Welding Defects Detection in Radiographic Images Using an Improved Denoising Technique Combined with an Enhanced Chan-Vese Model

Abstract:

The detection of welding defects is becoming an important operation in the industry and the field of non-destructive testing. Among the most used techniques in the detection of weld defects, it is radiography. The radiographic images acquired are generally of low contrast, poor quality, and uneven lighting. Therefore, the detection of welding defects becomes a difficult task. In this work, a new hybrid approach based on the combination of several techniques is proposed. It consists of three stages: firstly, we define the region of interest (ROI). Secondly, a preprocessing operation based on an improved version of denoising by soft thresholding of wavelet coefficients and an optimized threshold is applied to improve the image quality (noise reduction, contrast enhancement). Thirdly, an enhanced Chan-Vese model is proposed to segment the denoised ROI region. This enhanced model is based on the choice of a cluster obtained by the Fuzzy C-Mean algorithm (FCM) as the initial contour. The proposed approach is applied to the various radiographic welding images from the GDxray database to extract the characteristics of the welding defects. The results obtained clearly show the effectiveness of the proposed approach compared to conventional techniques.

• Periodical: International Journal of Engineering Research in Africa (Volume 60)
• Pages: 155-172
• DOI: https://doi.org/10.4028/p-w863h3
• Online since: May 2022
• Authors: Rabah Abdelkader*, Naim Ramou, Mohammed Khorchef
• Keywords: Chan-Vese Model, Fuzzy C-Means Clustering, Image Denoise, Radiographic Image, Thresholding, Wavelet, Welding Defects

References

[1] A.Suaila, I. M. Idris, M.Yupiter, Weld defect Features Extraction on Digital Radiographic Image using Chan-Vese Model, IEEE 9th International Colloquim on signal and its Application, Kuala Lumpur, Malaysia, (2013) 67-72.

DOI: 10.1109/cspa.2013.6530016

[2] M.Sundaram, J.Prabin, G.Jaffino, Weld in Defects Extraction for radiographic images using C-means segmentation method, IEEE International conference on communication and network technologies, Sivakasi, India, (2014) 79-83.

DOI: 10.1109/cnt.2014.7062729

[3] N. Chetih, N. Ramou, Z .Messali A.Serir, Y. Boutiche, Micrographic image segmentation using level set model based on possibilistic C-Means clustering, IEEE European conference on electrical engineering and computer science, Bern, Switzerland, (2017) 188-192.

DOI: 10.1109/eecs.2017.43

[4] Y. Boutiche, Local segmentation via an implicit region-based deformable model applied to weld defects extraction, International Journal of Computer and Information Technology. 2(4) (2013) 815-820.

[5] N. Ramou, Segmentation of Weld Defects Using Multiphase Level Set by the Piecewise-Smooth Mumford-Shah Model, Russian Journal of Nondestructive Testing. 55(2) (2019) 155–161.

DOI: 10.1134/s1061830919020074

[6] E Yahaghi, The detection of weld defect images using shape-from-shading and wavelet denoising methods, Insight-Non-Destructive Testing and Condition Monitoring. 56(6) (2014) 308-311.

DOI: 10.1784/insi.2014.56.6.308

[7] E. Yahaghi, M. Mirzapour, A. Movafeghi, B. Rokrok, Interlaced bilateral filtering and wavelet thresholding for flaw detection in the radiography of weldments, Eur. Phys. J. Plus 135, 42 (2020).

DOI: 10.1140/epjp/s13360-020-00119-y

[8] H. Jiang, R. Wang, Z. Gao, J. Gao, H. Wang, Classification of weld defects based on the analytical hierarchy process and Dempster–Shafer evidence theory. J Intell Manuf 30 (4) (2019) 2013–(2024).

DOI: 10.1007/s10845-017-1369-4

[9] H. Zhu, W. Ge, Z Liu, Deep Learning-Based Classification of Weld Surface Defects, Appl. Sci., 9 (16) (2019) 3312.

[10] J. Zapata, R. Vilar, R. Ruiz, Performance evaluation of an automatic inspection system of weld defects in radiographic images based on neuro-classifiers, Expert Syst. Appl. 38(7) (2011) 8812–8824.

