as follows.
In the concept of a method for monitoring the temperature of a human body,is shown in prFromBoth sides start to prHead and tail move without encountering a motion larger thanThe number of peaks passed before the peak of (2) is shown in fig. 3, and the peaks having the same coverage value are represented by triangles having the same color. Similarly, calculatePeak sequence ofAnd calculateWherein
For prOr pcThe coverage value often takes a finite integer value, p as shown in FIG. 3cValues of 0, 1,2, 4, 11 and 27. The coverage values are arranged according to a descending order to obtain a coverage value setFor example of FIG. 3Taking value according to the l coverage valueCoverage value in pcPeak value ofReferred to as the l-th order peak, at which the l-th order peak is locatedThe indexes in (1) are arranged in ascending order. Calculating the adjacent peak value in the l-th peak valueI.e. each peak is at the index spacing d from the previous peakAbsolute value of difference between indexes in) to calculate median value of index spacingAnd the number of occurrences thereofFor theEach element in the set has median values of adjacent index spacing and occurrence frequency thereof, and the values of the median values form a setSimilarly, calculateAndstep 1.2 the algorithm flow is detailed in fig. 6.
Step 1.3 calculates the ideal size of the grid. According to training samples I in the training sample set1，I2...INCan calculate the ith Sample IiIs/are as followsAndthen IiIdeal number of lines ofThe definition is as follows.
Where δ is the Dirac delta function (Dirac delta function). Ideal number of columns of IIs defined bySimilarly, only need to beIn the definition, an item with a subscript r may be replaced by a corresponding item with a subscript c, e.g.Is replaced byThe ideal size of the grid is defined asMedian ofAndmedian ofStep 1.3 the algorithm flow is detailed in fig. 7.
The calculation process of step 2.1 comprises step 1.1 and step 1.2. Step 2.1 the algorithm flow is detailed in figure 8.
Step 2.2 calculates the initial segmentation position, the flow is detailed in fig. 3. For the ith training sample IiCalculated according to step 2.1Andcalculating what is defined in step 1.2Andandandcalculated according to step 1.3Andcalculated by the following formulaThe most frequently occurringPeak value of stage
WhereinAndrespectively representing projection according to background pixel linesThe value set of the coverage value calculated by the peak sequence, the median value of the index interval of the first-level peak, the occurrence times of the median value of the index interval of the first-level peak and all the levels of peaks (cPeak values corresponding to all elements of) is determined.Is calculated bySimilarly, only need to beIn the definition, an item with a subscript r may be replaced by a corresponding item with a subscript c, e.g.Is replaced by
Suppose thatThere is a string of consecutive peaks in the stage peaks and each peak is at the previous peakThe absolute value of the difference between the indexes in (1) is close toThe series of peaks is atThe index in (1) is defined as the line initial division position SrThe peak value of the string is atThe indices in the level peaks conform to the following definition.
Wherein d isj+kIs shown asTwo peaks indexed j + k and j + k-1 in the level peaksThe absolute value of the difference between the medium indices,beta is more than 0 and less than 1, and beta is 0.1. Column initial dividing position ScRelated toAndis defined byAndsimilarly, it is sufficient to replace the term with the subscript r in the above formula with the corresponding term with the subscript c, for exampleIs replaced byAnd d isj+kAt this time, it representsTwo peaks indexed j + k and j + k-1 in the level peaksThe absolute value of the difference between the indices. Parameter beta pairAndthe calculation of (2) is general. Step 2.2 the algorithm flow is detailed in fig. 9.
