Multi - Class Welding Flaws Classification using Texture Feature for Radiographic Images Jayendra Kumar, R.S. Anand, S.P. Srivastava Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, India
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[email protected] Abstract—The paper presents a novel approach for multiclass weld flaw classification by means of Gray level cooccurrence matrix (GLCM) based texture feature extraction technique and Artificial Neural Network classifier. The weld radiography films have been digitized using CCD camera, followed by image processing techniques i.e. RGB to gray conversion, region of interest (ROI) selection, noise reduction and contrast enhancement. Subsequently a set of 8, 64 and 44 texture features vectors have been obtained from each of the digitized weld images by means of GLCM. Further, the features obtained have been classified with cascade-forward back propagation neural network. The proposed system has obtained overall classification accuracy of 86.10% for nine different types of weld flaws of digitized radiographic images.
classify the 6 different types of weld defects of radiographic images with classification accuracy of 80.00% using ANN. Researcher[4] have proposed a study on multi- class pattern classification and compared six different neural network system architectures. Zapata et al. proposed a methodology for the extraction of 12 geometrical features from radiographic weld images, but have used eccentricity, orientation, equivalent diameter and solidity features to give input to the classifier. The classification accuracy of 78.9% and 82.6 % have been reported using ANN and ANFIS, respectively by using above mentioned four features [5]. Further, they have proposed the use of ANN classifier with diverse architectures for the input and hidden layer, to raise the classification accuracy to 80.0% for the same feature sets [6].
Keywords—GLCM; multi-class classification; weld flaws; texture feature; neural network; radiographic images;
An approach for automatic detection of weld flaws in radiographic images and classification of the same with the inclusion of PCA in the input layer of ANN has proposed by Mirapeix et al.[7]. In 2010, Valavanis et al.[8] have proposed a SVM and ANN based technique for multiclass weld flaw detection and classification, by means of geometrical and texture features of the radiographic images in consideration. The overall classification accuracy of 85.0% has been reported by them. In 2012, Hassan et al.[9], have used feed forward back propagation algorithm to classify only four weld defects using geometrical features, followed by segmentation with classification accuracy of 86.0%.
I.
INTRODUCTION
Welding is considered to be the backbone of many fast growing sectors such as nuclear reactors, shipping industry, chemical plants and aeronautics. The detection of weld flaws is a vital task for non-destructive testing of welded material. For this purpose radiographic testing is the most commonly preferred technique, as it generates a physical record of inspection in the form of radiographic film. Radiography is a well established technique, which looks out for the defects if any, present inside the material. The efficiency of this process depends on the operating skills of the technicians. Machine vision technology has to play a vital role in the development of Automatic identification and classification of radiographic images of weld. The research for the development of an automatic or semi automatic system of weld flaws (defects) classification using radiographic images of weld joints has grown considerably in the last 10 years. An image processing approach has been used by D. Mery et al.[1], for automatic detection of welding defects from digitized radiographic films based on two types of widely known texture features extraction techniques, co-occurrence matrices and 2D Gabor functions. A study of nonlinear classifier using Artificial Neural Network (ANN) was implemented by Silva et al.[2], concluded that the quality of the extracted features are more important than the quantity of the features. Further, in the year 2005, Da Silva et al.[3] have proposed a methodology to estimate the classification accuracy of nonlinear classifier to
Till date, various researchers have worked on seven different types of flaws using radiographic images of weld with different set of descriptors of texture feature, geometrical measurement and classification methods. In this paper, author aims to present an approach to develop a methodology to classify nine different flaws of radiographic weld images that has not been considered by the researchers so far. The inclusion of moderate number of descriptor of feature extracted from the weld images using GLCM and selection of optimum quantity of neurons in the hidden layer of ANN has used to increase the classification accuracy of nine different weld flaws. The article is structured as follows: the proposed methodology have been discussed in Section II, the implementation aspect of the weld image flaws classification is presented in section III, further the results and discussion of the experimentation are being considered in section IV, and finally results are concluded in section V.
