Grading of Yarn Appearance
Using Image Analysis and
Artificial Intelligence
Technique
Dariush Semnani
Oct 2004
1
HIGH LIGHT POITS
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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
2
ASTM Standard Method (Section: D2255)
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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
Oct 2004
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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
3
A Sample for Standard
Boards
Cat.: 16-25 TEX
Grade A
Oct 2004
Grade B
Grade C
Grade D
4
Previous Methods for using computer vision
in yarn apparent grading
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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.
Oct 2004
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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.
5
The aim of present research
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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
6
Methodology
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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
7
Definition of apparent yarn feature based on various kinds
of fault
STANDARD DEFINITION:
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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.
Oct 2004
SUMMERIZED DEFINITION:
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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.
8
Definition of fault classes
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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.
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Other similar faults with smaller are in comparison with class I are
classified in class II.
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Long spread fibers as small fuzz are classified in class III. These faults are
spread in area.
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Small spread coating fibers and different small faults are classified in
class IV.
Oct 2004
9
Class IV
A Sample for faults
Class II
Class III
Class I
Class III
Class I
Oct 2004
10
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
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Converting image to binary mode (Level 110)
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Elimination of yarns Cores
Oct 2004
11
Relation between original
image and faults image
F=G+N
M
 j   f i, j / M
i1
If  j  T
f
i
,
j


gi, j  
0 If
i  T

Oct 2004
f
 i, j

n 
i, j 
0

If
If
 T
j
 T
j
12
Why it is required to divide
image to tapes
d

d0
l=100
α
x

tg
5 degree
l0
x=8
EX: 65 Tex
N
N0
x
d
l


x 0 d 0 l0
l

l0
N
N0
l  12.4 N
x0
Oct 2004
13
Finding Threshold for
detecting of thread core
b
Curve : Sorted vector of
columns means for whole
tapes
a: Turning point
Th: Threshold for thread core
X : sorted vector of means
Intensity: 0-1
m.
(h  1)X / 2 

1
a
Th
c
h
i
i 1
1 h
( x  0)
X
1 h 
y
x  h
 X 
yh 
Oct 2004
X
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).
14
Oct 2004
15
Elimination of background from yarn core eliminated
image

A small threshold for detecting of black columns in
every tape = 0.1
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Procedure verifies every column with threshold for
whole tapes.
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After elimination of black columns length of tape is
various among different tapes.
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So the image of fault is a wide image with height of tape
length and width of remained columns (not black).
Oct 2004
16
Counting and classification of faults
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Convert Fault Image to blocks vector
Finding optimum block size
Estimation of optimum values for thresholds
Classification of blocks by estimated thresholds
Oct 2004
17
Image Blocking
Tape
length
Width= remained columns
Size: B by B
Width= remained columns x tape length / B2
Oct 2004
18
Finding Optimum Block Size
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Calculation of Variance of blocks means for different block size
Optimum block size has Maximum variance of block means
Oct 2004
19
Estimation of Optimum Values
for Threshold
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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
Oct 2004
Sorted vector of means or
deviations of blocks
Tf
Index of vector
20
Classification of blocks by
estimated thresholds
-Class I: Condition:
bi  1.2T fm
N1
. Big neps, slug or slubs, and other big tangled faults are classified in this class.
-Class II: Condition:
T fm  bi  1.2T fm & vbi  T fv
N2
. Entangled faults which are smaller than faults of class I is classified in this class.
-Class III: Condition:
T fm  bi  1.2T fm & vbi  T fv
N3
. 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
Oct 2004
N4
21
The error of bad classification of
faults between tow blocks
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Error of the worst
situation for the
thinnest yarn = 0.036%
Error of the worst
situation for the
thickest yarn = 2.5%
Oct 2004
16
pixels
Region
between
blocks
22
N1  K  K
PFF 
 100
MN
N2  K  K
PHF 
 100
MN
N3  K  K
PLF 
 100
MN
Oct 2004
N4  K  K
PNF 
 100
MN
23
Table 1: Suitable Tape and block size for core elimination of
threads and classification of faults in images of standard boards
Block size Length
(Pixels) tapes
(Pixels)
Yarn Count of
board
(Tex)
Region of yarn
Count
(Tex)
Category
16x16
35
8
4-8
I
20x20
40
12
8-12
II
25x25
50
16
12-16
III
30x30
60
20
16-25
IV
45x45
90
50
25-50
V
50x50
100
65
50-590
VI
Oct 2004
24
Category
Grade
Tfm
Tfv
A
0.32
0.16
B
0.31
0.15
C
0.28
0.16
D
0.29
0.16
A
0.35
0.18
B
0.40
0.19
C
0.30
0.16
D
0.32
0.17
A
0.25
0.13
B
0.26
0.13
C
0.30
0.17
D
0.29
0.18
A
0.24
0.11
B
0.20
0.14
C
0.20
0.14
D
0.20
0.15
A
0.37
0.20
B
0.21
0.15
C
0.16
0.12
D
0.16
0.13
A
0.18
0.13
B
0.25
0.15
C
0.20
0.15
D
0.19
0.15
I
II
Table 2: Threshold values for
classification of fault blocks
in images of standard boards
III
IV
V
VI
Oct 2004
25
Grading of yarn appearance based on fault factors
ID=P.W
 PFF 
 PHF 

