Second Order Statistical Texture Representation of Asphalt Pavement Distress Images Based on Local Binary Pattern in Spatial and Wavelet Domain

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Shahabian Moghaddam R.; MOHAMMADZADEH MOGHADDAM A.; SAHAF S.A.; POURREZA H.R.

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Journal Paper

Paper Information

Journal: JOURNAL OF REHABILITATION IN CIVIL ENGINEERING Year:2019 | Volume:7 | Issue:3 Page(s): 48-67

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Title

Second Order Statistical Texture Representation of Asphalt Pavement Distress Images Based on Local Binary Pattern in Spatial and Wavelet Domain

Author(s)

Shahabian Moghaddam R. | MOHAMMADZADEH MOGHADDAM A. | SAHAF S.A. | POURREZA H.R. | Issue Writer Certificate

Keywords

Pavement Distress Texture

Computer Vision

Gray Level Co-Occurrence Matrix (GLCM)

Local Binary Pattern (LBP)

Wavelet Transform

Abstract

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.

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APA: Copy

Shahabian Moghaddam, R., MOHAMMADZADEH MOGHADDAM, A., SAHAF, S.A., & POURREZA, H.R.. (2019). Second Order Statistical Texture Representation of Asphalt Pavement Distress Images Based on Local Binary Pattern in Spatial and Wavelet Domain. JOURNAL OF REHABILITATION IN CIVIL ENGINEERING, 7(3), 48-67. SID. https://sid.ir/paper/339999/en

Vancouver: Copy

Shahabian Moghaddam R., MOHAMMADZADEH MOGHADDAM A., SAHAF S.A., POURREZA H.R.. Second Order Statistical Texture Representation of Asphalt Pavement Distress Images Based on Local Binary Pattern in Spatial and Wavelet Domain. JOURNAL OF REHABILITATION IN CIVIL ENGINEERING[Internet]. 2019;7(3):48-67. Available from: https://sid.ir/paper/339999/en

IEEE: Copy

R. Shahabian Moghaddam, A. MOHAMMADZADEH MOGHADDAM, S.A. SAHAF, and H.R. POURREZA, “Second Order Statistical Texture Representation of Asphalt Pavement Distress Images Based on Local Binary Pattern in Spatial and Wavelet Domain,” JOURNAL OF REHABILITATION IN CIVIL ENGINEERING, vol. 7, no. 3, pp. 48–67, 2019, [Online]. Available: https://sid.ir/paper/339999/en

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