Abstract: Non Destructive Testing (NDT) is an analysis technique used to inspect metal sheets and components without harming the product. NDT do not cause any change after inspection; this technique saves money and time in product evaluation, research and troubleshooting. In this study the objective is to perform NDT using soft computing techniques. Digital images are taken; Gray Level Co-occurrence Matrix (GLCM) extracts features from these images. Extracted features are then fed into the classifiers which classifies them into images with and without cracks. Three major classifiers: Neural networks, Support Vector Machine (SVM) and Linear classifiers are taken for the classification purpose. Performances of these classifiers are assessed and the best classifier for the given data is chosen

B Santhi

G Krishnamurthy

P K Ramakrishnan

S Siddharth

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Research Journal of Applied Sciences, Engineering and Technology 4(24): 5434-5437, 2012     
ISSN: 2040-7467
© Maxwell Scientific organization, 2012
Submitted: March 18, 2012 Accepted: April 14, 2012 Published: December 15, 2012
Corresponding Author: S. Siddharth, Department of Information and Communication Technology, School of Computing,
SASTRA University, India
5434
Pavement Crack Classifiers: A Comparative Study
S. Siddharth, P.K. Ramakrishnan, G. Krishnamurthy and B. Santhi
Department of Information and Communication Technology, School of Computing, 
SASTRA University, India
Abstract: Non Destructive Testing (NDT) is an analysis technique used to inspect metal sheets and components
without harming the product. NDT do not cause any change after inspection; this technique saves money and
time in product evaluation, research and troubleshooting. In this study the objective is to perform NDT using
soft computing techniques. Digital images are taken; Gray Level Co-occurrence Matrix (GLCM) extracts
features from these images. Extracted features are then fed into the classifiers which classifies them into images
with and without cracks. Three major classifiers: Neural networks, Support Vector Machine (SVM) and Linear
classifiers are taken for the classification purpose. Performances of these classifiers are assessed and the best
classifier for the given data is chosen.
Keywords: Gray Level Co-occurrence Matrix (GLCM), linear classifier, neural networks, Support Vector
Machine (SVM)
 