Step 2.3 the flow is detailed in FIG. 4, which shows only the calculated column segmentation locationsConceptual flow of (1), line segmentation positionThe calculation process of (2) is similar. Due to disturbances such as imperfections and noise, S is usuallyrAnd ScCovering only part of the image (i.e. line S)rThe image line index between the minimum and maximum values of (a) accounts for 80% of all the image line indexesLower or ScThe image column index between the minimum and maximum values of S accounts for 80% of all the image column indexes, and 80% is not included in both cases), so that the extension S is requiredrAnd Sc. For the ith training sample Ii，And ScAre respectively the I calculated in step 2.2iS ofrAnd Sc. Will be provided withThe elements in (1) are arranged in ascending order of size, and the minimum element in the elements is foundAnd maximum elementTo be provided withCalculating the deviation S for the step size1And S∞And the predicted positions of the lines close to the image boundary are calculated as followsAnd
obtaining I by step 1.1iBinary textile imageAnd updated according to the following three conditionsAnd
in the first case: if it isComputingThe middle column index y satisfiesAverage value of the two-valued object centroidAndthe middle column index y satisfiesAverage value of the two-valued object centroidThenIs added asAnd become a new elementRecalculation by definitionAnd
in the second case: if it isComputingThe middle column index y satisfiesAverage value of the two-valued object centroidThenIs added asAnd become a new elementRecalculation by definition
In the third case: if it isThe calculation is terminated.
Repeating the calculation of the three cases untilAnduntil no further change occurs. Similarly, the update is performed in the following three casesAnd
in the first case: if it isComputingThe middle column index y satisfiesAverage value of the two-valued object centroidAndthe middle column index y satisfiesAverage value of the two-valued object centroidThenIs added asAnd become a new elementRecalculation by definitionAnd
in the second case: if it isComputingThe middle column index y satisfiesAverage value of the two-valued object centroidThenIs added asAnd become a new elementRecalculation by definition
In the third case: if it isThe calculation is terminated.
Repeating the calculation of the three cases untilAnduntil no further change occurs, at which pointThe calculation of (2) is ended.Is calculated similarlyThat is to say, theThe elements in (1) are arranged in ascending order of size, and the minimum element in the elements is foundAnd maximum elementAccording toAndupdating three cases involvedAndi.e. replacing the superscript c of each of the three cases by r, e.g.Is replaced byWhile substituting y in the inequality and formula by x, e.g.Is replaced byAccording toAndupdating three cases involvedAndi.e. replacing the superscript c of each of the three cases by r, e.g.Is replaced byWhile substituting y in the inequality and formula by x, e.g.Is replaced byAccording toAndrespectively include row and column indices, IiThe division is performed according to the row and the column where the indexes are located, and the rectangular area obtained by the division is a graph grid, which is defined as follows.
WhereinAnd an index indicating the position of the grid arranged in I. For example, the lower left corner of FIG. 2, where the upper left corner is labeled L1，1，L1，1The right adjacent grid is L1，2，L1，1The next lower grid is L2，1And the likeAnd (6) pushing. Drawing gridIs composed ofAnd comprisesThe columns of (a) determine the grid boundaries. Step 2.3 the algorithm flow is detailed in fig. 10.
And 3, calculating a flawless area interval. If a training sample I is set, the cartoon component I is binarizedtcIs 0, then the gridArea ai，jIs defined asNumber of foreground pixels contained therein, whereinAnd isNamely:
the areas of the cells in the training sample set are often similar, so that there is a region of a clear cell area, which is called a clear area region. If the area of a frame is not within the clear area range, the frame may be a defective frame (a frame containing defects). For training sample set I1，I2...INCarrying out grid segmentation on each image and calculating the area of the grid, and respectively recording the minimum value and the maximum value of the obtained areas as a0(i) And a1(i) Then the lower boundary a of the flawless area interval0And an upper bound a1Is defined by the formula:
whereinAndrespectively represent a0(i) And a1(i) 1 linear ordered set, where i is 1,2Andthe elements in (1) are arranged in ascending order of the magnitude of the value, l andare respectively asAndthe index of the element(s) of (c),
2. testing phase
And on the basis of the parameters obtained in the training stage, a test stage is used for carrying out flaw detection and positioning on one image in the test sample set. The test phase comprises two steps: step 4 test sample grid segmentation and step 5 flaw identification.
Step 4, testing sample grid segmentation. Repeating the calculations of steps 2.1 to 2.3 for a given test sample, with the difference that the training samples involved in the calculation are replaced by training samplesChanging the test sample into a test sample, and finally obtaining the line segmentation position of the test sampleAnd column division positionAnd according toAndthe test sample is divided into grids.
