Assessment of pavement distresses is one of the important parts of pavement management systems to adopt the most effective road maintenance strategy. In the last decade, extensive studies have been done to develop automated systems for pavement distress processing based on machine vision techniques. One of the most important structural components of computer vision is the feature extraction method. In most of the application areas of image processing, textural features provide more efficient information of image regions properties than other characteristics. In this research, three different algorithms were used to extract the feature vector and statistically analyzing the texture of six various types of asphalt pavement surface distresses. The first algorithm is based on the extraction of images second-order textural statistics utilizing gray level co-occurrence matrix in spatial domain. In second and third algorithms, the second-order descriptors of images local binary patterns were extracted in spatial and wavelet transform domain, respectively. The classification of the disress images based on a combination of K-nearest neighbor method and Mahalanobis distance, indicates that two stages arranging of the gray levels of the distress images edges by applying wavelet transform and local binary pattern (third algorithm) had a superior result in comparison with other algorithms in texture recognition and separation of pavement distresses. Classification performance accuracy of the distress images based on first, second and third feature extraction algorithms is 61%, 75% and 97%, respectively.

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