INTRODUCTION
Non destructive testing is presently the most efficient
and cost effective technique used to identify the existence
of damage without causing a minute harm to the testing
product. Huge growth in the field of automated crack
detection, where the digital image of the test material is
sufficient to detect the exact location of the crack in the
material. [automated] Abundance of images may affect
the efficiency of the method as the images may contain
both positive and negative results for the occurrence of
cracks. Hence soft computing techniques are used to
classify images with and without cracks; positive
outcomes are fed into to the detector to accurately detect
the miniature cracks present. Santhi et al. (2012)
Classifiers are analysied based on the performance; best
suited classifier for the required application is chosen to
classify pavement images for the occurrence of cracks.
This study provides a comparison between several
important classifiers for classifying pavement images. A
range of well-known performance metrics were used for
this comparison, thus helping in identifying the paramount
classifier. 
LITERATURE REVIEW
In this section, a brief description about various
methods for crack classification and detection is
presented. Until now, various studies and researches have
been done in this field with varying degree of success.
Yong et al. (2010) proposed a similar method in
classification of cracks using six GLCM features and
classifying them by SVM. 
Mohamed and Stephen (1993) used parameters of
gray level histograms and classified them into cracks and
non-cracks using Multi Linear Feed forward neural
network. Mustaffar et al. (2008) proposed a different
approach in classification of cracks by using 3D-Digital
Elevation Model (DEM) parameters.
Many other methods have been proposed such as
wavelet transform by Zhou et al. (2005) and fuzzy set
theory (Cheng et al., 2001).
In this study, we use GLCM features for
classification purpose. And these results are compared
using different performance metrics.
MATERIALS AND METHODS
Pavement crack and non-crack images were collected
from different online resources in which 70% of images
were used for training and remaining 30% for testing
purposes. The proposed model is validated and results are
discussed.
Figure 1 represents the flow diagram which describes
the method of work flow in this study.
Image acquisition: Images which are used in the study
are acquired from various search engines randomly,
depending on the requirements of the classifiers. Images
are obtained such that it can be used to train the classifiers
in an efficient manner.
Res. J. Appl. Sci. Eng. Technol., 4(24): 5424-5437, 2012
5435
Image acquisition 
Image pre-processing 
Feature extraction 
       Classifiers 
(NN, SVM, MLR) 
Image without 
       cracks 
Image with cracks 
Horizontal 
   cracks 
Vertical 
 cracks 
Fig. 1: Proposed work flow
Image pre-processing: The obtained images are pre-
processed and made feasible such that it can be provided
as input to the classifiers. In this phase, images are
processed to get rid of noise and other unnecessary
information and then fed into the feature extractor.
Feature extraction: Gray Level Co-Occurrence matrix
technique is based on manipulating the gray levels of the
images. More number of gray levels provide accurate
analysis but at the cost of computational speed. GLCM is
a square matrix to which the given image is reduced. It
can be defined as: “a two dimensional histogram of gray
levels for a pair of pixels, which are separated by a fixed
spatial relationship.” Renzetti and Zortea (2011). Using
this GLCM matrix, 22 features are calculated. And these
features are as follows: 
C Autocorrelation
C Contrast
C Correlation Matlab
C Correlation 
C Cluster prominence
C Cluster shade
C Dissimilarity
C Energy
C Entropy
C Homogeneity Matlab
C Homogeneity 
C Maximum probability
C Sum of squares variance
C Sum average
C Sum variance
C Sum entropy
C Difference variance
C Difference entropy
C Information measure of correlation1
C Information measure of correlation2
C Inverse difference normalized
C Inverse difference moment normalized 
Classifiers:
Neural networks: Neural network comprises of elements
working in parallel.Learning process can be majorly
classified into three categories: Supervised learning,
Unsupervised learning and Reinforcement learning.
Supervised learning technique is used for pattern
recognition and for regression. In Unsupervised learning,
the weights are learned and the biases are changed based
on the response to network inputs only, as no target
outputs are available. Reinforcement learning technique
is a process in which the data is not provided and it is
obtained due to the interaction with the environment. In
this study supervised learning is used and its training
algorithm is BPN (Back Propagation Neural Network).
Linear classifier: The Multiple Regression Analysis
(MRA), analyzes the relationship between metric
dependent variables and metric independent variable.
Information obtained from the relationship improves the
accuracy in predicting values for the dependent variable.
There are three types of multiple regression analyzers,
Standard multiple, hierarchical and statistical. Standard
regression evaluates the relationships between a particular
set of dependent variables and independent variables.
Hierarchical regression performs the same function of
standard regression, but by controlling the effects of some
other independent variables on the dependent variable.
Statistical regression identifies the subset of independent
variables that has the strongest relationship to a dependent
variable. Y = "+$1 X1+$2 X2+$nXn+e where, Y is the
dependent variable; " is the Y-intercept; $1, $2 and $n, are
the slopes associated with X1, X2 and Xn, respectively; X1,
X2 . . . , Xn are the values of independent variables; e is
the error (Yildirim and Gunaydin, 2010).
Support vector machines: Support vector machine
concept is used to analyse and recognise datasets for
classification and pattern matching. Dataset is provided as
input to the SVM, these data are then classified into a
certain group which have similar characteristic features.
SVM comes under the category of kernel method, in this
method data are taken as dot products. The dot product
can be replaced by a kernel function which computes a
dot product in some possibly high dimensional feature
space. SVM has certain advantages namely, generates
non-linear decision boundaries using methods designed
for linear classifiers. The use of kernel functions allows to
apply a classifier to datasets which have no fixed-
dimensional vector space representation. SVM has to be
properly trained, as the decision it has to make relies
completely on the training. Training includes how to pre-
Res. J. Appl. Sci. Eng. Technol., 4(24): 5424-5437, 2012
5436
0
10
20
30
40
50
60
70
80
90
100
Accuracy Specificity Sensitivity 
BPN 
SVM
MLR
process the data, what kernel to use and also setting the
parameters of the SVM and the kernel. Improper choices
may lead to severely reduced performance. Gaussian
kernel, with a single parameter ( is opted. Combinations
of parameter choices are checked using cross validation,
the ones with highest accuracy are chosen. Finally, the
parameters with highest accuracy are used for training
purpose. The trained model is used for testing and
classification of the data set (Ben-Hur and Jason, 2007).
 
