Quantitative Road Crack Evaluation by a U-Net Architecture using
Smartphone Images and Lidar Data
Abstract
Road cracks are an important concern of administrators. Visual
inspection is labor-intensive and subjective, while previous algorithms
detecting cracks from optical camera images were not accurate.
Furthermore, the actual length and thicknesses of a crack cannot be
estimated only from images. Light Detection and Ranging (Lidar) is a
standard feature introduced on the latest smartphones. In this research,
for completely automatic, accurate and quantitative road crack
evaluation using smartphones, an up-to-date segmentation technique,
U-Net with morphology transform adopting data augmentation was proposed.
Lidar 3D point cloud data of smartphones is linked to color data
obtained from cameras. By registering images to Lidar data, geometrical
relationships were estimated to calculate the length and thicknesses.
The proposed algorithm was validated by a standard database of road
cracks and dataset constructed by the authors, showing 95% length
accuracy and 0.98 coefficient of determination for thickness estimation
irrespective of various crack shapes and asphalt pavement color
patterns.