A Direct Measurement Method of Yarn Evenness Based
on Machine Vision
Junjuan Li, Baoqi Zuo, Chen Wang, Wenxiao Tu
Soochow University, College of Textile and Clothing Engineering, Suzhou, Jiangsu CHINA
Correspondence to:
Baoqi Zuo email: [email protected]
ABSTRACT
In this paper, a new yarn evenness measurement
method based on machine vision is introduced,
which is a direct measurement process, as opposed
to other methods. Two types of yarns (i.e., same
yarn count but different quality grade and same
quality grade but different yarn count) are measured
to determine the coefficient of variation unevenness,
which can be compared with the results of USTER
ME100. The yarn images are continuously captured
via an image acquisition system. To determine the
main body of the yarn accurately, the yarn images
are processed sequentially by a threshold
segmentation and morphological opening operation.
Next, the coefficient of variation (CV value) of the
diameter is calculated to characterize the yarn
evenness. Different image processing methods are
used and compared to obtain a suitable method for
use in the experiment. A more accurate, more
efficient, and faster measurement system will meet
requirements of the manufacturing of yarn; the
suitable performance of the proposed method is
illustrated using experimental results.
Keywords: Yarn evenness, machine
pictorial indices, image processing
Currently, the capacitance method is commonly
used. The representative equipment used to perform
the capacitance method is the USTER evenness
tester, which can determine the coefficient of
variation unevenness, thick place number, thin place
number, and nep number. The capacitance method
is an internationally accepted method, although it is
not suitable for on-line measurement and cannot
directly provide the characteristics of the yarn
structure. Therefore, increasing numbers of methods
are being studied for use in measuring yarn
evenness. Some methods being developed may be
more accurate, their operations may be easier, or
their speeds may be faster [1-5].
Qin W.G. introduced an on-line method of
measurement of the yarn evenness using a CCD
image sensor. The sensor is mounted onto a
movable bracket of a small car to obtain the images,
which can determine the yarn coefficient of
variation unevenness after computer and digital
image processing. This method is executed quickly
and accurately, and the result is more accurate than
other methods. This method not only improves the
quality of the yarn but also improves status of the
textile industry [6]. Using a similar method,
Sparavigna, Broglia, and Lugli evaluated the yarn
evenness via optical measurement [7]. The system
consisted of a CCD camera which records the yarn
shadow and a photo receiver which records the light
diffused from the yarn. Using this system, the signal
is used to express the change of the yarn diameter
[8-10].
vision,
INTRODUCTION
One of the important pictorial indices to express
yarn evenness is the coefficient of variation
unevenness. The grey cotton yarn evenness can be
expressed as the blackboard unevenness or the
coefficient of variation unevenness, which satisfies
the grey cotton yarn standards (GB/T 398-93). The
blackboard unevenness is commonly used, but the
result is not very accurate because of human factors.
If two pictorial indices are not consistent, then the
coefficient of variation unevenness is used to
express the result.
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In recent years, image processing has been used to
perform yarn evenness measurements. Chen L from
Tianjin Polytechnic University claimed that he
could analyze the seriplane images of cotton yarn
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using digital image processing technology [11]. The
seriplane digital images can be obtained after
smoothing treatment, thresholding segmentation
and image impairing. Next, the average diameter of
the seriplane digital image is obtained via accurate
calculations. In addition, the process is automatic,
thereby enabling further time and labor savings
[12-16].
MACHINE
The framework of the yarn evenness measurement
method is depicted in Figure 1. The method uses an
image acquisition system, a computer processing
system and an USTER evenness tester. The image
acquisition system includes the following: CCD line
sensor (6), telecentric lens (7), light source (3) and
image capture card. A Dasla industrial CCD line
sensor (S2-1y-05H40) is selected to meet the
accuracy requirements. To match with the selected
linear scan sensor, Xcelera-cl LX is chosen as the
image capture card model. An Utron telecentric lens
(MGTL60C) with 6× magnification is used based
on the field of vision in this system. The
components of the computer processing system are:
the computer image processing module and the
result output. Halcon, which is the software used
worldwide in machine vision, is used to analyze the
images. A PC with a core i7 processor operating at
3.40 GHz is used for image processing and
presenting the results. The model of the USTER
evenness tester used is USTER ME100.
