Automated Inspection of Defects in Glass by proper Color space ...

) Discoloration Defects: These defect areas are roughly
defined as fairly large, several millimeters in diameter,
and relatively dark and/or bright regions that stand out
against the background [6]. Example: Water marks,
which occur during heating and annealing, blackening
or red color of glass which is caused due to heating.
c) Line Defects: These are the marks or irregular patches
on the surface [7]. These occur mainly during
transportation within the factory. These can be light
(like the marks made by using some tools) or deep
(penetrating into the surface of an item and can be felt
on touching the surface). These can occur during the
process of edge grinding and corner cutting. Example:
Scratches and spots, knot line.
d) Edge defects: Edge defects are the main cause of glass
breakage during its production. They can be prevented
by detecting them at an early stage and rejecting
suspicious sheets. Production line uptime will increase;
production costs will be reduced accordingly.
Example: jagged edges.
e) Point Defects: These are the inclusions trapped inside
glass as a defect during its production. Example:
Bubbles, stone, melt inclusions.
f) Surface defects: These are the surface defects which
cause major problems for manufacturers, particularly
when the production process includes a surface
treatment stage. Example: Holes and dirt.
Different image processing algorithms are required for
the detection of different types of defects which have been
reviewed in the next section.
3. Related Work
There has been considerable research in the field of
defect detection in glass utilizing in-line automated
inspection system. Makoto, Akira and Toshio [8], in their
paper, proposed a method for detecting foreign materials in
the inspection of an LCD with its protective film in place.
The surface of the LCD is scanned under a fan-beam laser
light to obtain a set of light-section time-series images.
These images are composed into a horizontal cross-section
image of the specified depth and internal foreign materials
are detected from it. To detect the low-contrast regions on
glass, a highly robust estimator, known as the Model-
Fitting (MF) estimator [9] was developed by X. Zhuang et
al. which used a modified MF estimator to robustly
estimate the background model and as a by-product to
segment the blemish defects, the outliers. The illumination
irregularity was made as a parabolic function; the center
area was made brighter than the perimeter of the image. A
zero mean Gaussian noise was added to the ground truth
and the amount of noise, the standard deviation of the
Gaussian noise, and the depth of circle of the ground truth
are controlled for each simulation. A system [10] was
designed to reproduce the real issues of an in-line quality
IJCTA | MAY-JUNE 2012
Available online@www.ijcta.com
Nishu et al ,Int.J.Computer Technology & Applications,Vol 3 (3), 1058-1063
ISSN:2229-6093
control system which included three subsystems: an array
of several CMOS cameras, a controllable roller conveyor,
and a PC-based image processing system that is also
responsible for the control of the other subsystems. The
detection of the defects was performed by means of canny
edge detection, with thresholds chosen according to some
statistics of the images being processed. Jie Zhao, Xu Zhao
and Yuncai Liu [11] proposed a method for detection of
bubbles and inclusions. First, the defect region was located
by the method of canny edge detection, and thus the
smallest connected region (rectangle) was found. Then, the
binary information of the core region was obtained based
on an OSTU [12] and an adaptive algorithm. After noises
were removed, a Binary Feature Histogram (BFH) was
used to describe the characteristic of the glass defect.
Finally, the AdaBoost method was adopted for
classification.
The quality requirements for glass have continuously
increased over the past years. We propose an algorithm
which would be able to detect a defective area in glass with
reasonable accuracy. The design and implementation of the
algorithm is carried out using MATLAB. This would offer
manufacturers with an opportunity to significantly improve
quality and reduce costs. Significant and clear images of
the different glass defects would enable production staff to
trace back to the cause of the defect without delay ensuring
high product quality.
4. Defect Detection Process
Defects are complicated and uncertain. According to
appearing areas, defects can be separated into various
categories. There are three main modules of this process.
Those are image preprocessing module, color space
selection module and image segmentation module. The
image preprocessing module basically involves formatting
of the images in the database as per the requirement to
achieve the best results at later stages of research. Next step
is to select a color space which best shows the defect in an
image thereby reducing the further complexities during its
detection during segmentation stage.
4.1. Color space conversion
The selection of color space is one of the determinants
of the image segmentation quality; the segmentation results
would be more accurate if an appropriate color space is
adopted. During this process each input image containing a
defect is converted from RGB to four other color spaces
given below:
4.1.1 RGB
In RGB color space, the colors red, green, and blue are
mapped onto a 3-D Cartesian coordinate system which
results is a 3-D cube. The vertices of cube are the primary
colors (red, green, and blue) and the secondary colors
(cyan, yellow, and magenta) of light.
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