inspection of faults in textile web materials using wavelets and anfis

7/27/2019 Inspection of Faults in Textile Web Materials using Wavelets and ANFIS

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Inspection of Fauts in Textie Web Materias using

Waveets and ANFISB Venktesn uSRgthy PVidhylkshmi BVinoth

Department of Eleconics and Instrumentation EngineeringKongu Engineering College, Perundurai, Erode-638 Tamilnadu, India

Email:[email protected]@yahoocoin

Abtact:Quliy is he whword of ny ype of usiness. produ wihou quliy leds o loss nd lk of usomersisfion. This is rue in se of exile indusries lso. Texilemnufuring is proess of onvering vrious ypes of ers

ino yrn, whih in urn woven ino fri. Weving proess isused o produe he fri or loh y inerling wo disin se

of yrn hreds nmely wrp nd wef yrn. In exile indusries,quliy inspeion is one of he mjor prolems for fri

mnufurers. presen, he ful deeion is done mnullyer produion of suien moun of fri. The frioined from he produion mhine re hed ino lrgerrolls nd sujeed o he inspeion frme. The nure of he

work is very dull nd repeiive. Due o mnul inspeion of hemnufured fri, here is possiiliy of humn errors wih

high inspeion ime, hene i is uneonomil. This pperproposed PC-sed inspeion sysem wih enes of low os

nd high deeion re. Boh norml nd fuly imges reproessed nd feures re exred y using Gry Level Coourrene Mrix (GLCM) nd lssiion is done usingdpive Neuro Fuzzy Inferene Sysem (NFIS). Proposed

sheme performs 3666% eer hn he exising miroonrollersed lssiion sysem.

Kewords: NFIS; Texile Defe Deeion; GLCM; WveleTrnsform.

. INTRODUCTON

The ndian textile industry has a major impact on theworld economy trough millenniums. At present, all thetextile industries aim to produce good quality fabrics withhigh production rate. n the textile sector, there are huge lossesdue to faulty fabrics.The fabric is obtained by interweaving ofwap and we y. The raw material for the garment industryis available in the form of continuous rolls. Nearly, 85% of thedefects in the gment industry are due to the faults found in

the fabrics. These faults are obtained in the fabrics due toirregular weaving of warp d we ya in the weavingprocess. Hence fabric inspection is utmost important formaintaining its quality.Most defects in cloth occur while it iswoven on the loom. Some of these fabric defects are visible,while others re not. Again some fabric defects may be

/$ IEEE

identied during weaving and some aer weaving. Themual inspection of fabric material is not economical.Hencethe investment in automated fabric defect detection is moreeconomic when reduction in labor cost and other benets areconsidered.

The various fault detection approaches for textile web

material is given in [1 ].An automated defect detection andclassication system enhances the product quality and resultsin increased productivity [2]. mproved gabor lters for textiledetection results in less computational complexity as well aspossibility of online implementation. Auto-correlation is usedas a robust algorithm for patteed and un-patteed fabricdetect detections [3]. n the fabric fault detectionmethodology, wavelet transform with multiresolution level 3gives better results than the other traditional methods likeFourier Transform and Sobel Algorithm of edge detection[4].Multi Resolution Combined Statistical and SpatiaFrequency (MRCSF) methodology is the combination of rstorder and second order statical properties combined with

spatial equency of multiresolution analysis. This methodsucceeded in classiing the fabric with repeated pattes asdefective or non-defective based on the MRCSF [5]Comparing with the traditional equency ltering techniquessuch as ideal, butterworth, exponential and trapezoid low andhigh lters, the multi-scale analysis ability of Wavelettransform performs much better, All the classiers requiretraining om the known classes of fabric defects. A largenumber of classes with large intra-class diversity remainasmajor problem in using Feed-forward Neural Network (FFN)nd Support Vector Machines (SVM) based inspectiontechniques [8] & [9].

On detail review of the above literatures, an idea aboutfabric inspection method is proposed. The rest of the paper isorganized as follows. The proposed methodology with itsblock diagram is given in section . Section discuss aboutthe wavelet transform. GLCM formulation and tureextraction is presented in section V. Results and


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Discussionare presented in section V. Conclusion is drawn insection V.n this project, ofine inspection is made.

