Abstract
The most common asphalt pavement surface distress is cracking, manifested in various forms, such as transverse, longitudinal, and reflective cracks, governed by different initiation and propagation mechanisms. Being able to timely detect and classify different types of cracks provides critical information for properly maintaining and managing our invaluable road assets. In this paper, a series of classic image processing techniques were applied to pavement surface images to delineate crack patterns. The processed images were projected to a low-dimensional feature space through principal component analysis (PCA). A K-means algorithm is then applied to cluster images in the low-dimensional feature space. The results revealed a meaningful correlation between the crack patterns and the clusters derived.
Keywords
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Abdelmawla, A., Yang, J.J., Kim, S.S. (2021). Unsupervised Learning of Pavement Distresses from Surface Images. In: Liu, Y., Cuomo, S., Yang, J. (eds) Advances in Innovative Geotechnical Engineering. GeoChina 2021. Sustainable Civil Infrastructures. Springer, Cham. https://doi.org/10.1007/978-3-030-80316-2_1
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DOI: https://doi.org/10.1007/978-3-030-80316-2_1
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