COMPUTED RADIOGRAPHY VISION IN WELD TESTING CR-VISION-WT

Size: px

Start display at page:

Download "COMPUTED RADIOGRAPHY VISION IN WELD TESTING CR-VISION-WT"

Angela Debra Dickerson
6 years ago
Views:

ISSN 1310-3946 NDT days 2017 / Дни на безразрушителния контрол 2017 Year /Година XXV Number/ Брой 1 (216) June/Юни 2017 COMPUTED

dz, Research Center in Industrial Technologies, CRTI, Algeria Nadia Mhamda, e-mail: n.mhamda@crti.

dz, Research Center in Industrial Technologies, CRTI, Algeria Abstract: With an aim of gathering some works carried out during

conceived software which we have called Computed Radiography Vision in Weld Testing (CRVision-WT).

quantifying and identifying possible discontinuities and defects present in a welded joint.

in question according to international standards such as API 1104 and ASME Sect. V.

appropriate ones for the current processed radiographic image.

Introduction The interpretation of possible weld discontinuities in industrial radiography is ensured by human interpreters.

of the poor quality of radiographic images, the weak size of defect, etc., due essentially to the exposure conditions.

It is thus opportune to develop computer-aided techniques [1-14] to help the interpreter in evaluating the quality of the welded

1 a general configuration of a radiographic testing system used in the inspection of the welded joint.

1 ISSN NDT days 2017 / Дни на безразрушителния контрол 2017 Year /Година XXV Number/ Брой 1 (216) June/Юни 2017 COMPUTED RADIOGRAPHY VISION IN WELD TESTING CR-VISION-WT Nafaa Nacereddine, n.nacereddine@crti.dz, Research Center in Industrial Technologies, CRTI, Algeria Nadia Mhamda, n.mhamda@crti.dz, Research Center in Industrial Technologies, CRTI, Algeria Aicha Baya Goumeidane, a.goumeidane@crti.dz, Research Center in Industrial Technologies, CRTI, Algeria Abstract: With an aim of gathering some works carried out during several years within the Pattern Recognition team of Signal Processing and Imagery Division in our research center, we have conceived software which we have called Computed Radiography Vision in Weld Testing (CRVision-WT). This software is dedicated to the image processing and analysis in industrial radiography for the purposes of detecting, locating, quantifying and identifying possible discontinuities and defects present in a welded joint. The ultimate processing step in this software is related to decision-making about the acceptance or rejection of the discontinuity in question according to international standards such as API 1104 and ASME Sect. V. For a given processing stage, the software offers sometimes several routines in order to seek, in an interactive way, the more appropriate ones for the current processed radiographic image. Keywords: radiographic testing, weld defect, image processing and analysis, software. 1. Introduction The interpretation of possible weld discontinuities in industrial radiography is ensured by human interpreters. Consequently, it is submitted to subjective considerations such as the aptitude and the experiment of the interpreter, in addition of the poor quality of radiographic images, the weak size of defect, etc., due essentially to the exposure conditions. These considerations make sometimes the weld quality interpretation inconsistent, labor intensive and biased. It is thus opportune to develop computer-aided techniques [1-14] to help the interpreter in evaluating the quality of the welded joints. We present in the Fig. 1 a general configuration of a radiographic testing system used in the inspection of the welded joint. In the software proposed in this paper, a toolbar provide shortcuts to some processing operations (see Fig. 3). Fig. 1 General configuration of a welded joint inspection system using radiography Synoptic scheme of the proposed software is displayed in Fig. 2. Fig. 3 The software: Computed Radiography Vision in Weld Testing, CRVision-WT 2. Image operations In the first rubric, we present to the user various operations to handle the image to be processed. For example, load image, select the region of interest, display and save the resulted images (Figs. 4 and 5). Fig. 2 Global structure of the proposed software 392

Fig. 7 Example of median filter Fig. 4 Handle the image to process and the resulted images Fig.

6 proposes several preprocessing algorithms to enhance the quality of the image to be processed.

7) Contrast enhancement by image stretching, (Fig.

Fig. 9 Example of statistical enhancement 4.