EXPERIMENT AND RESULTS
In this algorithm, data sets collected from various
web resources are fed into the classifier and its results are
obtained.
This is followed with detection of cracks from the
classified crack images. Confusion matrix, which is a
specific table layout, tabulated for a supervised learning
method provides visualization of the performance of an
algorithm. Using this matrix, the performance metrics like
accuracy, specificity and sensitivity of the different
classifiers are calculated using the following formulas:
Accuracy = (TP+TN)/ (TP+FP+FN+TN)
Specificity = TN/ (TN+FP)
Sensitivity = TP/ (TP+FN)
where, 
TP : Number of true positives
TN : Number of true negatives
FP : Number of false positives
FN : Number of false negatives
 
Here, Table 1-3 represents the Confusion Matrix of
BPN,  SVM  and  MLR  classifiers,   respectively.  And
Table 4 represents the consolidated result. Figure 2 
Table 1: Confusion matrix: BPN 
Crack Non-crack
Crack 5 (TP) 1 (FN)
Non-crack 0 (FP) 3 (TN)
Table 2: Confusion matrix: SVM
Crack Non-crack
Crack 6 (TP) 0 (FN)
Non-crack 0 (FP) 3 (TN)
Table 3: Confusion matrix: MLR
Crack Non-crack
Crack 4 (TP) 2 (FN)
Non-crack 0 (FP) 3 (TN)
Table 4: Consolidated result
BPN SVM MLR
Accuracy 88.9 100 77.8
Specificity 100.0 100 100.0
Sensitivity 83.3 100 66.7
 
Fig. 2: Comparison graph 
represents a comparison graph of the output of the three
classifiers with respect to performance metrics.
CONCLUSION
The Sample images are fed into the classifiers which
classify the samples into crack and non crack images.
Now the non crack images need not be fed to the detectors
for detection of cracks. These detectors precisely detect
the location of cracks (Santhi et al., 2012). By doing so,
the process time of the detector is fairly reduced. The
performance metrics indicate that the sensitivity and the
accuracy of SVM and BPN are higher than that of MLR.
SVM and BPN perform far better in classification of
cracks when compared to MLR for the given data set.
SVM classified 100% of the images precisely and
BPN classified 90% of the images precisely. But, MLR
managed to classify only 78% of the images precisely.
This makes SVM and BPN more suitable for crack
classification. 
In future, this study can be further improved for
classifying random multiline cracks. Classification of
images and detection of the precise location of cracks in
the image can be clubbed, making it highly useful for
industrial applications (leather, metal sheets, etc…).
These can be used for distress detection in pavements and
buildings also.
REFERENCES
Ben-Hur, A. and W. Jason, 2007. A User’s Guide to
Support Vector Machines.
Cheng, H.D., J.L. Wang, C. Clazier, X.J. Shi and X.W.
Chen, 2001. Novel approach to pavement cracking
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Mohamed, S.K. and G.R. Stephen, 1993.A neural
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Mustaffar, M., T.C. Ling and O.C. Puan, 2008.
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Renzetti, F.R. and L. Zortea, 2011. Use of a gray level co-
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Pavement Crack Classifiers: A Comparative Study

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