14 May 2017 A method based on machine learning using hand-crafted features for crack detection from asphalt pavement surface images
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Proceedings Volume 10338, Thirteenth International Conference on Quality Control by Artificial Vision 2017; 103380I (2017) https://doi.org/10.1117/12.2264075
Event: The International Conference on Quality Control by Artificial Vision 2017, 2017, Tokyo, Japan
Abstract
Application of machine vision is expected for efficiency and objectivity of inspection in various fields. Automation of visual inspection for asphalt pavement surface images is also expected, but it is difficult because of unexpected objects, non-uniform illumination and irregularities in the pavement surface. Many of conventional approaches are based on state-of-the-arts. However, there is a problem that the application conditions of these is limited. In this article, we proposed a new method based on state-of-the-art and machine learning for crack detection from asphalt pavement surface images. The classifier of the proposed method is the linear support vector machine, and it uses features proposed in the conventional study that is one of the state-of-the-art approaches. The proposed system need not a large number of training data, unlike deep learning architectures. It is easy to train the classifier to detect cracks using a GUI tool developed by authors. Quantitative evaluation using 100 road surface images obtained by mobile mapping system was performed to compare with our conventional method as one of state-of-the-art approaches. Experiments show that our proposed method clearly outperforms the state-of-the-art approach.
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Yusuke Fujita, Yusuke Fujita, Koji Shimada, Koji Shimada, Manabu Ichihara, Manabu Ichihara, Yoshihiko Hamamoto, Yoshihiko Hamamoto, } "A method based on machine learning using hand-crafted features for crack detection from asphalt pavement surface images ", Proc. SPIE 10338, Thirteenth International Conference on Quality Control by Artificial Vision 2017, 103380I (14 May 2017); doi: 10.1117/12.2264075; https://doi.org/10.1117/12.2264075
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