Weld Defect Detection in Industrial Radiography Based Digital Image Processing
Industrial radiography is a famous technique for the identification and evaluation of discontinuities, or defects, such as cracks, porosity and foreign inclusions found in welded joints. Although this technique has been well developed, improving both the inspection process and operating time, it does suffer from several drawbacks. The poor quality of radiographic images is due to the physical nature of radiography as well as small size of the defects and their poor orientation relatively to the size and thickness of the evaluated parts. Digital image processing techniques allow the interpretation of the image to be automated, avoiding the presence of human operators making the inspection system more reliable, reproducible and faster. This paper describes our attempt to develop and implement digital image processing algorithms for the purpose of automatic defect detection in radiographic images. Because of the complex nature of the considered images, and in order that the detected defect region represents the most accurately possible the real defect, the choice of global and local preprocessing and segmentation methods must be appropriated.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1330641Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3630
 A. de Carvalho & al., “Evaluation of the relevant features of welding defects in radiographic inspection," Materials Research, vol. 6, n┬░ 3, pp. 427-432, 2003.
 Ch. Schwartz, “Automatic Evaluation of Welded Joints Using Image Processing on Radiographs," Conference Proceedings American Institute of Physics, vol 657(1) pp. 689-694, March 2003.
 N. Nacereddine, R Drai and A. Benchaala, “Weld defect extraction and identification in radiograms based neural networks," in Proc. IASTED International Conference on Signal Processing, Pattern Recognition, and Applications, Crete, Greece, June 2002, pp 38-43.
 N. Nacereddine, “Automated method implementation for detection and classification of weld defects in industrial radiography," M.S. thesis, Dept. Automation, Boumerdes Univ., Algeria, 2004.
 A. Kehoe, “The detection and evaluation of defects in industrial images," Ph.D. Thesis, University of Surrey, 1990.
 Y. Zheng, J.P. Basart, “Image analysis, feature extraction and various applied enhancement methods for NDE X-Ray images," Review of Progress in QNDE, vol. 7, pp. 813-820, 1988.
 Da Silva & al., “Contribution to the development of a radiographic inspection automated system," 8eECNDT, Barcelone, June 2002.
 R.C. Gonzalez, R.E. Woods, Digital Image Processing. Addison Wesley Publishing Company, 1993.
 L. Soler, G. Malandrin and H. Delinguette, “Segmentation automatique: Application aux angioscanners 3D," Revue de Traitement de Signal, vol. 15, 1998.
 Sezgin, B. Sankur, “Comparison of thresholding methods for nondestructive testing applications," IEEE Conference on Image Processing, Grèce. Oct. 2001.
 W. Niblack, Introduction to digital image processing. Prentice Hall, 1986.
 R.M. Haralick, S.R. Sternberg, X. Zhuang, ÔÇÿImage analysis using mathematical morphology-, IEEE Trans on Pattern Analysis and Machine Intelligence, vol.9/4, pp.532-550, 1987.