Oct 2004 1

Grading of Yarn Appearance Using Image Analysis and Artificial Intelligence Technique

Dariush Semnani

Oct 2004 2

HIGH LIGHT POITS

ASTM Standard Method (Section: D2255)

Previous Methods for using computer vision in yarn apparent grading

The aim of present research Methodology Results and discussion Conclusion

Oct 2004 3

ASTM Standard Method (Section: D2255)

Yarn grading based on appearance

Use for short staple yarns

Four grade, six category of yarn count

Definition of yarn grades

Methodology is based on human vision

Comparing with standard boards of yarn

Grading is only for apparent features

Not for every yarn Types

It is not capable for grading of yarn in extended region

Objective so non calculative

Mistakes of human vision, Different judges

Difficult conditions for experiment

Oct 2004 4

A Sample for Standard Boards

Cat.: 16-25 TEX

Grade A Grade B Grade C Grade D

Oct 2004 5

Previous Methods for using computer vision in yarn apparent grading

Scanning of a yarn thread in different equal sections with CCD camera.

Measuring of yarn diameter by image processing.

Detecting of unevenness of yarn.

Assign a grade to yarn based on diameter unevenness.

Modeling of yarn board by sorting of scanned threads as EIB board.

Lightening problems, not conformed with standard method.

Not capable for detecting of yarn body region.

Faults could not been classified.

Assigned grade is not conformed with standard.

Real board has objective appearance.

Oct 2004 6

The aim of present research

Present a new method for yarn grading based on appearance which is conformed with standard and useable for yarn boards

Useable for different yarn counts and yarn types. Capable to classify faults based on configuration Use image processing technique for yarn board Development of grading region Calculation of faults. Definition of numerical index of grade by fuzzy

logic

Oct 2004 7

Methodology

Definition of apparent yarn feature based on various kinds of fault

Elimination of yarn body from picture of yarn table

Elimination of background from yarn core eliminated image

Counting and classification of faults Grading of yarn appearance based on fault

factors

Oct 2004 8

Definition of apparent yarn feature based on various kinds of fault

STANDARD DEFINITION:STANDARD DEFINITION: nep with thickness of less than three

times of yarn diameter. nep with thickness of more than three

times of yarn diameter. Foreign trashes. Fettling fibers with thickness of less

than three times of yarn diameter such as small bunch, slug, or slub.

Fettling fibers with thickness of more than three times of yarn diameter such as big bunch, slug, or slub.

Unevenness coating of yarn surface with shapes of fuzziness.

Free fibers on yarn surface. These fibers are named fuzz. The fuzz should not be confused with the cover.

SUMMERIZED DEFINITION:SUMMERIZED DEFINITION: Class I: Big and entangled faults

which are tightened fibers with uniform configuration.

Class II: Big faults with less area in comparison with first category (Class I).

Class III: Non uniform and extended faults with spread configuration.

Class IV: Small spread faults such as non uniform coating fibers and short tangled hairs.

Oct 2004 9

Definition of fault classes

All of the big tightened faults such as fettling fibers, tangled fibers, big neps, big melted spots and confused helical fibers are classified in class I. These faults have big tangled area.

Other similar faults with smaller are in comparison with class I are classified in class II.

Long spread fibers as small fuzz are classified in class III. These faults are spread in area.

Small spread coating fibers and different small faults are classified in class IV.

Oct 2004 10

A Sample for faults

Class IV

Class III

Class III

Class II

Class I

Class I

Oct 2004 11

Elimination of yarn body from picture of yarn table

Scanning of board (300 DPI, Gray Scale 256, 10 by 9 inch)

More Resolution=More Processing Time & Less Res.=Less Accuracy

Converting image to binary mode (Level 110)

Elimination of yarns Cores

Oct 2004 12

Relation between original image and faults image

F=G+N

MM

i jifj /1 ,

TiIf

TjIfjif

jig

0

,

,

TjIf0

TjIfj,if

j,in

Oct 2004 13

x0

Why it is required to divide image to tapes

l0

l=100

tg

x α

00 N

N

d

d

000 l

l

d

d

x

x

00 N

N

l

l

Nl 4.12

5 degree

x=8

EX: 65 Tex

Oct 2004 14

Finding Threshold for detecting of thread coreCurve : Sorted vector of

columns means for whole tapes

a: Turning point

Th: Threshold for thread core

X : sorted vector of means

Intensity: 0-1

b

a

1

h

Th

c

X

.m

1ii2/X)1h(

hxX

hy

xX

hhy

1

)0(1 In nominal method, point (a) is located

where the difference between vector of line cb and sorted mean vector (curve) is

in minimum. The height of point (a) is desired threshold value (Th).

Oct 2004 15

Oct 2004 16

Elimination of background from yarn core eliminated image

A small threshold for detecting of black columns in every tape = 0.1

Procedure verifies every column with threshold for whole tapes.

After elimination of black columns length of tape is various among different tapes.

So the image of fault is a wide image with height of tape length and width of remained columns (not black).

Oct 2004 17

Counting and classification of faults

Convert Fault Image to blocks vector Finding optimum block size Estimation of optimum values for thresholds Classification of blocks by estimated thresholds

Oct 2004 18

Image Blocking

Tape length

Width= remained columns

Size: B by B

Width= remained columns x tape length / B2

Oct 2004 19

Finding Optimum Block Size Calculation of Variance of blocks means for different block size Optimum block size has Maximum variance of block means

Oct 2004 20

Estimation of Optimum Values for Threshold TFm= Threshold of blocks

means TFv= Threshold of blocks

variances TFm : for bigness of fault

in block TFv =for configuration of

fault in block

Thresholds are located in turning point of sorted vector of means and variances

Turning point is calculated by numerical second order differential of vectors

Tf

Sorted vector of means or deviations of blocks

Index of vector

Oct 2004 21

Classification of blocks by estimated thresholds

-Class I: Condition: fmi Tb 2.1. Big neps, slug or slubs, and other big tangled faults are classified in this class.