In this paper, digital image processing is used to
analyze images obtained by a camera. Such digital
image processing is a form of continuous
processing, and the result of such processing will be
compared with the result of the USTER evenness
tester. Two categories of yarn are discussed in the
experiment. Most importantly, the measurement
method can directly determine the result by
determining the value of the yarn diameter. In
contrast to the other methods currently being used,
the accuracy of the diameter measurement is up to
several microns. Such a level of accuracy will meet
the future requirements.
As shown in Figure 1, a single cotton yarn (1) is
pulled by two rollers (4, 5) at a certain speed. The
images of the moving yarn are captured using the
image acquisition system. Back light by illustration
is adopted. Thus, the light source (3) is placed on
one side of the yarn, and the other devices of the
image acquisition system are placed on the other
side. Next, the yarn images are sent to the computer
image processing module to be analyzed. Finally,
the CV value of the diameter is calculated to
characterize the evenness, and the result is exported.
At the same time, the CV value of height
determined by the USTER ME100 (8) is obtained.
The result of the new measurement system can be
compared with the USTER ME100 measurement.
EXPERIMENTAL
MATERIALS
In this paper, two large classes of yarns are used to
determine the coefficient of variation unevenness.
One class has the same count but a different quality
grade defined by the USTER CLASSIMAT 5, and
the other is of the same quality grade but has a
different count. Table І presents a summary of the
data for the samples tested. The data include the
metric count, blending ratio, and yarn quality grade
for all seven varieties of yarn where each variety
was tested with five samples to determine the value;
the values can be compared with the results
obtained using the USTER ME100.
TABLE І. Yarns used in this experiment.
Number
Metric Count
Blending Ratio
(c:cotton)
Yarn Quality Grade
The Number of
Samples
1
83tex
100%c
one
five
2
83tex
100%c
two
five
3
63tex
100%c
three
five
4
63tex
100%c
five
five
5
11tex
100%c
one
five
6
10tex
100%c
one
five
7
7tex
100%c
one
five
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FIGURE 2. Original yarn image.
FIGURE 1. Yarn evenness measuring system schematic: (1)
cotton yarn, (2) guide bar, (3) light source, (4),(5) drive roller, (6)
linear scan CCD sensor, (7) telecentric lens, (8) USTER sensor.
IMAGE PROCESSING
As shown in Figure 2, image quality is slightly
affected by noise, and some hairs are found around
the main body of yarn, which is difficult to separate
from the yarn. To separate the yarn from the
background and extract the main body, an image
processing method consisting of thresholding
segmentation and morphological opening operation
is used in this measurement system.
Thresholding Segment
To separate the yarn and the background, a
gray-level threshold is used in this experiment. The
yarn and the background have different gray levels
in an image, and a threshold value can be used to
determine whether a pixel belongs to the yarn or the
background. The Gaussian histogram algorithm is
selected to determine automatic segmentation based
the difference between yarn and background gray
level. As shown in Figure 3, after thresholding
segmentation using the Gaussian histogram
algorithm, many hairs are removed, but there are
still some thick hairs and loop fibers remaining
around the main body of yarn.
FIGURE 3. The effect image after threshold segmentation with
the Gaussian histogram algorithm.
Morphology Opening
A small amount of hairiness still remained in the
images after image segmentation. To decrease the
remaining
hairiness,
morphology
opening
processing is used, which is one method of
mathematical
morphology.
Mathematical
morphology is a technology to extract objects,
smooth the edge of objects, and denoise image.
Journal of Engineered Fibers and Fabrics
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FIGURE 4. The effect image after morphology opening.
Some of the common structuring elements include
circles, hexagons and squares. According to the size
of the protrusion and the loop fiber in this image, an
appropriate structuring element and side should be
chosen to perform the morphology opening. In this
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paper, a square structuring element is used in this
system because a circular structuring element and a
hexagonal structuring element both greatly affect
the main body of the yarn. Figure 5 shows the effect
image of different sizes of the square structuring
element. The boundary of the extracted region is
observed to become smoother with increase in the
size of the square structuring element.
