A Comparative Study of Shearlet, Wavelet, Laplacian Pyramid, Curvelet, and Contourlet Transform to Defect Detection

Abstract

This study presents a new approach based on shearlet transform for the first time to detect damages, and compare it with the wavelet, Laplacian pyramid, curvelet, and contourlet transforms to specify different types of defects in plate structures. Wavelet and Laplacian pyramid transforms have inferior performance to detect flaws with different multi-directions, such as curves, because of their basic element form, expressing the need for more efficient transforms. Therefore, some transforms, including curvelet and contourlet, have been evaluated so far for improving the performance of traditional transforms. Although these transforms have overcome the deficiencies of previous methods, they have a weakness in detecting several imperfections with various shapes in plate structures —one of the essential requirements that each transform should possess. In this study, we have used the shearlet transform that is used for the first time to detect identification and overcome all previous transform dysfunctionalities. In this regard, these transforms were applied to a four-fixed supported square plate with various defects. The obtained results revealed that the shearlet transform has the premier capability to demonstrate all kinds of damages compared to the other transforms, namely wavelet, Laplacian pyramid, curvelet, and contourlet. Also, the shearlet transform can be considered as an excellent and operational approach to demonstrate different forms of defects. Furthermore, the performance and correctness of the transforms have been verified via the experiment.

Keywords

• wavelet transform
• laplacian pyramid transform
• curvelet transform
• contourlet transform
• shearlet transform
• damage detection