II.
METHODOLOGY
The block diagram of the proposed methodology for the classification of radiographic images of weld flaws is as shown in Fig.1. It consist of digitization of radiographic film, RGB to gray scale conversion, pre-processing stage, GLCM based feature extraction and ANN as classifier. Further, each of the blocks are discussed in detail as follows:
Fig. 1. Block diagram of radiographic weld flaw classification system
A. Digitization of Radiographic film: After obtaining the radiographic film, the first stage is to digitize the film for further processing. The most common way of digitization of radiographic film is to scan it using high optical density scanner[6], but in this work authors have used Nikon, 14 megapixel digital camera and white glow board (generally used by doctor to read X-ray) for digitization of film. The radiographic film of weld images are positioned on top of intensity controlled glow board; rest of the part of box is covered by black paper to reduce the non uniform illumination effect. Then a digital image of the film is capture through CCD camera [10]. Further the images are cropped in order to select the ROI of weld portion for additional processing. The images obtained thus have size of 2500 x 500 pixels with a resolution of 300 dpi.
D. Feature extraction After pre-processing the digitized radiographic images, the features of these images are extracted for further processing. The feature extraction technique represents a huge reduction of image size without changing the nature of the image for a particular application. The output of this stage is a descriptor for each of the weld defects in the radiographic images. In this work, the authors have used different set of texture feature descriptors based on GLCM approach for different experiment to improve the overall classification accuracy, as well as individual classification accuracy of different weld flaws. E. Gray Level Co occurrence Matrices (GLCM) : Gray level co-occurrence matrix is a common statistical texture analysis method in which texture features are extracted by means of statistical approaches from the co-occurrence matrix[13].Texture descriptors based on GLCM are an wellorganized ways to study the texture of an image. The GLCM used in the present study is used for a series of second order statistics calculations. These second order statistics are calculated for all pair wise combinations of grey levels. In the present work twenty two texture feature based on study of Clausi, Soh and Haralick [11, 12, 13] have been used. These features are energy, entropy, dissimilarity, contrast, inverse difference, correlation, homogeneity, autocorrelation, cluster shade, cluster prominence, maximum probability, sum of square, sum average, sum variance, sum entropy, difference variance, difference entropy, information measurement of correlation(1), information measurement of correlation(2), maximum correlation coefficient, inverse difference normalized and inverse difference moment normalized. F. Classification using ANN: A neural network is made of simple processing units called neuron. In the proposed scheme, the classification method is a feature matching method for which ANNs are typically used. One of the most universal and frequently used ANN learning algorithms is the back propagation algorithm which is based on the error energy minimization. III.
IMPLEMENTATION ASPECTS
B. RGB to Gray Conversion: Since, the image are taken by digital camera so the digitized radiographic weld images are RGB image (24 bit), it should be converted to gray scale (8 bit image ) for further processing, in order to save the computation time to extract texture features.
A. Radiographic Image Database: Overall 79, radiographic weld images are considered for this experimentation. The original radiographic films of weld flaws have been collected from Welding Research Laboratory, Mechanical Engineering Department, Indian Institute of Technology, Roorkee, India. The distributions of different flaws in the database are shown in Fig.2 and digitized image of each flaws are in Fig.3.
C. Pre-processing stage: With a digital image, it is common practice to pre-process the image in order to shrink or remove the noise and enhance the contrast level of it. Radiographic film generally has noise and deficient contrast due to non-uniform illumination and limited range of intensities of the capturing device [5]. Two pre-processing steps are applied in this paper: first for dropping or eliminating noise by an adaptive 5 x 5 Wiener filter. Secondly, the contrast enhancement has been applied to adjust the contrast value to a specified range.
