Asphalt pavement macrotexture reconstruction from monocular image based on deep convolutional neural network

Abstract Pavement macrotexture is one of the major factors affecting pavement functions, and it is meaningful to reconstruct the pavement macrotexture rapidly and accurately for pavement life cycle performance and quality evaluation. To reconstruct pavement macrotexture from monocular image, a novel method was developed based on a deep convolutional neural network (CNN). First, the red‐green‐blue (RGB) images and depth maps (RGB‐D) of pavement texture were acquired by smartphone and laser texture scanner, respectively, from various asphalt mixture slab specimens fabricated in the laboratory, and the pavement texture RGB‐D dataset was established from scratch. Then, an encoder–decoder CNN architecture was proposed based on residual network‐101, and different training strategies were discussed for model optimization. Finally, the precision of the CNN and the three‐dimensional characteristics of the reconstructed macrotexture were analyzed. The results show that the established RGB‐D dataset can be used for training directly, and the established CNN architecture is plausible and effective. The mean texture depth and f 8 mac of the reconstructed macrotexture both correlate with the benchmarks significantly, and the correlation coefficients are 0.88 and 0.96, respectively. It could be concluded that the proposed CNN can reconstruct the macrotexture from monocular RGB images precisely, and the reconstructed macrotexture could be further used for pavement macrotexture evaluation.