From this operation, we try to delimitate the region of the object representing the discontinuity

This software offers several segmentation methods Binarization: Histogram 1D : Otsu, Kapur,

2 Fig. 7 Example of median filter Fig. 4 Handle the image to process and the resulted images Fig. 8 Image stretching Fig. 5 Select the region of interest 3. Preprocessing This rubric in Fig. 6 proposes several preprocessing algorithms to enhance the quality of the image to be processed. We can find: Noise removal using the filters (Fig. 7) Contrast enhancement by image stretching, (Fig. 8) Contrast enhancement by mathematical morphology, statistical and adaptive enhancement (Fig. 9). Fig. 9 Example of statistical enhancement 4. Segmentation The segmentation is the main step of image processing. From this operation, we try to delimitate the region of the object representing the discontinuity or the defct in the welded joint (Fig. 10). This software offers several segmentation methods Binarization: Histogram 1D : Otsu, Kapur, Kittler et Tsai, Histogram 2D : local, joint and relative entropies, Adaptive locally : Niblack and Sauvola (Fig. 11), Active contours: polygonal statistical (Fig. 12) et polygonal statistical with automatic ROI, Finite mixture models: Gaussian (Fig. 13) and generalized Gaussian distributions. Fig. 6 Preprocessing and image improvement 393

5. Post-processing The procedures in this rubric aim to improve the results

opening, closing, artefact removal, etc. (Fig. 15) Fig.

14 Segmentation without post-processing Fig.

12 Example of active contour-based segmentation Fig.

Description or feature extraction In this rubric (Fig.

3 5. Post-processing The procedures in this rubric aim to improve the results of segmentation in order to better fit the objet representing the weld defect. In post-processing (Fig. 14), we have: Median filter Morphological operators, dilation, erosion, opening, closing, artefact removal, etc. (Fig. 15) Fig. 10 Image segmentation toolbar Fig. 14 Segmentation without post-processing Fig. 11 Example of binarization with Sauvola s method Fig. 12 Example of active contour-based segmentation Fig. 15 Applying of morphological operators 6. Description or feature extraction In this rubric (Fig. 16), we compute the geometric descriptor (Fig. 17) and the generic Fourier descriptor (GFD) in order to describe the defect in terms of shape (elongation, rectangularity, symmetry, etc. These descriptors constitute the inputs of the weld defect classification stage. Fig. 13 Example of segmentation by a Gaussian mixture model 394

Fig. 19 Example of classification for the discontinuity in Fig. 17 Fig.

Diagnosis/Standards In this rubric, we apply the international standards dealing with

These international standards are based on criteria computed according to the

Here, API 1104 standard is used in the proposed software (Fig. 21).

Classification We have chosen four types of weld defects for this software: Crack (Cr),

4 Fig. 19 Example of classification for the discontinuity in Fig. 17 Fig. 16 Feature extraction toolbar 8. Diagnosis/Standards In this rubric, we apply the international standards dealing with radiographic testing in order to accept or reject a weld (Fig. 20). These international standards are based on criteria computed according to the dimensions of the extracted defect indications. Here, API 1104 standard is used in the proposed software (Fig. 21). The software application on the other standards (ASME Sect. V, API 5L, etc.) are under construction. Fig. 17 Example of image description using the shape geometric descriptor 7. Classification We have chosen four types of weld defects for this software: Crack (Cr), Lack of penetration (LP), Porosity (Po) and Solid inclusion (SI). In the rubric classification (Fig. 18, 19), our purpose is to classify the defect indication present the radiographic image, if it exists, into one of the cited defects using several classification and clustering techniques such as finite mixture model, support vector machine, artificial neural network and Bayesian networks. Fig. 20 Decision making according to international standards Fig. 18 Classification of the segmented image 395