B. Processing Step: The radiographic weld flaw recognition process in weld radiography image provide a set of image that belongs to one of the following classes: gas cavity(GC), lack of penetration(LOP), porosity(PO), slag inclusion(SI), crack(CR), lack of fusion(LOF), worm hole(WH) undercut(UC) and non defect(ND). In order to differentiate each of the weld flaws a different set of descriptor of GLCM based texture features are extracted and then used as a input to ANN based multi-class classifier. Many researchers have used combination of
No. of samples
geometric and texture features for defect classsification. In case, if the geometrical features are considered, thhen it is of utmost important to segment the weld defect objeccts, which is time consuming. In the present work, authors havve concentrated on various descriptors of GLCM technique with different direction and spatial pixel distance, to increase the classification accuracy. 25 20 15 10 5 0 H UC ND GC LOP PO SI CR LOF WH Name of flaws Fig. 2. Distribution of different flaws in database
Gas cavity(GC)
Lack of penetration(LOP)
Porosity(PO)
Slag inclusion(SI)
Crack(CR)
Lack of fusion(LOF)
Worm hole(WH)
Undercut(UC)
Non defect(ND) Fig. 3. Digitized images of radiographic Weld W flaws
IV.
RESULTS AND DISCUSSION
Three experiments are beeing performed to classify the radiographic weld flaws into nine different categories. In all the cases the training, testing andd validation data ratio are 70%, 15% and 15% respectively. A. Experiment 1: In the first experiment, fourr texture feature of second order statistics (contrast, correlation, energy and homogeneity) [13] are extracted in two direction (0o and 180o) for spatial pixel distance d=2 (0,2; 2,0). So thhere are 4x2=8 texture feature descriptors extracted for eachh of the image The data-base utilized in the present study connsist of 79 digitized weld images with nine different types of o flaws. Based on various experimentation it is observedd that the cascade-forward back propagation neural network arrchitecture is most suitable for classification of flaws in the weld w images. So the target vector of size 9x79 and test vector of size s 79x8 are applied to cascadeforward back propagation neuural network architecture. The overall accuracy of classificatioon is 82.3% for one hidden layer with 146 neurons in that layer. The confusion matrix in Table-I shows that 87.5% of gas cavitty flaws are classified correctly whereas 12.5% are misclassiffied as crack. The accuracy of other flaws like LOP, SI, CR,, UC and ND are 75%, 87.5%, 90.9%, 66.7% and 80% resppectively. The lowest accuracy recorded for porosity type of flaws f is 57.1%. The accuracy of WH and LOF are recorded as 100% in this case.
Flaws GC LOP PO SI CR LOF WH UC ND
TABLE I.
CONFUSIION MATRIX FOR ANN-4X2
GC
LOP
PO
SI
CR
LOF
WH
UC
ND
87.5 0 0 0 12.5 0 0 0 0
0 75 0 20 0 0 0 5 0
0 0 57.1 42.9 0 0 0 0 0
0 6.2 0 877.5 0 0 6.2 0 0
0 0 9.1 0 90.9 0 0 0 0
0 0 0 0 0 100 0 0 0
0 0 0 0 0 0 100 0 0
0 33.3 0 0 0 0 0 66.7 0
0 0 0 20 0 0 0 0 80
Classifiication Accuracy %
B. Experiment 2: t same four texture parameter In the second experiment the of second order statistic are exttracted in four direction (0o, 45o, 90o, 135o) for four different spatial pixel distances d=1,2,3,4 thus sixteen features for eachh parameters are extracted with pixel value (0,1; 0,2; 0,3; 0,4; -1,1; -2,2; -3,3; -4,4; -1,0; -2,0; 3,0; -4,0; -1,-1; -2,-2; -3,-3; -4,-4). So there are 4x16=64 texture features descriptors which are extracted for each of the images. Again it is observed that the cascade-forward back propagation neural network arcchitecture is the most suitable for classification of flaws in the weld images with the foresaid features set. So the target vectoor of size 9x79 and test vector of size 79x64 are applied to neuraal network architecture. Here the overall accuracy of the classifiication is 86.1% for one hidden layer with 110 neuron in that layer. The confusion matrix for the same is shown in Table-II. The T diagonal value of confusion matrix indicates the individual classification rate of each flaws. In this set of feature classification rate of porosity is again low compared to other but higher thhan the previous one.