DOI: 10.1016/j.eswa.2011.01.092

[11] J. Kumar, R.S. Anand, S.P. Srivastava, Flaws classification using ANN for radiographic weld images, IEEE International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India, (2014)145-150.

DOI: 10.1109/spin.2014.6776938

[12] R. Vilar, J. Zapata, R. Ruiz, An automatic system of classification of weld defects in radiographic images, NDT E International. 42(5) (2009) 467–476.

DOI: 10.1016/j.ndteint.2009.02.004

[13] M.Barstugan, Y.S. Ceran, M. Yilmaz and N.A. Dundar, Detection of Defects on Single-Bead Welding by Machine Learning Methods, IOP Conference Series: Materials Science and Engineering 895(1) (2020).

DOI: 10.1088/1757-899x/895/1/012012

[14] W. Khalifa, O. Abouelatta, E. Gadelmawla, I. Elewa, Classification of Welding Defects Using Gray Level Histogram Techniques via Neural Network, Mansoura Engineering Journal. 39(4) (2014) 1-13.

DOI: 10.21608/bfemu.2020.102839

[15] D. Mery, M. A. Berti, Automatic detection of welding defects using texture features, Insight-Non-Destructive Testing and Condition Monitoring 45(10) (2003) 676-681.

DOI: 10.1784/insi.45.10.676.52952

[16] D. Mery, R.R. da Silva, L.P. Calôba, and J. Rebello, Pattern recognition in the automatic inspection of aluminum castings, In Insight-Non-Destructive Testing and Condition Monitoring, 45 (7) (2003) 475-483.

DOI: 10.1784/insi.45.7.475.54452

[17] J. Fang, K. Wang, Weld Pool Image Segmentation of Hump Formation Based on Fuzzy C-Means and Chan-Vese Model, J. of Materi Eng and Perform. 28 (2019) 4467–4476.

DOI: 10.1007/s11665-019-04168-y

[18] H. Pan, W. Liu, L. Li, G. Zhou, A novel level set approach for image segmentation with landmark constraints, Optik. 182 (2019) 257–268.

DOI: 10.1016/j.ijleo.2019.01.009

[19] T. Chan, L. Vese, An Active Contour Model without Edges, IEEE Trans. Image Processing. 10 (2) (2001) 266-277.

DOI: 10.1109/83.902291

[20] L.A. Vese, T.F. Chan, A Multiphase Level Set Framework for Image Segmentation Using the Mumford–Shah Model, International Journal of Computer Vision 50(3) (2002) 271-293.

[21] D. Mumford, J. Shah, Optimal approximations by piecewise smooth functions and associated variational problems, Communications on Pure and Applied Mathematics. 42(5) (1989) 577-685.

DOI: 10.1002/cpa.3160420503

[22] G. Chang, B. Yu, and M. Vetterli, Adaptive wavelet thresholding for image denoising and compression, IEEE Trans. Image Process. 9 (9) (2000) 1532–1546.

DOI: 10.1109/83.862633

[23] J. C. Dunn, A Fuzzy Relative of the ISODATA Process and its Use in Detecting Compact Well-Separated Clusters, Journal of Cybernetics 3(3) (1973) 32-57.

DOI: 10.1080/01969727308546046

[24] J.C. Bezdek, Objective function clustering, in Pattern recognition with fuzzy objective function algorithms, Springer, Boston, MA, 1981, pp.43-93.

DOI: 10.1007/978-1-4757-0450-1_3

[25] Information on https://domingomery.ing.puc.cl/material/gdxray/.

[26] N.D.H. Thanh, D. Sergey, V.B.S. Prasath, N.H. Hai, Blood vessels Segmentation method for retinal fundus images based on adaptive principal curvature and image derivative operators, International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, Moscow, Russia (2019).

DOI: 10.5194/isprs-archives-xlii-2-w12-211-2019

[27] G. Csurka, D. Larlus, F. Perronnin, and F. Meylan, What is a good evaluation measure for semantic segmentation?, BMVC, 27 (2013).

DOI: 10.5244/c.27.32