P
 PLF 


 PNF 
Oct 2004
W  [ w1, w2, w3, w4]
26
Estimation of W (Classifier
Ceriteria) from ANN
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6 ANN for 6 classifier regarding
to 6 Categories
Fuzzy Layer
PFF
w1
ANN : Perceptron with 2 layers
PHF
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Fuzzy layer only in use
w2
ID
w3
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Training : 10000 epok with
training rate 0.1
PLF
w4
Grade
PNF
Oct 2004
27
Results and discussion
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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
28
Category
Grade
PFF
PHF
PLF
PNF
A
0.01
0
0.91
25.94
B
0.19
0
3.16
39.49
C
0.03
0
1.37
48.12
D
0.12
0
3.46
58.28
A
0
0
1.01
50.44
B
0
0
1.46
56.39
C
0.11
0
1.26
43.1
D
0.12
0
2.03
39.25
A
0.01
0
1.26
31.03
B
0.11
0
1.61
34.75
C
0.77
0
10.49
29.8
D
1.86
0
8.24
29.4
A
0.02
0
1.07
23.53
B
2.75
0.11
6.25
14.26
C
5.07
2.18
5.36
10.57
D
7.54
7.32
0
6.41
A
5.72
0
11.81
36.78
B
6.03
13.45
5.16
22.51
C
18.26
0
13.65
7.26
D
15.19
14.06
1.64
8.38
A
2.96
4.74
10.54
23.17
B
6.06
0
14.4
24.88
C
14.4
12.31
3.66
14.71
D
17.3
17.43
0
10.35
I
II
Tabel 3: Calculated factors
for images of standard
boards
III
IV
V
VI
Oct 2004
29
Table 4: Region of grades indexes and
indicator values
Indicator value
Region of apparent
grade
A
25
Less & 20-40
B
50
40-60
C
70
60-80
D
90
80-100 & above
Grade of appearance
Oct 2004
30
Category
Grade
W1
W2
W3
W4
A
33.221
B
I
Table 5: Faults weights which are
calculated from standard pictures
by neural nets
59.363
29.999
1.999
1.999
1.199
C
61.334
D
80.393
A
37.397
B
II
50.077
249.99
24.99
24.99
0.241
C
69.375
D
90.190
A
34.934
B
III
41.957
24.891
1.891
1.891
1.041
C
70.024
D
92.485
A
26.195
B
IV
50.008
6.377
5.077
2.877
0.977
C
69.147
D
91.509
A
24.112
B
V
56.002
3.697
2.397
0.167
0.027
C
69.983
D
90.359
A
29.389
B
VI
Oct 2004
Grade Index by
Modified
Weight Factor
from ANN
41.128
4.136
0.939
0.789
0.189
C
76.785
D
89.876
31
Table 6: Minimum error of training for neural nets
SSE
Minimum error
I
322.649
18.666
II
154.117
12.711
III
169.539
14.654
IV
4.432
1.923
V
36.942
3.995
VI
144.028
11.282
Category
Oct 2004
32
Table 7: Recommended grades for grading of yarns based on appearance
Grade of yarn appearance
based on ASTM grading
A
B
C
D
Oct 2004
Developed grades
Region of Index of degree
A+
0-20
A
20-30
A-
30-40
B+
40-50
B
50-60
C+
60-70
C
70-80
D+
80-90
D
90-100
D-
Above 100
33
Conclusion
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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
Oct 2004
34