. PROOSD THODOLOGY

Using C microcontroller, detection of fault is basicallydone by using Neural Network. The overall accuracy to detect

fault is 76.33% [].The images of fabric material withdierent faults are captured and then processed. mages withNo fault, Hole, Oil Spot and Hole with Oil Spot areconsidered for inspection.The proposed scheme tries tostrengthen the fault detection rate. t consists of ve majorsteps.

mage Acquisition reprocessing Wavelet Transform GLCM formulation and Feature xtraction Classication

First images are captured using the CCD cameras. Thesecond step involves that the RGB color image into grayconversion and the histogram equalization. The third stepinvolves the wavelet transform of the preprocessed image.Thefourh is the formulation of GLCM and feature extractions,and the h step is fault classication using Adaptive NeuroFuzzy nference System (ANFS).

There are two common types of scaning techniquesemployed for the fabric inspection cameras: line scannng andarea scaning. The disadvantage with the line scan cameras isthat they do not generate complete image at once and requiresexteal hardware to build up images om multiple line scans.For area scan cameras, the usage of transport encoders is

optional and the inspection resolution in both directions isindependent of web speed. n this project, the camera havinghigh resolution of659498 (HV ixels) with Charge CoupledDevice (CCD) sensor technology, which is capable of 71ames/second is used. The pixel data coming om the camerais converted into a digitized image by the ame grabber. Allweb inspection systems used for fabric inspection, have tocope with the mltiple camera inputs. Some systems do thisby using some kind of video multiplexer unit between thecamera and the ame grabber. A rather expensive way to copewith multiple cameras is to use one ame grabber unit percamera. 1394 FireWire cable is used for communicationbetween camera and Vision system, and theet cable is used

for communication between the Vision system and workingstation (C). Web material images are captured tough thecamera using vision system with the help of Vision Assistanttoolkit, and then processed with the help of MATLABsoware.

n the proposed scheme, the image obtained om imagegrabber or digital cameras are preprocessed and analyzed forfault detection and classication. During preprocessing, thefabric color image is converted into gray scale image. Theconverted gray scale image is then histogram equalized andwavelet transform is performed, so that the resultant imagewill have the fault in an enhanced form. Thus the resultant

image will be more suitable with distinct fault for detection.

Five co-occurrence matrices are calculated om theresultant image, four matrices in different orientations

= k: ' k = 1,2,3,4 and the h matrix is constrcted asthe mean of the preceding four matrices calculated at dierentangles. Four independent statistical features are extracted omthe GLCM [11] and given as inputs to adaptive neuro-zzynetwork. The adaptive neuro-zzy system is used to classithe dierent type of faults. The block diagram of the proposedmethod of fault detection is illustrated in the Fig. 1.

age es

! teocessg Casscato

tatve eo-

aveet zzy eeceaso yste! f

C eate

ao tacto

Figure 1. Block diagram of proposed method

. AVLT RANSFORM

The information that is not readily seen in the time domaincan be seen in the equency domain. To separate the low andhigh equency components of the image, scaling and waveletlter function coecients are used respectively. By usingthese nctions, the information contents of the image areseparated corresponding to low and high equencies.

The wavelet is constrcted by scaling function satisingthe two-scale difference equations giving in (1) and (2) [12].

x =Lkhk 2x k (1 )


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x =kgk 2x k (2)Where,

gk = (_l)kh(l- k (3)The wavelet bases obtained trough the above (1) and (2)procedure be unique, orthonoal and have desired regularity[13].Multresolution decomposition using gabor lter resultsin redundant features at different scales. This is due to nonorthogonality. Orthogonal wavelets are not redundant and aresuitable for image denoisin and compressin.Biorthogonalwavelet usually has a linear phase property and issuitable for image feature extraction. The multiresolutiondecomposition using orthogonal and compact supportedwavelet bases can be used to avoid the correlation of featuresbetween the scales.Fig. 2. shows the result of Waveletdecomposedimages of web material.

]

--'.'-"!l>l

(mIDJ

Figure 2. Two levels of wavelet decomposition

V. ATUR EXTRACTONHROUGH GLCM

mage analysis consists of two steps: Featurextraction and Recognition (Classication). Varioustecniques are presented like Markov Random Field (MRF),Gabor Filter, Spatial Gray Level Dependence Matrix(SGLDM), Gray Level Run Length Matrix (GLRM), andGray level Dependence Matrix (GLM) in [14]. Atpresent, research for texture feature extraction is focusedtrough GLCM.

A Gr Level Co-occurrence Matr (GLCM)

Features are extracted by computing GLCM, which is used todescribe the texture as a matrix of 'pair gray levelprobabilities. This helps to nd gray-level pairs, which are allmore dominant and which are all less dominant, and in some

cases, t applicable forstochastic textures by randomlydeciding pixel according to pair gray level probabilities.Fromthe GLCM, four features have been extracted. The extractedfeatures are Contrast,Correlation, nergy and Homogeneity.

