A NOVEL FEATURE EXTRACTION AND SELECTION

METHOD FOR STEEL SHEET DEFECTS CLASSIFICATION

Navid Rabbani, Mohammad Alamdari

Disdeh Pardaz Saba Co., Isfahan Science and Technology Town, Isfahan, Iran

Mohammad Rohollah Yazdani

Science & Research Branch, Islamic Azad University (IAU), Tehran, Iran

Farhad Imanpour

Cold-Rolling Mill II, Isfahan’s Mobarekeh Steel Co., Isfahan, Iran

Keywords: Steel Sheet Defects, Feature Extraction, Feature Selection, SFFS, Computational Complexity, SVM.

Abstract: This paper presents a novel approach for detection and classification of steel sheet defects. A Defects

database with enough samples and good imaging conditions introduced. A set of new features proposed to

extract the appropriate textural characteristics from defects images. This is followed by the selection of

important features using SFFS algorithm. Modifications to SFFS feature selection method were presented to

achieve the real-time needs of research. The proposed scheme decrease computational complexity in cost of

little decreasing of classification accuracy.

1 INTRODUCTION

In this paper we propose an algorithm utilizing novel

feature extraction and selection method with an

SVM classifier to detect, recognize and classify the

surface defects of steel strip.

Most of researches in this field suffer from lack

of acceptable steel images with good imaging

conditions and improper consideration for high-

speed production lines ((Guha, 2001), (Swaroop,

2000)) To respond these needs, a system with the

aim of imaging and archiving cold rolled steel sheets

was implanted in Isfahan's Mobarakeh Steel

Company.

2 STRUCTURE OF

IMPLEMENTED SYSTEM

Our scheme, which was implemented in Isfahan's

Mobarakeh Steel Company, was based on these

main subdivisions:

1. Imaging: imaging system with advanced line

scan camera and illumination unit provides 400

micrometer resolution.

2. Pre-processing and image enhancement.

3. Defect Detection and classification: the pre-

processed images have been segmented to non-

overlapping cells with 100 pixels by 100 pixels

dimension. A feature vector has been extracted

from each cell, which passed to classifier block

for labeling the cell with a specific defect.

4. Labels revision and extra information extraction:

the probability of a specific defect occurrence

depends on the position of cell within the sheet

and also the occurrence of defects in neighbor

cells. Regarding to these facts, in this stage, the

labels of defected cells should be revised and

extra information like distance between periodic

defects, could be extracted.

The main issue in this paper is describing the third

stage, which is the most significant procedure in the

processing algorithm.

3 DEFECTS DATABASE

To acheive an acceptable database, implemented

system in Isfahan's Mobarakeh Steel Company,

250

Rabbani N., Alamdari M., Yazdani M. and Imanpour F. (2009).

A NOVEL FEATURE EXTRACTION AND SELECTION METHOD FOR STEEL SHEET DEFECTS CLASSIFICATION .

In Proceedings of the Fourth International Conference on Computer Vision Theory and Applications, pages 250-253

DOI: 10.5220/0001784702500253

 SciTePress

archives thousands kilometers of steel sheet images.

A collection of defects have been collected from

archived images with supervision of quality control

technicians. The database with 2958 samples from 6

defects gathered so far and it’s still growing.

In table 1 the frequency of samples in different

classes has been reported.

Table 1: The frequency of samples in different defects

classes.

TotalTest TrainDefect Name

224110 114Punture

251127 124Roll mark

568283 285Scratch

843420 423Pressure line

5126 25Luder bounds

1021510 511No-Defection

29581476 1482Total

4 FEATURE EXTRACTION

Appropriate to visual look and the texture content of

defects, multiple types of features have been

developed. These features described, here:

 Histogram based Features:

A number of defects can be discovered by means

of image histogram. To extract some features

from histogram, there are two approaches. In

first approach, histogram can be modeled by its

statistics, like central moments of different

degrees. In second approach, the histogram can

be quantized to fewer levels and there is no need

to have uniform quantization level. Here, mean

and variance, from former group and eight

features from later one have been selected. The

later features, represents number of pixels with

grayscale value among ranges [0,50], [51,71],

[72,92], [93,113], [114,134], [135,155],

[156,176], [177,255]. These ranges have been

selected wisely to have most discriminative

features over different defects.