And 5, identifying flaws. For a set of test samples I, carrying out grid segmentation and calculating grid area, and calculating a grid area histogramLet t denoteThe scale of the horizontal axis, i.e. the range of the area of the grid, h (t) representsOn the vertical axis, i.e. a in Ii，jOf tThe number of the edge is calculated by the following equation, and the notch value t 'and the cliff value t' are calculated.
If t 'is present, let t' be a0Otherwise, see if t 'exists, if so, set t' as a0. For I, any grid area is less than a0Or greater than a1The grid of (a) is marked as a flaw.
The high efficiency experiment of the invention proves that:
the defect detection effect evaluation of the method of the present invention used 56 24-bit color textile images with a pixel size of 256 × 256 provided by the industrial automation laboratory of the electrical and electronic engineering systems of hong Kong university, which were converted into 8-bit grayscale images in the experiment. The images include a pattern: box images. The box images included 30 non-defective and 26 defective images. The box image includes 5 defect types: broken ends (brookend end), holes (hole), webbing (nettingmultiple), thick streaks (thick bar) and thin streaks (thin bar), the specific number of each type of flaw being specified in the first column of table 1. All the defect images have a defect reference map (ground-route image) of the same size, and the defect reference map is a 2-value image, where 1 represents a defect and 0 represents a background. Algorithms for comparison include WGIS, BB, RB and ER, the parameter settings of which are the same as in the literature (Jia L., Liang J., textile impact based on anisotropic segmentation, Journal of the Franklin Institute 354(13), (2017) 5694-5738).
The indices used for evaluation include True Positive (TP), False Positive (FPR), True Positive Rate (TPR), False Positive Rate (FPR), Positive Predictive Value (PPV), and Negative Predictive Value (NPV). TPR measures the proportion of pixels which represent flaws in the flaw reference image and are correctly calibrated as flaws by the algorithm, FPR measures the proportion of pixels which represent background in the flaw reference image and are wrongly calibrated as flaws by the algorithm, PPV measures the proportion of flaws in the flaw reference image in the flaws output by the algorithm, and NPV measures the proportion of background in the flaw reference image in the background output by the algorithm. For TPR, PPV and NPV, the index value is larger as better, and for FPR, the smaller as better. Relevant mathematical definitions can be found in the literature (M.K.Ng, H.Y.T.Ngan, X.Yuan, et al, Patterned fibrous analysis and visualization by the method of imaging composition, IEEETranss.Autom.Sci.Eng.11 (3) (2014) 943-. The method of the present invention, index calculation method of WGIS, BB, RB and ER, is the same as that of the literature (Jia L., Liang J., textile impact based on immunological analysis, Journal of Franklin Institute 354(13) (2017) 5694-5738). The experimental hardware platform is a notebook computer with processors of Intel CoreTMi7-3610QM 230-GHz and 8.00GB memory, and the software is Windows 10 and Maltabb 8.4.
Table 1 lists the box image defect detection results, where the index value of each row for marking the defect type is the index average of the results of all test samples of the defect type calculated by the corresponding method. According to summary column of Table 1, the process of the invention has optimal global ACC (0.54) and PPV (0.67), with NPV (0.99) very close to the optimal value (1.00). The TPR of the method of the present invention is optimized for end breakage and hole type defects. The TPR (0.28) of the texture of the method of the invention is relatively close to the optimum value (0.31). The TPR of the coarse stripes is a suboptimal value, and the TPR of the fine stripes is lower. The FPR of the method is optimal or suboptimal with respect to broken ends, thick streaks and thin streaks, while the FPR of holes and textures is larger. In conclusion, the method of the invention achieves the global optimal TPR and PPV, the global NPV of the TPR and PPV is very close to the optimal value, and the method of the invention is particularly suitable for detecting the flaw of the broken end type of the box-shaped image.