-Class II: Condition: fvifmifm TvbTbT &2.1. Entangled faults which are smaller than faults of class I is classified in this class.

-Class III: Condition: fvifmifm TvbTbT &2.1

. The faults which are spread in block is classified in this class.

Class IV: Any other blocks which are not classified in above classes. The spread small faults are located in this class

N1

N2

N3

N4

Oct 2004 22

The error of bad classification of faults between tow blocks

Error of the worst situation for the thinnest yarn = 0.036%

Error of the worst situation for the thickest yarn = 2.5% Region

between blocks

16 pixels

Oct 2004 23

100NM

KKNPNF

100NM

KKNPLF

100NM

KKNPHF

100NM

KKNPFF

4

3

2

1

Oct 2004 24

Table 1: Suitable Tape and block size for core elimination of threads and classification of faults in images of standard boards

Block size(Pixels)

Length tapes(Pixels)

Yarn Count of board(Tex)

Region of yarn Count(Tex)

Category

16x163584-8I

20x2040128-12II

25x25501612-16III

30x30602016-25IV

45x45905025-50V

50x501006550-590VI

Oct 2004 25

Table 2: Threshold values for classification of fault blocks in images of standard boards

CategoryGradeTfmTfv

I

A0.320.16

B0.310.15

C0.280.16

D0.290.16

II

A0.350.18

B0.400.19

C0.300.16

D0.320.17

III

A0.250.13

B0.260.13

C0.300.17

D0.290.18

IV

A0.240.11

B0.200.14

C0.200.14

D0.200.15

V

A0.370.20

B0.210.15

C0.160.12

D0.160.13

VI

A0.180.13

B0.250.15

C0.200.15

D0.190.15

Oct 2004 26

Grading of yarn appearance based on fault factors

PNF

PLF

PHF

PFF

P

ID=P.W

],,[ 432,1 wwwwW

Oct 2004 27

Estimation of W (Classifier Ceriteria) from ANN 6 ANN for 6 classifier

regarding to 6 Categories

ANN : Perceptron with 2 layers

Fuzzy layer only in use

Training : 10000 epok with training rate 0.1

w1

w2

w3

w4

PFF

PHF

PLF

PNF

ID

Grade

Fuzzy Layer

Oct 2004 28

Results and discussion Cat. I, II, III: PFF weight of big faults (PFF) which is named W1, is

more important in weights

Cat. IV and V: both of PFF and PHF are effective on yarn appearance, the effect of spread faults is less than tangled faults, but the difference of these faults with tangled faults is less than previous categories

Cat. VI: small and spread faults are more effect on yarn appearance in comparison with last categories, though weight of tangled faults is more than small and spread faults

Oct 2004 29

Tabel 3: Calculated factors for images of standard boards

CategoryGradePFFPHFPLFPNF

I

A0.0100.9125.94

B0.1903.1639.49

C0.0301.3748.12

D0.1203.4658.28

II

A001.0150.44

B001.4656.39

C0.1101.2643.1

D0.1202.0339.25

III

A0.0101.2631.03

B0.1101.6134.75

C0.77010.4929.8

D1.8608.2429.4

IV

A0.0201.0723.53

B2.750.116.2514.26

C5.072.185.3610.57

D7.547.3206.41

V

A5.72011.8136.78

B6.0313.455.1622.51

C18.26013.657.26

D15.1914.061.648.38

VI

A2.964.7410.5423.17

B6.06014.424.88

C14.412.313.6614.71

D17.317.43010.35

Oct 2004 30

Table 4: Region of grades indexes and indicator values

Grade of appearanceIndicator value

Region of apparent grade

A25Less & 20-40

B5040-60

C7060-80

D9080-100 & above

Oct 2004 31

Table 5: Faults weights which are calculated from standard pictures by neural nets

CategoryGradeW1W2W3W4

Grade Index by Modified

Weight Factor from ANN

I

A

29.9991.9991.9991.199

33.221

B59.363

C61.334

D80.393

II

A

249.9924.9924.990.241

37.397

B50.077

C69.375

D90.190

III

A

24.8911.8911.8911.041

34.934

B41.957

C70.024

D92.485

IV

A

6.3775.0772.8770.977

26.195

B50.008

C69.147

D91.509

V

A

3.6972.3970.1670.027

24.112

B56.002

C69.983

D90.359

VI

A

4.1360.9390.7890.189

29.389

B41.128

C76.785

D89.876

Oct 2004 32

Table 6: Minimum error of training for neural nets

CategorySSEMinimum error

I322.64918.666

II154.11712.711

III169.53914.654

IV4.4321.923

V36.9423.995

VI144.02811.282

Oct 2004 33

Table 7: Recommended grades for grading of yarns based on appearance

Grade of yarn appearance based on ASTM grading

Developed gradesRegion of Index of degree

A

A+0-20

A20-30

A-30-40

BB+40-50

B50-60

CC+60-70

C70-80

D

D+80-90

D90-100

D-Above 100

Oct 2004 34

Detection and classification of faults from yarn board Measurement of faults by image analysis and box counting

method Grading of yarn appearance from measured faults by a classifier

criteria. Estimation of classifier criteria by using ANN. The error of grading is acceptable. The presented method is independent to faults nature and it works

based on their apparent parameters It is possible to develop this method for grading of other types of

yarn such as worsted, woolen, filament, high bulk and textured yarns

Conclusion