International Journal of Advancements in Research & Technology, Volume 2, Issue4, April-2013
ISSN 2278-7763
254
CRACK DETECTION IN ARMOURED FIGHTING
VEHICLES USING CONTOURLET TRANSFORM ANALYSIS
S.Sumathi1
K.S.Lakshmi Narayanan2
ABSTRACT
This project aims to develop a
methodology to detect cracks with their
dimensions in the structure of Armoured
Fighting Vehicles using Contourlet
Transform analysis. The image analysis
based detection is found to be a novel,
easy and an inexpensive technique. This
project aims in identifying the cracks
present in structure of AFVs i.e. Chassis,
Turret and sub assemblies like Hydro gas
suspension systems etc. The main source of
this project includes the images taken
using a high resolution CCD camera.
Cracks are high frequencies that can be
easily detected using contourlet transform
which sub bands the image into images of
different frequencies using a Laplacian
Pyramid (LP) and thereafter decompose
the redundancy using Directional Filter
Bank (DFB). The extracted features such
as mean, standard deviation, variance,
mean square error & PSNR help in
differentiating the images with good and
bad. The project also adds up the
technique to measure the crack length and
the depth.
INTRODUCTION
Armoured Fighting vehicle is a combat
tracked or wheeled vehicle protected by
strong
armour
having
defensive
capabilities. In this AFV ,cracks usually
occur due to improper welding and casting
such
as
improper
penetration,undercutting,porosity,hot
Copyright © 2013 SciResPub.
U.Nandhini3
J.R.Ramyashri4
tearing, longitudinal cracking, shrinking
etc.,
METHODS OF CRACK DETECTION
1) DYE PENETRANT TESTING - Dye
penetrant testing examines the surface of
an item for cracks. A liquid penetrant is
applied to the surface and left to soak. The
liquid is drawn into any cracks via
capillary action. The liquid is typically
brightly colored or a fluorescent dye.
After the soak time has expired, the excess
penetrant is wiped off and a developer is
applied. The developer is usually a dry
white powder suspension that is spayed on
the component. The developer is drawn
out of the crack by reverse capillary action,
resulting in a colored indication on the
surface that is broader than the actual flaw,
and therefore, much more visible. This
technique can be used to detect surface
flaws on essentially any non-porous
material.
2)
RADIOGRAPHY
OR
X-RAY
EXAMINATION - Radiant energy from
an X-ray tube or gamma-ray is passed
through the section of the object and
intensity of emergent rays recorded on a
film held on the opposite surface. Defects
in the form of voids or cracks are recorded
as blackened areas on the film.
3) ULTRASONIC TESTING - It is based
on the principle of reflection of high
frequency sound waves. If the object
contains some defect, the wave is reflected
from the surface of the defect and returns
in a shorter period of time. If the section is
free from defect, the wave is reflected back
International Journal of Advancements in Research & Technology, Volume 2, Issue4, April-2013
ISSN 2278-7763
after travelling through the entire section.
An oscillograph is used to detect the
lengths of time.
PROPOSED TECHNIQUE
In this project we propose a novel,
inexpensive
technique
for
the
identification of cracks in Armoured
Fighting Vehicle by acquiring the image of
the cracked vehicle using high resolution
camera. The acquired image is then
processed
using
image processing
techniques and cracks are identified using
Contourlet analysis and dimension finding
by Hough Transform.
IMPLEMENTATION
Minute cracks are very difficult to
identify. Using Contourlet Transform we
can find minute cracks in an image.
Images are acquired using CCD camera.
Before applying CT the image is denoised
using 2D Median Filter. Applying the
contourlet transform an image is initially
decomposed to non-redundant low pass
sub image and redundant band pass sub
image by LP and the latter when subjected
to DFB yields the contourlet coefficients
containing the information about the crack.
