Statistical Texture Analysis of Asphalt Pavement Distress Images Based on Grey Level Co-occurrence Matrix

Evaluation of pavement performance plays a major role in pavement management systems for determination of optimum strategy in repair and maintenance of the road. One of the most prominent assets in evaluation of the pavement is identification and survey of pavement surface distresses. In the past two decades, extensive studies have been carried out in order to develop automatic methods for pavement distress evaluation. Most of these methods are based on computer vision and image processing techniques. Of the most important components of machine vision systems is the feature extraction process. Textural features present more detailed information about the image regions characteristics compared to other features such as color and geometrical (shape) properties. In the present study, after acquisition of six different groups of asphalt pavement distress images under controlled condition, in order to analyze and describe their texture, second order statistics based on grey level co-occurrence matrix has been employed. In order to generate the images co-occurrence matrices, four distinct directions and three different distance (offset) parameters have been utilized. Based on the results of the classification of distress images acquired by Mahalanobis minimum distance classifier, it can be concluded that statistical indices extracted from grey level co-occurrence matrix having distance parameter equal to one, have superior discrimination performance in camparison to other selected distance values. The classification accuracy rates of asphalt pavement distress images based on grey level co-occurrence matrix with one, two and three distance parameters values are 80%, 75% and 60%, respectively.