TABLE 1 Box image Defect detection results
In light of the foregoing description of preferred embodiments in accordance with the invention, it is to be understood that numerous changes and modifications may be made by those skilled in the art without departing from the scope of the invention. The technical scope of the present invention is not limited to the contents of the specification, and must be determined according to the scope of the claims.
Claims (5)
1. A textile flaw detection method based on peak coverage value and area calculation is characterized in that: the method comprises a training stage and a testing stage; in the training stage, parameters required by image lattice segmentation are calculated according to a series of flaw-free textile gray level images, hereinafter referred to as flaw-free images, then the image lattice segmentation is carried out on the flaw-free images, and parameters required by flaw identification in the testing stage are calculated; a testing stage, namely performing graph division on a set of textile image according to the parameters obtained in the training stage, judging whether the graph contains flaws or not, and finally marking the graph containing the flaws;
the training phase comprises the steps of:
step 1: calculating parameters required by the graph frame segmentation according to a series of flawless images to determine the ideal size of the graph frame;
step 2: carrying out lattice segmentation on the training sample set according to the ideal size of the lattice obtained in the step 1 to obtain a training sample lattice;
and step 3: calculating the area of the training sample lattices generated by the lattice segmentation in the step 2 so as to obtain a flawless area interval of the flawless images in the training sample set;
the testing phase comprises the following steps:
and 4, step 4: carrying out pattern segmentation on a given test sample according to the method in the step 2 to obtain a test sample pattern;
and 5: calculating the area of the grid of the test sample according to the method in the step 3, and comparing the calculation result with the area of the grid of the flawless image to identify the grid with flaws;
the step 1 specifically comprises the following steps:
step 1.1: background pixel projection, calculating cartoon components of a gray level image I of a textile according to an RTV model, binarizing the cartoon components by using a Bradley method, denoising the binarized cartoon components by morphological corrosion and expansion operation, and obtaining the binarized I by using a Moore-Neighbor tracking algorithmcCalculating the area of the binary object in the binary cartoon component, and deleting the area not in the interval ((1-alpha) · m)a,(1+α)·ma) Obtaining a binary textile image I by an internal binary objecttcWherein m isaIs a binary object area median value,and 0<α<1; calculation of ItcThe background pixel number of each line is arranged according to the ascending order of the line index to obtain the background pixel line projectionArranging the background pixel number of each column in ascending order according to the column index to obtain the background pixel row projection
Step 1.2: calculating the peak value coverage value, calculating the background pixel row projection of the textile gray level image IThe peak value is projected on the background pixel line according to the peak valueThe indexes in the sequence are arranged in an ascending order to obtain a peak value sequenceFor prTo middlePeak valueCalculated according to the following formulaCoverage value of
Projected with background pixel rowsCoverage value ofThe calculation method is the same, the items of the lower corner marks r in the formula are replaced by the corresponding items with the lower corner marks c, and the calculation is carried outPeak sequence ofComputingWherein1≤ipc(ii) a Calculating prOrdered set of medium peak coverage valuesThe medium elements are arranged in descending order according to size; for theTo (1)An element Satisfy in a sequence of peaksIs/are as followsThe ordered set is called the firstPeak value of stageFirst, theThe elements in the level peak are as followsThe indexes in (1) are arranged in an ascending order; for the firstThe peak value of each stage is calculated in the range of the previous peak valueThe median of the absolute values is calculatedAnd the number of occurrences thereof Composition setComposition setMiddle element value groupSimilarly, according toAndcomputing satisfactionIs/are as followsOrdered set ofLevel peak value'Calculate the front and back elements in the l' th peakThe absolute value of the difference between the indexes in (1) and the median value thereofAnd number of occurrences of median value Form a multiple setForm a multiple setMiddle element value group
Step 1.3: calculating the ideal size of the grid, I for the training sample set1,I2…INThe ith ofTraining sample IiCalculating I according to step 1.2iIs/are as followspr，pc，And computingValue setIiIdeal number of lines ofIs defined by the formula:
