Defect detection for tire laser shearography image using curvelet transform based edge detector

In this work, we approach the analysis and segmentation of tire laser shearography image by combining curvelet transform and Canny edge detection to detect defects in tire surface. We rely on the feature of curvelet that edge features can be represented with larger coefficients in sub-highest frequency band thus we modify curvelet coefficients to enhance image edges before further edge detection operations. Only the most important coefficients that contribute to rebuild edges are selected to reconstruct the image while most small coefficients are cut off. This would result in a reconstructed image more convenient for edge detection and the time complexity is reduced on the other hand. Furthermore, the eight-neighborhood bilinear interpolation non-maximum suppression method is introduced to improve the performance of Canny edge detection. Our detection results are evaluated on test laser shearography images using the proposed scheme and compare favorably to the state-of-the-art methods.

► We combine curvelet and Canny operator to detect defects in laser shearography image. ► We represent edge features with the larger coefficients in sub-highest frequency band. ► Modifying curvelet coefficients will enhance image edges. ► Cutting off most small coefficients and reconstruct with that contribute to edges. ► Eight-neighborhood bilinear interpolation non-maximum suppression method is introduced.