Before applying threshold, the image is
subjected to contrast enhancement by
adopting
Adaptive
Histogram
Equalization. Edge is detected using canny
operator and Threshold for the image can
be determined by subtracting the mean and
standard deviation value for the image. We
assign a value of ‘0’ to the pixel values
below the threshold value featuring the
cracks and a value of ‘1’ for the pixel
values above the threshold value featuring
the background. Thus, the resulting binary
image shows the cracks of an image
isolated from its background. The resulted
image is given as input to the Hough
transform for calculating length and width
of the crack using linear and circular
Hough transform. And the classification of
the image is done using Support Vector
Machine. Thus using this proposed
Copyright © 2013 SciResPub.
255
algorithm cracks with dimension in the
image can be efficiently detected. In this
project we use the mathematical tool Mat
lab for obtaining our goal. The code for
detecting cracks and dimensions is written
in Matlab using the contourlet tool kit.
IMAGE ACQUISION USING CAMERA
IMAGE PREPROCESSING
CONTOURLET APPLICATION
IMAGE POSTPROCESSING
HOUGH TRANSFORM APPLICATION
CONDITION MONITORING BY SVM
Figure (a) Flow diagram
I) IMAGE ACQUISITION
The acquired image by the camera
is given as input to the program we have
written.
Figure (b) Input image
II) IMAGE PREPROCESSING
The dimension of the image is checked
after the acquisition of the image. If the
dimension is found to be greater than two
the image is converted from colour to gray
scale. The gray converted image is then
subjected to 2Dimensional Median filter
where the image is denoised. Every
acquired image has a inherent Gaussian
profile present in it. In order to remove it
International Journal of Advancements in Research & Technology, Volume 2, Issue4, April-2013
ISSN 2278-7763
256
we denoise the image. The denoised image
is assumed to have a higher PSNR
compared to the Original image.
The gray converted image is given to the
median filter. The median is got by sorting
all the values from low to high and taking
the median of it. The resultant median
filtered image is free of salt and pepper
noise.
Figure (d) Four level contourlet decomposed image.
IV) IMAGE POSTPROCESSING
Figure (c) Median filtered image
III) CONTOURLET APPLICATION
The pre-processed image is given as input
to the contourlet. The contourlet is a
double filter bank structure consists of
Laplacian Pyramid and Directional Filter
Bank. It is very efficient in handling
smooth contours. The Laplacian Pyramid
is used for multiscale decomposition and
Directional Filter Bank for producing
highly
directional
contourlet
Coefficients.The denoised image is subjected
to Laplacian Pyramid which decomposes
the image into low pass and band pass
image. The contourlet Co-efficient are
present in the band pass image hence the
redundant band pass image is given to the
DFB for extracting Contour Co-efficient.
The resultant image from the DFB is then
post processed by Adaptive Histogram
Equalisation. The histogram of grey levels
in a window around each pixel is
generated
first.
The
cumulative
distribution of GL’s, is used to map the
input pixel GL’s to output GL’s. If a pixel
has a GL lower than all others in the
surrounding window the output is
maximally black if it has the median value
in its window the output is 50% grey. The
resultant image is then global threshold
and edge detected using canny edge
detector. The features such as Mean,
Standard Deviation, Variance, PSNR, and
Mean Square Error are then calculated
from the edge detected image.
Figure (e) Thresholded image
Copyright © 2013 SciResPub.
International Journal of Advancements in Research & Technology, Volume 2, Issue4, April-2013
ISSN 2278-7763
FEATURES
VALUE
EXTRACTED
MEAN
0.0151
STANDARD
0.0070
DEVIATION
VARIANCE
1.0556E-007
PSNR
24
MEAN SQUARE
379.8470
ERROR
Table (a) First order features
257
LINEAR HOUGH: Each edge point in the
(x, y) plane is converted to line in (m,b)
plane. First the Hough space is quantised
into uniform intervals of accumulator cells
and initially all values are set to zero. The
dimension of the accumulator is equal to
the number of unknowns. In linear Hough
transform the dimension of the
accumulator is two and in Circular Hough
Transform the dimension is three since
three unknowns. For each pixel and its
neighbourhood, the algorithm evaluates for
the existence of edge at that pixel, if so it
will calculate the parameter of the line and
look accumulator that the parameter fall
into, and increment the value of
accumulator. The Peak value in the
accumulator represents the presence of
line.