 Morphology based Features:

To discover the vertical and horizontal edges,

that made by defects, nonlinear gradients can be

employed. In this paper, morphological operators

have been utilized to extract features, measuring

quantitatively presence of vertical and horizontal

edges in image. To reach this purpose,

morphological operator of

)()(

SEimSEim

⊗

−⊕

derived with two horizontal and vertical

structuring elements. In each case the number of

pixels in gradient image greater than an adaptive

threshold calculated to represent features.

 Linear Gradient based Features:

To extract the edges, linear filters can be used

instead of morphological operators. Here,

vertical and horizontal Prewitt masks utilized to

find the horizontal and vertical edges. Similar to

morphological features, the number of pixels in

gradient image greater than an adaptive

threshold calculated to represent features.

 Adaptive Thresholding based Features:

If grayscale values of image’s pixels have the

mean of

, thresholds M

, M

have been

determined, where

. The number of

pixels with grayscale value greater than

and the ones with grayscale value less than

make two features. To determine the values of

coefficients

and

, pattern search algorithm

used to maximize the Mahalanobis distance

between feature vectors of defects images. (A

slight number of database images used in this

stage, have been omitted from training and test

sets). The optimal result is

92.0

and

11.1

 Quadratic Surface Modeling Features (Guha,

2001):

To identify defects like indents, Quadratic

Surface Modeling can be utilized. A grayscale

image can be assumed as a three dimensional

surface. This surface can be modeled with a

quadratic one.

Once the polynomial parameters are determined,

we get a fairly well idea of the surface profile

from them. The coefficients of the second order

terms

and

, determine the surface

curliness and can be used as features (Guha,

2001).

 Hu set of Invariant Moments:

The Hu set consist of a group of nonlinear

centralized moment expressions, which are

invariant under scale, position and rotation.

 Radon Transform based Features:

The radon transform computes projections of an

image along specified directions (Gonzalez and

Woods, 2008). Lines with specified direction in

original image make maximums (or minimums)

in radon transform space. By mean of calculating

transform in various directions from 0 to 180

degrees, we are able to find line shaped defects

in specified directions. The features selected as

direction and location of maximum in transform

space.

 Hough Transform based Features:

A NOVEL FEATURE EXTRACTION AND SELECTION METHOD FOR STEEL SHEET DEFECTS

CLASSIFICATION

251

To discover line-shaped defects, the Hough

transform also can be used (Gonzalez and

Woods, 2008).

To extract features from Hough transform, first,

edges detected from the image, then each point

of edges maps to a curve in Hough space, all the

curves in Hough space sum together then

maximum point determined, and

,r of

maximum point makes the features.

We can summarize all the features previously

described:

1, 2. mean and variance

3, 4. number of pixels with greater than 25% in

grayscale value after employing horizontal and

vertical Perwiet masks.

5, 6. mean and variance for pixels with grayscale

value less than threshold calculated with Otsu's

method, if threshold effectiveness is more than

65%, else we use mean and variance of all pixels

7, 8. number of pixels greater than 1.11 times of

pixel’s mean and less than 0.92 times of pixel’s

mean.

9, 10, 11, 12. mean and number of pixels with

grayscale value greater than 1.8 times of pixel’s

mean after performing described morphological

operation.

13, 14. the

of maximum point in Hough

transform space.

15, 16. number of edge pixels after employing

edge detection method based on zero-crossing

17, 23. Hu set of invariant moments

24, 25. The coefficients of the second order

terms (

and

), after utilizing quadratic

surface modeling.

26, 33. Requantized nonlinear histogram

34, 35. Direction and location of maximum point

in radon transform space.

5 FEATURE SELECTION

The main goal of feature selection is to select a

subset of d features from given set of D

measurements,

Dd <

without significantly

degrading (or possibly even improving due to the

“peaking phenomena” (Pudil et al., 1994)) the

performance of recognition system. We have been

used Sequential Forward Floating Search (SFFS),

which provides close to optimal solution at an

affordable computational cost ((Pudil et al., 1994),

(Jain and Zongker, 1997)).

5.1 Sequential Forward Floating

Search Algorithm

SFFS algorithm, start with an empty subset and

iteratively add or remove features, trying to

maximize a criterion function, until some

termination condition is met (Jain and Zongker,

1997).

The main issue remains is selecting criterion

function. In most applications, the feature’s

interclass distance (i.e. Mahalanobis distance) being

used as criterion function. It can also be selected as

accuracy of a trained classifier with inspecting

subset of features.

5.2 Criterion Function based on

Computational Cost and Accuracy

In real time application, like automatic surface

inspection, the computational cost is as important as

accuracy. So it’s more realistic to have a criterion

function, which depends on both computational cost

and accuracy. The computational cost consists the

normalized time needed to calculate the inspecting

subset of features.

