14 May 2017 Development of a classification method for a crack on a pavement surface images using machine learning
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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.
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Akiyoshi Hizukuri, 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); doi: 10.1117/12.2266911; https://doi.org/10.1117/12.2266911
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