Automatic Recognition and Classification of Pavement Distress based on Analysis of Image Texture in Spatial and Transformation Domain

Evaluation of pavement performance is one of the most prominent assets in choosing the beneficial strategy for pavement management operations. In the past two decades, a considerable number of investigations have been carried out on developing automatic methods for distress rocognition all of which rely on the machine vision and image processing techniques. In the past few years multi-resolutional analysis methods, namely wavelet transform has provided a great tool for fast and accurate auto-detection of distresses. In the present study, a method has been proposed utilizing the wavelet transform method which can analyze the texture surface of pavement considering the longitudinal, transverse and diagonal textural structures as the key elements. In this paper, after performing the discrete wavelet transform and decomposing the image into frequency sub-bands using 4 different wavelet families, properties of sub-bands texture has been acquired (based on grey level co-occurrence matrix) and compared to the results acquired based on image texture analysis in spatial domain. Finally, the minimal Mahalanobis distance method was applied in order to categorize the acquired images into seven classes including alligator cracking, without distress, longitudinal cracking, transverse cracking, bleeding, patching and raveling. Based on the results of validation and evaluation of the classifiction performance it was observed that the distress image classification using image texture analysis in the transformation domain leads to the more accurate results in comparison to spatial domain. The mean accuracy of distress image classification in transformation domain is 67% while the accuracy rate in classification of distress data based on extraction of texture features in spatial domain is 49/76%. In case of transformation domain, although Daubechies 2 filter has a better sensitivity rate in discrimination of bleeding distress, in general, the Haar filter outperformed other utilized wavelets in recognition and classification of asphalt pavement surface distresses with 95% accuracy.