Multiclass defect detection and classification in weld radiographic images using geometric and texture features

Expert Systems with Applications

Volumn 37, Issue 12, 2010, Pages 7606-7614

  Valavanis, Ioannis ^a   Kosmopoulos, Dimitrios ^b  

^a Zenon Automation Technologies   (Greece)

^b INSTITUTE OF INFORMATICS AND TELECOMMUNICATIONS   (Greece)

Author keywords

Classification; Defects; Geometrical features; Radiography; Segmentation; Texture; Welds

Indexed keywords

DEFECTS; GEOMETRY; IMAGE CLASSIFICATION; IMAGE SEGMENTATION; NEAREST NEIGHBOR SEARCH; NONDESTRUCTIVE EXAMINATION; RADIOGRAPHY; SLAGS; TEXTURES; WELDING; WELDS;

CLASSIFICATION OF DEFECTS; DEFECT DETECTION; GEOMETRICAL FEATURES; RADIOGRAPHIC IMAGES; SEGMENTED OBJECTS; TEXTURE FEATURES; TEXTURE MEASUREMENT; THREE FOLD CROSS VALIDATION;

CLASSIFICATION (OF INFORMATION);

EID: 77957830254     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2010.04.082     Document Type: Article

References (26)

1. Anouncia, S. M., & Saravanan, R. (2006). Non destructive testing using radiographic images - A survey. Insight: Non-Destructive Testing and Condition Monitoring, 48(10), 592-597.
2. British Standard (1998). Weldings and allied processes-classification of geometric imperfections in metallic materials-part 1: Fusion welding bs en iso 6520-6521
3. Carrasco, M. A., & Mery, D. (2004). Segmentation of welding defects using a robust algorithm. Materials Evaluation, 62(11), 1142-1147.
4. Crammer, K., & Singer, Y. (2002). On the algorithmic implementation of multiclass kernel-based vector machines. Journal of Machine Learning Research, 2, 265-292.
5. Da Silva, R. R., Siqueira, M. H. S., De Souza, M. P. V., Rebello, J. M. A., & Caloba, L. P. (2005). Estimated accuracy of classification of defects detected in welded joints by radiographic tests. NDT and E International, 38(5), 335-343.
6. Felzenszwalb, P. F., & Huttenlocher, D. P. (2004). Efficient graph-based image segmentation. International Journal of Computer Vision, 59(2), 167-181.
7. Haralick, R. M., Dinstein & Shanmugam, K. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, SMC-3, 610-621.
8. Hayes, C. (1997). ABC's of nondestructive weld examination. Weld, 76(5), 46-51.
9. Haykin, S. (1999). Neural Networks: A comprehensive foundation. Prentice Hall.
10. Hernandez, S., Saez, D., Mery, D., Da Silva, R., Calba, L. P., & Rebello, J. M. A. (2006). Statistical tools for weld defect evaluation in radiographic testing. In World conference on NDT - WCNDT06.
11. Jain, A. (1997). Feature selection: Evaluation, application, and small sample performance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(2), 153-158.
12. Lashkia, V. (2001). Defect detection in X-ray images using fuzzy reasoning. Image and Vision Computing, 19(5), 261-269.
13. Liao, T. W. (2003). Classification of welding flaw types with fuzzy expert systems. Expert Systems with Applications, 25(1), 101-111.
14. Liao, T. W., & Li, Y. (1998). An automated radiographic ndt system for weld inspection: Part ii - Flaw detection. NDT and E International, 31(3), 183-192.
15. Mery, D., & Berti, M. A. (2003). Automatic detection of welding defects using texture features. Insight: Non-Destructive Testing and Condition Monitoring, 45(10), 676-681.
16. Mirapeix, J., Garcia-Allende, P. B., Cobo, A., Conde, O. M., & Lspez-Higuera, J. M. (2007). Real-time arc-welding defect detection and classification with principal component analysis and artificial neural networks. NDT and E International, 40(4), 315-323. (Pubitemid 46400450)
17. Nacereddine, N., Tridi, M., Hamami, L., & Ziou, D. (2006). Statistical tools for weld defect evaluation in radiographic testing. In Proceedings of 12th European conference on non-destructive testing - ECNDT 2006.
18. Nacereddine, N., Zelmat, M., Belaifa, S. S., & Tridi, M. (2005). Qweld defect detection in industrial radiography based digital image processing. Transactions on Engineering Computing and Technology, 2, 145-148.
19. Nockemann, C., Heidt, H., & Thomsen, N. (1991). Reliability in ndt: Roc study of radiographic weld inspections. NDT and E International, 24(5), 235-245.
20. Shafeek, H. I., Gadelmawla, E. S., Abdel-Shafy, A. A., & Elewa, I. M. (2004). Automatic inspection of gas pipeline welding defects using an expert vision system. NDT and E International, 37(4), 301-307.
21. Sofia, M., & Redouane, D. (2002). Shapes recognition system applied to the non destructive testing. In Proceedings of 8th European conference on non-destructive testing - ECNDT 2002.
22. Sood, S., Blakeley, B., & Rebuffel, V. (2006). Shapes recognition system applied to the non destructive testing. In Proceedings of 12th European conference on nondestructive testing - ECNDT 2006.
23. Vilar, R., Zapata, J., & Ruiz, R. (2009). An automatic system of classification of weld defects in radiographic images. NDT and E International, 42(5), 467-476.
24. Wang, G., & Liao, T. W. (2002). Automatic identification of different types of welding defects in radiographic images. NDT and E International, 35(8), 519-528.
25. Warren Liao, T. (2008). Classification of weld flaws with imbalanced class data. Expert Systems with Applications, 35(3), 1041-1052.
26. Warren Liao, T. (2009). Improving the accuracy of computer-aided radiographic weld inspection by feature selection. NDT and E International, 42(4), 229-239.