CIRCULAR HOUGH: At each edge point
a circle is drawn with desired radius. At
the co-ordinates which belong to the
parameter of the circle, increment the
accumulator cell. The accumulator will
now contain the number of circles passing
through every individual co-ordinate. The
highest element in the accumulator
represents the presence of circle.
Figure (f) PSNR Plot
V) APPLYING HOUGH TRANSFORM
Hough transform is used to detect lines,
circles and other parametric curves. The
input to the Hough transform should be a
edge detected image. Hence the resultant
image from the post processing is given to
the Hough Transform. Hough transform is
point to line transformation. Each edge
point is converted into line in the
parametric space. The numbers of lines
intersecting at the point in the parametric
space is entered in the accumulator cell.
The output of the Hough transform is in
the form of accumulator. The Length of
the crack is determined using the linear
Hough Transform and depth is calculated
using Circular Hough Transform.
Copyright © 2013 SciResPub.
FEATURES
IN PPI
Crack length
172
Crack depth
12.8471
Table (b) crack length and depth
VI) CONDITION MONITORING BY
SVM
Support Vector machine is used to classify
whether the image is normal or abnormal
based on the comparison of the trained and
testing data. The Support Vector machine
takes a set of input data and predicts, for
each input, which of the possible classes
forms the output, making it a nonprobabilistic
BINARY
LINEAR
CLASSIFIER. The edge detected image is
given as input to the Gray level Cooccurrence matrix(GLCM).GLCM is a
tabulation of how often different
International Journal of Advancements in Research & Technology, Volume 2, Issue4, April-2013
ISSN 2278-7763
combination of gray level occur in the
image. GLCM considers the relation
between two neighbouring pixels in one
offset, as the second order texture, where
the first pixel is called reference and the
second one the neighbour pixel. Based on
the Comparison the GLCM extracts the
second order features such as AutoCorrelation,Contrast,Correlation,Cluster
Prominence,
Cluster
Shade,Dissimilarity,Sum of Squares ,Sum
average,
Sum
entropy,
Difference
Variance, Difference Entropy, Information
measure of correlation 1, Information
measure
of
correlation
2,Inverse
Difference Normalized,Energy,Maximum
Probability and Homogenity are extracted
by this GLCM.Since the image is
converted to grey image before processing
,each GLCM extracted features has low
intensity and high intensity value. These
second order features are given as input to
the SVM. Based on the trained data the
SVM result says the image as good or bad
258
CONCLUSION
Thus the crack in the metal image captured
is detected using Contourlet transform and
its dimensions are measured using Hough
transform and the images with and without
cracks are classified using Support Vector
Machine.
REFRENCES
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Contourlet Transform And Its Application
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Yonghao Ai;Ke Xu Opt. Eng. 51(11),
113605
(Nov
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Doi:10.1117/1.Oe.51.11.113605
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Figure (g) Result of the program
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Chih-Jen Lin National Taiwan University,
Taipei
106,
Taiwan
Http://Www.Csie.Ntu.Edu.Tw/~Cjlin
Initial Version: 2003 ; Last Updated: April
15, 2010.
Figure (h) Result of the program
Copyright © 2013 SciResPub.
[6] Haralick, R.M., K. Shanmugan, And I.
Dinstein, "Textural Features For Image
Classification", Ieee Transactions On
International Journal of Advancements in Research & Technology, Volume 2, Issue4, April-2013
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Transforms Documentation Coms30121 Image Processing And Computer Vision
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Jb2383@Bris.Ac.Uk
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International Journal of Advancements in Research & Technology, Volume 2, Issue4, April-2013
ISSN 2278-7763
Copyright © 2013 SciResPub.
260