Bayesian Pressure Snake for Weld Defect Detection

Author(s):  
Aicha Baya Goumeidane ◽  
Mohammed Khamadja ◽  
Nafaa Naceredine
Keyword(s):  
2020 ◽  
Vol 11 (4) ◽  
pp. 04020047
Author(s):  
L. S. Dai ◽  
Q. S. Feng ◽  
J. Sutherland ◽  
T. Wang ◽  
S. Y. Sha ◽  
...  

2016 ◽  
Vol 26 (7) ◽  
pp. 1-4 ◽  
Author(s):  
Yuhua Cheng ◽  
Libing Bai ◽  
Fan Yang ◽  
Yifan Chen ◽  
Shenhua Jiang ◽  
...  

2012 ◽  
Vol 9 (1) ◽  
pp. 15
Author(s):  
Suhaila Abdul Halim ◽  
Arsmah Ibrahim ◽  
Yupiter Harangan Prasada Manurung

Accurate inspection of welded materials is important in relation to achieve acceptable standards. Radiography, a non-destructive test method, is commonly used to evaluate the internal condition of a material with respect to defect detection. The presence of noise in low resolution of radiographic images significantly complicates analysis; therefore attaining higher quality radiographic images makes defect detection more readily achievable. This paper presents a study pertaining to the quality enhancement of radiographic images with respect to different types of defects. A series of digital radiographic weld flaw images were smoothed using multiple smoothing techniques to remove inherent noise followed by top and bottom hat morphological transformations. Image quality was evaluated quantitatively with respect to SNR, PSNR and MAE. The results indicate that smoothing enhances the quality of radiographic images, thereby promoting defect detection with the respect to original radiographic images. 


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