If total time needed to compute the features of

subset

(

)

and the accuracy of classifier

trained with features of subset

()

, the

criterion,

J , can be assumed as:

() ()

()

XAXJ

′

βα

(1)

where

′

are normalized values of

and

coefficients

−

1, are selected upon to the

relative importance of accuracy and time. It’s

obvious that accuracy criterion is a special case of

accuracy-computational complexity with

6 EXPERIMENTAL RESULTS

In this section, we demonstrate the effectiveness of

the proposed methodology compared with traditional

methods. To test the described algorithms, the

defects database divided into training and testing

sets. All the purposed features calculated for whole

images in database. In other hand, the mean time for

calculating each feature measured to use in feature

selection procedure as computational complexity.

In feature selection stage, three types of criterion

function were compared:

VISAPP 2009 - International Conference on Computer Vision Theory and Applications

252

Table 2: Comparing classification results for various feature selection methods.

Classification

Accuracy

Computational

complexity

Optimal subset No. of Features

in optimal subset

Criterion function used by

SFFS Algorithm

89.92% 10432.7 1 .. 35 35 Without feature selection

90.12% 9468.2 13,34,30,31,14,32,29,24,10,33

16,4,11,8,2,3,27,1,21,23,22,18

22 Mahalanobis distance

93.10% 8607 13,9,10,35,1,8,34,16,28,5 10 SVM classifier Accuracy

85.36% 1021 12,11,33,18,24 5 Accuracy-Computational

complexity with

5.0=

83.98% 922 12,11,33,18 4 Accuracy-Computational

complexity with

4.0=

87.84% 1116 12,11,33,18,24,25,32,3 8 Accuracy-Computational

complexity with

6.0=

a) Mahalanobis distance: in each SFFS iteration,

Mahalanobis distance can be calculated simply

from database samples for selected subset.

b) Classifier accuracy: In each iteration, a feature

subset is selected by SFFS algorithm, then

classifier have been trained upon to selected

subset and training database. The classification

accuracy have been calculated using trained

classifier over testing database and it has been

used as criterion value of SFFS for next iteration.

c) Classifier accuracy-computational complexity:

Similar to former routine, we can obtain

accuracy-computational complexity criterion,

using eq. (1)

Several experiments were accomplished for each

suggested criterion functions. SVM used as classifier

with Gaussian kernel and

. In each

experiment, the optimal subset has been determined

by SFFS procedure. The optimal subsets, accuracy

and computational complexity were shown in table

2, where it can be seen that feature selection using

accuracy-computational complexity criterion

outperforms convectional criterions like,

Mahalanobis distance. Also it’s clear that by

regulating

, we can attain desired classification

accuracy in cost of computational complexity

increasing and vice versa.

7 CONCLUSIONS

We have presented a scheme for detection and

classification of steel sheet defects. A set of new

features proposed to extract the appropriate

textural

characteristics

from defects images. Feature

selection methods utilized to select outperformed

features, modifications to SFFS feature selection

method were presented to achieve the real-time

needs of research. We can decrease computational

complexity in cost of little decreasing of

classification accuracy

ACKNOWLEDGEMENTS

The authors would like to thank all the experts of

Dideh Pardaz Saba Co. and Isfahan’s Mobarekeh

Steel Co., who have helped us during this research.

In particular, we wish to thank K. Dalvi and M.

Faghih-Imani for their particular efforts.

REFERENCES

Guha, P., 2001, Automated visual inspection of steel

surface, texture segmentation and development of a

perceptual similarity measure, Indian institute of

Technology, Kanpur, Master's Thesis.

Swaroop K Chalasani, 2000, Segmentation and

Performance Evaluation of Steel Defect Images,

Indian Institute of Technology, Kanpur, Master's

Thesis.

Gonzalez R C, Woods R E, 2008, Digital Image

Processing, Prentice Hall, 3

Edition.

Pudil P, Novovicova J, Kittler J, 1994, Floating Search

Methods in Feature Selection, In Pattern Recognition

Letters, Vol. 15, pp. 1119-1125.

Jain A, Zongker D, 1997, Feature Selection: Evaluation,

Application, and Small Sample Performance, In IEEE

Transactions on Pattern Analysis and Machine

Intelligence, Vol. 19, No. 2.

A NOVEL FEATURE EXTRACTION AND SELECTION METHOD FOR STEEL SHEET DEFECTS

CLASSIFICATION

253