X = D1 - D2 + 1
(1)
4) Repeat steps 2 through 3 in a row-by-row manner
until all diameters are obtained in the image.
5) Repeat all of the steps above in all images
acquired and record every X obtained.
The conversion equation between diameter and CV
value is
Coefficient of Variation (CV value)
The yarn diameter was considered to be a
characteristic of the appearance quality of yarn. The
coefficient of variation (CV value) is primarily used
to reflect the evenness. In this paper, the yarn
diameter in the images is measured using the new
measurement system, although the result is a
relative value. The yarn diameter can be calculated
by the number of pixels occupied by the diameter as
follows:
1) Scan the region extracted in the first image from
left to right, starting from the first row.
2) Record the column ordinal D1 of the first pixel
and the column ordinal D2 of the last pixel.
3) Calculate the diameter value X:
CV =
1
X
(
)
1 n
2
∑ X − X × 100%
ni =1 i
X = D1 - D2 + 1
(2)
(3)
Where D1 is the column ordinal of the first pixel, D2
is the column ordinal of the last pixel, X is the
average of diameter values, xi is the diameter value
obtained in i row, and n is the total number of
obtained diameters.
FIGURE 5. The effect image after the morphology opening of different sizes of the square structuring element.
TESTING METHODS
To determine the yarn evenness, the capacitance
method is commonly used. The representative
equipment used in the capacitance method is the
USTER evenness tester, which can determine the
coefficient of variation unevenness, thick place
number, thin place number, and nep number. In this
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paper, the USTER ME100 apparatus is adopted,
which uses parallel plate capacitors of 8-mm width,
allowing for measurements with 8-mm resolution.
However, the yarn mass determined in a smaller
range is important to correctly detect irregularities,
as most of them have lengths that can approach
several micrometers.
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The proposed measurement method is based on the
photoelectric method, which is comprised of an
image acquisition system, a computer processing
system, and an USTER evenness tester. The images
of cotton yarn are captured continuously by the
system of the proposed measurement method, and
then are processed sequentially using threshold
segmentation and the morphological opening
operation. In this process, numbers will be obtained
for every row of pixels. Next, the main body of the
yarn is extracted, and the CV value of the diameter
is used to characterize the evenness. By calculation,
the theoretical detection accuracy of the measuring
system is 0.0023 mm, which indicates that a change
in yarn diameter exceeding 0.0023 mm can be
detected.
19
18
CV(%)
17
16
15
14
13
1
2
3
4
5
The code of samples
FIGURE 6. The CV value of 83tex yarn obtained using the two
methods.
21
The process of the proposed measurement method
is more complex than the capacitance method, but
the result is more accurate, which is determined
directly from yarn diameter rather than weight.
20
CV(%)
19
RESULTS AND DISCUSSION
The CV value obtained via the USTER ME100
tester is used as the reference standard in this paper.
The results of the new measurement system are
compared with the reference values to demonstrate
that the new measurement system is suitable to
measure and characterize the evenness of yarn as
well as other yarn defects.
USTER ME100(three)
new measurement syste(three)
USTER ME100(five)
new measurement syste(five)
18
17
16
15
1
2
3
4
5
The code of samples
FIGURE 7. The CV value of 63tex yarn using the two methods.
As indicated in Figure 6, the CV value obtained
using the proposed measurement method is similar
to the CV value obtained using the USTER ME100
tester. The curves have the same sequence, though
the distances between curves are different from each
other. The differences between these curves reflect
the differences between two test methods.
USTER ME100
new measurement system
13.5
CV(%)
13.0
12.5
12.0
(1) The former is the CV value of the diameter,
which reflects the external change in size. The
latter is the CV value of weight, which reflects the
change in weight.
(2) USTER ME100 uses parallel plate capacitors of
8-mm width, allowing for measurements with 8-mm
resolution. The data of the new measurement
system are obtained for each row of pixels. The
accuracy is up to several microns.
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USTER ME100(one)
new measurement system(one)
USTER ME100(two)
new measurement system(two)
11.5
1
2
3
4
5
The code of samples
FIGURE 8. The CV value of 11tex yarn using the two methods.
