فهرست مطالب seyed ali sahaf

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 distress 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.

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.