Automatic Recognition and Classification of Asphalt Pavement Distress Texture Based on Wavelet Transform

Inspection of the pavement distresses is one of the most prominent phases of pavement management process in regard with determining optimum pavement maintenance strategies. Over the past few decades, a considerable number of efforts have been carried out on developing automatic methods for objectively distress detection all of which rely on machine vision and image processing techniques. One of the most important assets comprising machine vision systems is the feature extraction process. In the past few years, multi-resolutional analysis approaches, namely wavelet transforms has provided a great tool for fast and accurate image texture representation. In the present study, after acquisition of six different types of asphalt pavement distresses under controlled condition, in order to identify and categorize them, four 2-D multi-resolution transforms including Haar discrete wavelet, Daubechies3 discrete wavelet, Coiflet1 discrete wavelet and dual-tree complex wavelet were utilized. After decomposition of the distress images by applying the aforementioned transforms, first-order statistical indices based on histogram and second-order statistics based on gray level co-occurrence matrix were employed, in order to describe the wavelet frequency sub-bands texture. The distress classification results based on minimum Mahalanobis distance classifier, indicate that extracting second-order statistics from the sub-bands of the dual-tree complex wavelet and Haar discrete wavelet transforms, yielding classification accuracy of 99% and 95% respectively, outperform other feature extraction algorithms in distress recognition. Furthermore, statistical indices acquired from gray level co-occurrence matrix with average classification rate of 87%, obtained superior performance in distress images discrimination compared to histogram statistics.