Development of a classification method for a crack on a pavement surface images using machine learning

Akiyoshi Hizukuri; Takeshi Nagata

doi:10.1117/12.2266911

14 May 2017 Development of a classification method for a crack on a pavement surface images using machine learning

Akiyoshi Hizukuri, Takeshi Nagata

Proceedings Volume 10338, Thirteenth International Conference on Quality Control by Artificial Vision 2017; 103380M (2017) https://doi.org/10.1117/12.2266911
Event: The International Conference on Quality Control by Artificial Vision 2017, 2017, Tokyo, Japan

Abstract

The purpose of this study is to develop a classification method for a crack on a pavement surface image using machine learning to reduce a maintenance fee. Our database consists of 3500 pavement surface images. This includes 800 crack and 2700 normal pavement surface images. The pavement surface images first are decomposed into several sub-images using a discrete wavelet transform (DWT) decomposition. We then calculate the wavelet sub-band histogram from each several sub-images at each level. The support vector machine (SVM) with computed wavelet sub-band histogram is employed for distinguishing between a crack and normal pavement surface images. The accuracies of the proposed classification method are 85.3% for crack and 84.4% for normal pavement images. The proposed classification method achieved high performance. Therefore, the proposed method would be useful in maintenance inspection.

Citation Download Citation

Akiyoshi Hizukuri and Takeshi Nagata "Development of a classification method for a crack on a pavement surface images using machine learning", Proc. SPIE 10338, Thirteenth International Conference on Quality Control by Artificial Vision 2017, 103380M (14 May 2017); https://doi.org/10.1117/12.2266911

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