Three-Dimensional Pavement Crack Detection Algorithm Based on Two-Dimensional Empirical Mode Decomposition

In order to detect pavement cracking with high accuracy, a type of three-dimensional (3D) pavement detection approach is proposed based on a two-dimensional (2D) empirical mode decomposition (EMD) algorithm, a nonparametric and data-driven sifting method which is able to detect pavement cracking without complicated convolution processes. By extending 2D EMD to the analysis of 3D pavement data, the proposed approach first decomposes pavement cracking data into subelements called intrinsic model functions (IMFs), which are monocomponent functions that have well-defined instantaneous frequencies. By combining double-phase standard deviation with structural element–based morphological algorithms, random noise is eliminated from the original collected 3D pavement data. The main cracking then is extracted using the EMD method, followed by binarization of the pavement distress images. Finally, pavement cracking is extracted by combining the region growing method with the morphology method on the binarized images. Experiments on different types of pavement cracking demonstrate the proposed algorithm to be accurate and superior to manual detection in terms of precision-recall curves. Moreover, the proposed approach gives a novel prospective for the measurement of pavement performance criteria.