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As shown in Figure 6, Figure 7, Figure 8, Figure 9,
and Figure 10, although a high correlation exists
between the CV value of the USTER ME100 and
that of the new measurement system, there are some
minor differences.
USTER ME100
new measurement system
12.8
12.6
CV(%)
12.4
12.2
(1) The value obtained using the new measurement
system is larger than the value obtained using the
USTER ME100 in every figure.
12.0
11.8
11.6
(2) The curve obtained using the USTER ME100 is
more gently varying than the curve from the new
measurement system.
11.4
1
2
3
4
5
The code of samples
FIGURE 9. The CV value of 10tex yarn using the two methods.
The main reason leading to these differences is that
the accuracy of USTER ME100 is 8 mm rather than
the improved 0.0023-mm accuracy of the new
measurement system.
14.8
14.6
14.4
CV(%)
14.2
The most obvious difference is illustrated in Figure
8: two values of USTER ME100 are greater than
the values of the new measurement system. There
are several reasons leading to this phenomenon,
such as the value of thresholding, the use of a
suitable structuring element of morphology opening
(shape and size), the high speed of the measurement
process, and the different measurement accuracies.
If the value of thresholding is too high, small hairs
and loop fibers will still remain around the main
body of the yarn, thereby causing the value of the
new measurement to be small.
14.0
USTER ME100
new measurement system
13.8
13.6
13.4
13.2
13.0
1
2
3
4
5
The code of samples
FIGURE 10. The CV value of 7tex yarn using the two methods.
As shown in Figure 6 and Figure 7, every curve has
a similar sequence, although the yarns belong to
different quality grades, they are of the same count.
That is, the result of the new measurement system is
not affected by the quality grade of yarn.
Analyzing the results, we can conclude that the
measurement result of the new measurement system
has a strong correlation with the result of the
USTER ME100, regardless of the value or the trend
of the curve. These two measurement methods can
accurately reflect the level of yarn evenness.
However, the result of the new measurement system
is more accurate than the result of the USTER
ME100 due to the superior accuracy.
The results illustrated in Figure 8, Figure 9, and
Figure 10 indicate that there is not much difference
in the variation trend of the CV value for various
counts, but the same quality grade of 100% cotton
yarn. In addition, two curves in each figure have the
same sequence, although the distances between
curves are different from each other. This difference
is related to the yarn count, twist, or other points,
among other factors. The result of the new
measurement system is not affected by the quality
grade of yarn.
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CONCLUSION
In this paper, a dynamic measurement system for
determining cotton yarn evenness using machine
vision was proposed. Compared with the existing
approaches of measurement and assessment of yarn
evenness, such as capacitance-detecting technology,
the measurement method using machine vision has
several characteristics:
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(1) Because the image capture equipment is a linear
CCD with high resolution, the dynamic
measurement of yarn evenness can be realized with
high speed.
[4]
(2) A telecentric lens is used in the designed yarn
evenness measurement, eliminating the influence of
the jitter of yarn along the optical axis during image
capturing.
[5]
(3) The high quality yarn image can provide
information regarding the yarn after performing an
image threshold segmentation and morphology
operation.
[6]
[7]
In the experiment, the CV value of the diameter is
used to characterize the yarn evenness. Compared
with the test results of different morphology
opening processing methods, the effective image
processing method based on the Gaussian histogram
algorithm is used in threshold segmentation and the
square structuring element with a size of 30 × 30 is
selected in morphology opening processing. Finally,
compared with the results of the USTER ME100,
the new measurement method is able to directly
determine the result of the diameter rather than
weight of yarn, and the accuracy of the new
measurement method is higher than that of the
USTER ME100.
[8]
[9]
[10]
ACKNOWLEDGMENTS
We gratefully acknowledge the support of the First
Phase of Jiangsu Universities' Distinctive Discipline
Development Program for Textile Science and
Engineering of Soochow University and the
National Engineering Laboratory for Modern Silk.
[11]
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AUTHORS’ ADDRESSES
Junjuan Li
Baoqi Zuo
Chen Wang
Wenxiao Tu
Soochow University
College of Textile and Clothing Engineering
199 Renai Road
Suzhou Industrial Park
Suzhou, Jiangsu 215000
CHINA
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