5 Fig. 21 Example of applying of the standard API Conclusion The software CRVision-WT is an image processing tool dedicated to nondestructive testing of weld by radiography. From the input radiographic film image of a weld, the software apply incrementally all the processing stages involved in a machine vision system, namely, preprocessing, segmentation, feature extraction, classification, decision-making based on norms and standards governing NDT by radiography. 10. References 1. Nacereddine N., Weld defect detection and recognition in industrial radiography based image analysis and neuronal classifiers, Fourth International Conference on NDE in Relation to Structural Integrity for Nuclear and Pressurized Components, London, UK, 6-8 Dec Nacereddine N., M. Tridi, Computer-aided shape analysis and classification of weld defects in industrial radiography based invariant attributes and neural networks, Fourth IEEE International Symposium on Image and Signal Processing and Analysis, Zagreb, Croatia, Sept Nacereddine N., L. Hamami, D. Ziou, Thresholding Techniques and their Performance Evaluation for Weld Defect Detection in Radiographic Testing, Machine Graphics & Vision, ISSN: , vol. 15, no 3/4, pp , Nacereddine N., L. Hamami, D. Ziou, M. Tridi, Statistical tools for weld defect evaluation in radiographic testing, 9th European Conference in Non Destructive testing, ECNDT, Berlin, Sept Mekhalfa F., N. Nacereddine, A.B. Goumeidane, Unsupervised Algorithm for Radiographic image Segmentation based on the Gaussian Mixture Model, IEEE Conf. on Computer as a tool EUROCON 2007, Varsovie, Pologne, 9-12 Sept Goumeidane A.B., M. Khamadja, N. Nacereddine, F. Mekhalfa, Statistical Deformable Model Based Weld Defect Contour Estimation in Radiographic Inspection, IEEE International Conference on Computational Intelligence for Modelling, Control and Automation, CIMCA 08, Vienna, Austria, pp , Dec , Nacereddine N., S. Tabbone, D. Ziou, L. Hamami, L algorithme EM et le Modèle de Mélanges de Gaussiennes Généralisées pour la Segmentation d images. Application au contrôle des joints soudés par radiographie, Traitement et Analyse de l'information : Méthodes et Applications, TAIMA 09, Hammamet, Tunisie, pp , May Goumeidane A.B., M. Khamadja, N. Nacereddine, Bayesian Pressure Snake for Weld Defect Detection, in Proc. de Advanced Concepts for Intelligent Vision Systems ACIVS 09, Sep.28 Oct.2, 2009, Bordeaux, France. In Lecture Notes in Computer Science LNCS 5807, pp , Nacereddine N., S. Tabbone, D. Ziou, L. Hamami, Asymmetric Generalized Gaussian Mixture Models and EM algorithm for Image Segmentation, in Proc. of 20th Int. Conf. on Pattern Recognition ICPR 10, pp , Istanbul, Aug Nacereddine N., D. Ziou, Invariant shape features and Relevance Feedback for Weld Defect Image Retrieval, VIth International Workshop NDT in Progress, Prague, Oct Nacereddine N., D. Ziou, L. Hamami Fusion-based Shape Descriptor for Weld Defect Radiographic Image Retrieval, International Journal of Advanced Manufacturing Technology 68 (9-12), pp , Nacereddine N., D. Ziou, Hybrid Shape Descriptors for an Improved Weld Defect Retrieval in Radiographic Testing, 6th Int. Conf. on Image Processing and Communications, Bydgoszcz, Poland, Sep In Advances in Intelligent Systems and Computing AISC 313, Image Processing and Communications Challenges 6, pp , Mekhalfa F., N. Nacereddine, Multiclass Classification of Weld Defects in Radiographic Images Based on Support Vector Machines, 10th IEEE Intern. Conf. on Signal Image Technology & Internet Based Systems (SITIS 14), Marrakech, Morocco, Nov , Goumeidane A.B., A. Bouzaieni, N. Nacereddine, S. Tabbone, Bayesian Networks-Based Defects Classes Discrimination in Weld Radiographic Images, in Proc. of 16th Int. Conf. on Computer Analysis of Images and Patterns, CAIP 15, Sep , Valletta, Malta. In LNCS 9257, pp ,

A. Benali 1, H. Dermèche 2, E. Zigh1 1, 2 1 National Institute of Telecommunications and Information Technologies and Communications of Oran

Elimination of False Detections by Mathematical Morphology for a Semi-automatic Buildings Extraction of Multi Spectral Urban Very High Resolution IKONOS Images A. Benali 1, H. Dermèche 2, E. Zigh1 1, 2