Quantitative road crack evaluation by a U-Net architecture using smartphone images and Lidar data |
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Authors: | Takahiro Yamaguchi Tsukasa Mizutani |
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Affiliation: | Institute of Industrial Science, The University of Tokyo, Tokyo, Japan |
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Abstract: | Road cracks are a major concern for administrators. Visual inspection is labor-intensive. The accuracy of previous algorithms for detecting cracks in images requires improvement. Further, the length and thickness of cracks must be estimated. Light detection and ranging (Lidar), a standard smartphone feature is used to develop a method for the completely automatic, accurate, and quantitative evaluation of road cracks. The two contributions of this study are as follows. To achieve the highest segmentation accuracy, U-Net is combined with data augmentation and morphology transform. To calculate the crack length and thickness, crack images are registered into Lidar color data. The proposed algorithm was validated using a public database of road cracks and those measured by the authors. The algorithm was 95% accurate in determining crack length. The coefficient of determination for thickness estimation accuracy was 0.98 addressing various crack shapes and asphalt pavement patterns. |
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