CClass cation

The ANFS is used for defect classication. The extractedfour features are considered as an input to the ANFS and itclassies the given input as either Not faulty image or Hole orOil Spot or Hole with Ol Spot. ANFS parameter details areshown in table I.

TABLE A NETWORK ARAMETER

Parameters Values

No. of nputs 4

No. of Output 1

No. of Nodes 1 93No. of Linear arameter 405

No. of Non-linear arameter 24

No. of rules 81

Leaing method Hybrid

MS 0.0952

.-Figure 3. Architecture of the ANFS used for training and

testing

V. RSULTS AND DSCUSSON

n this paper, 40 images have been considered with 30 faultyimages, and results are compared with the existing systems forthe validation.The experiment is conducted in two phasesTraining and Testing. e existing microcontroller basedfabric inspection is compared with the ANFS basedinspection system.Results obtained trough the proposedscheme are compared with the existing scheme and the results


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are shown in table 2.Figure 4 gives the resultant images andFigure 4 gives the comparison of classication result.

(a) b c

Figure 4. Results of sample images with Various Defects (a)Hole (b) Oil Spot (c) Hole with Oil Spot

TABLE 2. LASSIFICATIO ESULTS

:.]0

o ofo of

nspection abricaulty

ethod magemageSamples

Manual40 30

nspection

Microcontroller

based 40 30automaticInspectionroposednspection 40 30

System

35

3025

2015

10

Mau Microontrole PoposedMethod

o of o ofmages ault

dentied lassi-as Faulty ed

29 28

21 1 8

30 29

Images Identificaed as faulty Faul Eacy laSified

Figure 5. Comparison of classication results between theexisting and proposed method

V. ONCLUSON

n this paper computer aided fabric fault detection isimplemented. Wavelet transform is performed andtheFeaturesre extracted by formulation of GLCM forimproved fault classication. roposed method performinglikes manual inspection and provides more efciency than the

existing method as well as the manual inspection method.

CKNOWLDGMNT

We sinerey aknowede the Department of Scienceand Technology for their nancial support and encouragementin carrying out this project.

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System,EEE Transactions on Industrial Electronics vol. 52, no. 4,pp. 1073-1079,2005.[3]. lhamHoseini, FaoushFarhadi and Farshad Tajeripour, "Fabric

Defect Detection Using Auto-Correlation Function, The 3rdInteational Conference on Machine Vision (EEE Trans.) pp. 557-561, 2010.

[4. Liu Shugug d QuPingge, "Fabric Defects Automatic InspectionBased on Computer Vision, Int. Con! on Image and SialProcessing (CISP '09), at Tianjin,pp. 1-5,17-19 October2009.

[5. Sabeenin R.S., Paramasivam M.. and Dinesh P.M., "ComputerVision based Defect Detection and Identication in Handloom SilkFabrics, Inteational Joual of Computer Applications vol. 42,no17,pp. 41-47,2012.

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[7]. Sungshin Kim, Mn Hung Lee and Kwang-Bang Woo, "Waveletanalysis to Fabric Defects Detection in Weaving Processes, ProcIEEE Int. Symp. In Electron vol. 3,pp.1406-1409,1999.

[8]. A. Kumar, "Neural network based detection of local textile defectsPatte Recognition Vol. 36,pp. 1645-1659,2003.

[9] A. Kumar d H C. Shen, 'exture inspection for defects using neuralnetworks and support vector machines,Proc. Int. Conference on

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[10]. Tamnun Mursalin, Fajrana Zebin ishita, Ahmed Ridwanul Islamand Dr Md ngir Alam, "Real Time Automated Fabric DefectDetection System using Microcontroller, Journal of ConvergenceInformation Technolo Vol. 3,No. I,March 2008.

[II]. Robert M. Haralick, Shnmug K. and ItsHakDinsten, "TexturaFeatures for Image Classication, EEE Trans. on systems, Man andCyeetics,Vo.SMC-3,No.6,pp.610-621,1973

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Texture Defect Detection: Sub-band domain co-occurrence matricesImage and Vision Computing Vol. 18,Issue 6-7,pp. 543-553,2000.[13]. N. ansu, R.A. Haddad, "Multiresolution Signal Decomposition

Academic Press, Boston, MA, 1992.[14]. Chung-Ming Wu, Yung-Chang Chen "Statistical Feature matrix for

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inspection of faults in textile web materials using wavelets and anfis

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