Exploring pavement crack evaluation with bidimensional empirical mode decomposition

Albert Ayenu-Prah; Nii Attoh-Okine

doi:10.1117/12.719418

9 April 2007 Exploring pavement crack evaluation with bidimensional empirical mode decomposition

Albert Ayenu-Prah, Nii Attoh-Okine

Proceedings Volume 6576, Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks V; 65760Q (2007) https://doi.org/10.1117/12.719418
Event: Defense and Security Symposium, 2007, Orlando, Florida, United States

Abstract

Crack evaluation is essential for effective classification of pavement cracks. Digital images of pavement cracks have been analyzed using techniques such as fuzzy set theory and neural networks. Bidimensional empirical mode decomposition (BEMD), a new image analysis method recently developed, can potentially be used for pavement crack evaluation. BEMD is an extension of the empirical mode decomposition (EMD), which can decompose non-linear and non-stationary signals into basis functions called intrinsic mode functions (IMF). IMFs are monocomponent functions that have well defined instantaneous frequencies. EMD is a sifting process that is non-parametric and data-driven; it does not depend on an a priori basis set. It is able to remove noise from signals without complicated convolution processes. BEMD decomposes an image into two-dimensional IMFs. The present paper explores pavement crack detection using BEMD together with the Sobel edge detector. A number of images are filtered with BEMD to remove noise, and the residual image analyzed with the Sobel edge detector for crack detection. The results are compared with results from the Canny edge detector, which uses a Gaussian filter for image smoothing before performing edge detection. The objective is to qualitatively explore how well BEMD is able to smooth an image for more effective and speedier edge detection with the Sobel method.

Citation Download Citation

Albert Ayenu-Prah and Nii Attoh-Okine "Exploring pavement crack evaluation with bidimensional empirical mode decomposition", Proc. SPIE 6576, Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks V, 65760Q (9 April 2007); https://doi.org/10.1117/12.719418

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