Bayesian Pressure Snake for Weld Defect Detection

2009 ◽  
pp. 309-319 ◽  
Author(s):  
Aicha Baya Goumeidane ◽  
Mohammed Khamadja ◽  
Nafaa Naceredine
Keyword(s):  
Defect Detection ◽  
Weld Defect

Application of MFL on Girth-Weld Defect Detection of Oil and Gas Pipelines

2020 ◽  
Vol 11 (4) ◽  
pp. 04020047
Author(s):  
L. S. Dai ◽  
Q. S. Feng ◽  
J. Sutherland ◽  
T. Wang ◽  
S. Y. Sha ◽  
...  
Keyword(s):  
Defect Detection ◽  
Oil And Gas ◽  
Gas Pipelines ◽  
Weld Defect ◽  
Oil And Gas Pipelines

Stainless Steel Weld Defect Detection Using Pulsed Inductive Thermography

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

A Weld Defect Detection Method Based on Triplet Deep Neural Network

2020 ◽  
Author(s):  
Xiaoyuan Liu ◽  
Jinhai Liu ◽  
Fuming Qu ◽  
Hongfei Zhu ◽  
Danyu Lu
Keyword(s):  
Neural Network ◽  
Defect Detection ◽  
Deep Neural Network ◽  
Detection Method ◽  
Weld Defect

Digital Radiographic Image Enhancement for Weld Defect Detection using Smoothing and Morphological Transformations

2012 ◽  
Vol 9 (1) ◽  
pp. 15
Author(s):  
Suhaila Abdul Halim ◽  
Arsmah Ibrahim ◽  
Yupiter Harangan Prasada Manurung
Keyword(s):  
Defect Detection ◽  
Test Method ◽  
Radiographic Image ◽  
Radiographic Image Enhancement ◽  
Radiographic Images ◽  
Weld Defect ◽  
Different Types ◽  
Morphological Transformations ◽  
Non Destructive

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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