Crack defect detection and localization using genetic-based inverse voting Hough transform

Author(s):  
K. Maalmi ◽  
A. El-Ouaazizi ◽  
R. Benslimane ◽  
L.F.C. Lew Yan Voon ◽  
A. Diou ◽  
...  
Author(s):  
Leszek J Chmielewski ◽  
Katarzyna Laszewicz-Śmietańska ◽  
Piotr Mitas ◽  
Arkadiusz Orłowski ◽  
Jarosław Górski ◽  
...  

2019 ◽  
Vol 9 (3) ◽  
pp. 459 ◽  
Author(s):  
Qingnan Xie ◽  
Chenyin Ni ◽  
Zhonghua Shen

When working in humid environments, corrosion defects are easily produced in metallic plates. For defect detection in underwater plates, symmetric modes of Lamb waves are widely used because of their characteristics including long propagating distance and high sensitivity to defects. In this study, we extend our previous work by applying the laser laterally generated S0 mode to detection and localization of defects represented by artificial notches in an aluminum plate immersed in water. Pure non-dispersive S0 mode is generated in an underwater plate by lateral laser source irradiation and its fd (frequency·thickness) range is selected by theoretical calculation. Using this lateral excitation, the S0 mode is enhanced; meanwhile, the A0 mode is effectively suppressed. The mode-converted A0 mode from the incident S0 mode is used to detect and localize the defect. The results reveal a significantly improved capability to detect defects in an underwater plate using the laser laterally generated S0 mode, while that using A0 is limited due to its high attenuation. Furthermore, owing to the long propagating distance and the non-dispersion characteristics of the S0 generated by the lateral laser source, multiple defects can also be detected and localized according to the mode conversion at the defects.


2011 ◽  
Vol 2011 ◽  
pp. 1-14 ◽  
Author(s):  
Ikhlas Abdel-Qader ◽  
Fadi Abu-Amara ◽  
Osama Abudayyeh

We present in this paper a framework for the automatic detection and localization of defects inside bridge decks. Using Ground-Penetrating Radar (GPR) raw scans, this framework is composed of a feature extraction algorithm using fractals to detect defective regions and a deconvolution algorithm using banded-independent component analysis (ICA) to reduce overlapping between reflections and to estimate the radar waves travel time and depth of defects. Results indicate that the defects' estimated horizontal location and depth falling within 2 cm (76.92% accuracy) and 1 cm (84.62% accuracy) from their actual values.


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