where δ is the Dirac delta function (Dirac delta function),Iiideal number of columns ofCalculation andsimilarly, replacing the term with the lower subscript r in the above formula with the corresponding term with the lower subscript c; the ideal size of the grid is defined asMedian ofAndmedian of
2. A method of textile defect detection based on peak coverage and area calculations as claimed in claim 1 wherein: the step 2 specifically comprises the following steps:
step 2.1: background pixel projection, wherein the calculation process comprises a step 1.1 and a step 1.2;
step 2.2: calculating an initial segmentation position for the ith training sample IiCalculated according to step 2.1Andcalculating what is defined in step 1.2Andandandcalculated according to step 1.3Andcalculated by the following formulaThe most frequently occurringPeak value of stage
In the same way, can calculateThe most frequently occurringThe level peak value, namely replacing the item with the lower corner mark r in the above formula with the corresponding item with the lower corner mark c;
suppose thatThere is a string of consecutive peaks in the stage peaks and each peak is at the previous peakThe absolute value of the difference between the indexes in (1) is close toThe series of peaks is atThe index in (1) is defined as the line initial division position SrThe peak value of the string is atThe indices in the level peaks follow the following equation:
wherein d isj+kIs shown asTwo peaks indexed j + k and j + k-1 in the level peaksThe absolute value of the difference between the medium indices,and 0<β<1 is a parameter; column initial dividing position ScRelated toAndis defined byAndsimilarly, it is sufficient to replace the term with the subscript r in the above formula with the corresponding term with the subscript c, and dj+kAt this time, it representsTwo peaks indexed j + k and j + k-1 in the level peaksThe absolute value of the difference between the medium indices;
step 2.3: calculating the final segmentation position, i.e. the line segmentation positionAnd column division positionFor the ith training sample Ii，Andare respectively the I calculated in step 2.2iLine initial dividing position SrAnd column initial division position Sc(ii) a Will be provided withThe elements in (1) are arranged in ascending order of size to find outThe minimum element thereinAnd maximum elementFour predicted positions are calculated as followsAnd
according toAndrespectively include row and column indices, IiAnd dividing the rectangular area into a grid according to the rows and the columns of the indexes, wherein the rectangular area obtained by the division is defined as the following:
wherein, and an index indicating the position of the grid arranged in I.
3. A method of textile defect detection based on peak coverage and area calculations as claimed in claim 2 wherein: step 3, calculating the flawless area interval comprises the following steps:
let training sample I binarize cartoon component ItcIs 0, then the gridArea ai,jIs defined asNumber of foreground pixels contained, where ir, And isNamely:
for training sample set I1,I2…INCarrying out grid segmentation on each image and calculating the area of the grid, and respectively recording the minimum value and the maximum value of the obtained areas as a0(i) And a1(i) Then the lower boundary a of the flawless area interval0And an upper bound a1Is defined by the formula:
whereinAndrespectively represent a0(i) And a1(i) Linear ordered set (linear ordered set) where i is 1,2 … N, i.e.Andthe elements in (1) are arranged in ascending order of the magnitude of the value, l andare respectively asAndthe index of the element(s) of (c),
4. a method of textile defect detection based on peak coverage and area calculations as claimed in claim 3 wherein: step 4, the test sample grid segmentation specifically comprises the following steps: repeating the calculations of steps 2.1 to 2.3 for a given test sample, with the difference that the training samples involved in the calculation are replaced by test samples, resulting in the row segmentation locations of the test samplesAnd column (1)Segmentation positionAnd according toAndthe test sample is divided into grids.
5. The method of claim 4 for textile defect detection based on peak coverage and area calculations, wherein: step 5, defect identification specifically comprises the following steps: for a set of test samples I, carrying out grid segmentation and calculating grid area, and calculating a grid area histogramLet t denoteThe scale of the horizontal axis, i.e. the range of the area of the grid, h (t) representsOn the vertical axis, i.e. a in Ii,jOf tThe notch value t' and the cliff value t ″ are calculated according to the following formula:
if t 'is present, let t' be a0Otherwise, see if t 'exists, if so, set t' as a0(ii) a For I, any grid area is less than a0Or greater than a1The grid of (a) is marked as a flaw.
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