TABLE II.
CONFUSION MATRIX FOR ANN-4X16
V.
Classification Accuracy %
Flaws GC LOP PO SI CR LOF WH UC ND
GC
LOP
PO
SI
CR
LOF
WH
UC
ND
87.5 0 0 12.5 0 0 0 0 0
0 85 0 15 0 0 0 0 0
0 0 71.4 28.6 0 0 0 0 0
6.2 6.2 0 81.3 0 0 0 0 6.2
0 0 9.1 9.1 81.8 0 0 0 0
0 0 0 0 0 100 0 0 0
0 0 0 0 0 0 100 0 0
0 0 0 0 0 0 0 100 0
0 0 0 0 0 0 0 0 100
C. Experiment 3: In the third experiment twenty two texture features of second order statistic discussed in section-II are considered for feature extraction in two direction (0o and 180o) for spatial pixel distance d=2 (0,2; 2,0). Here, total of 22x2=44 texture feature descriptors are extracted from each of the image. Again it is observed that the cascade-forward back propagation neural network architecture is the most suitable for classification of flaws in the weld images with these features set. So the target vector of size 9x79 and test vector of size 79x44 are applied to above said neural network architecture. For 44x79 feature vector the maximum overall accuracy of classification are noted to same value as mention earlier i.e. 86.1% for one hidden layer with 116 number of neuron but the individual flaws classification accuracy are somewhat different. From the Table-III, it is clear that PO, LOF, WH and UC give 100% classification accuracy whereas GC, LOP, SI, CR and ND gives 62.5%,90%, 87.5%,72.7% and 80% respectively. In this set of feature classification accuracy of gas cavity is minimum. The Fig.4 below shows the individual classification accuracy of different flaws with different sets of feature vector. TABLE III.
This work was carried on digitized weld flaws images using radiographic film obtained from the Welding Research Lab, Department of Mechanical Engineering, IIT Roorkee, India, for the study of multiclass pattern classification using artificial neural network classifier with three different texture feature set having 8, 64, and 44 descriptors of texture features of 79 images containing nine types of flaws . From experimental observation it is concluded that the flaws class like LOF, WH, PO, UC presents high accuracy of classification with 44 feature set however the GC, LOP, SI and CR gives moderately low accuracy of classification in all the three feature sets, whereas, confusion between different classes are different in each experiment. The combined accuracy of classification using three different feature sets on same data sets are 82.3%, 86.1% and 86.1% respectively. From Fig.4, it is also clear that recognition rate 100% can be achieved with the GLCM based texture feature approach. REFERENCES [1]
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Classification Accuracy %
Flaws GC LOP PO SI CR LOF WH UC ND
100
classification accuracy(in %)
CONFUSION MATRIX FOR ANN-22X2
GC
LOP
PO
SI
CR
LOF
WH
UC
ND
62.5 12.5 0 0 0 12.5 0 0 12.5
0 90 0 5 0 0 0 5 0
0 0 100 0 0 0 0 0 0
0 12.5 0 87.5 0 0 0 0 0
0 27.3 0 0 72.7 0 0 0 0
0 0 0 0 0 100 0 0 0
0 0 0 0 0 0 100 0 0
0 0 0 0 0 0 0 100 0
0 0 20 0 0 0 0 0 80
exp.1.C A%
exp.2.C A%
exp.3.C A%
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GC LOP PO
SI
CR LOF WH UC
Name of weld flaws
ND
Fig. 4. Classification accuracy of different flaws in different experiments
CONCLUSION
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