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_8mac 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.

中文翻译：

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基于深度卷积神经网络的单目图像沥青路面宏观纹理重建

路面宏观纹理是影响路面功能的主要因素之一，快速、准确地重建路面宏观纹理对于路面生命周期性能和质量评价具有重要意义。为了从单目图像重建路面宏观纹理，开发了一种基于深度卷积神经网络（CNN）的新方法。首先，通过智能手机和激光纹理扫描仪，分别从实验室制作的各种沥青混合料板试样中获取路面纹理的红-绿-蓝（RGB）图像和深度图（RGB-D），以及路面纹理RGB -D 数据集是从头开始建立的。然后，提出了一种基于残差网络101的编码器-解码器CNN架构，并讨论了模型优化的不同训练策略。最后，分析了CNN的精度和重建的宏观纹理的三维特征。结果表明，建立的RGB-D数据集可直接用于训练，建立的CNN架构合理有效。平均纹理深度和重构的宏观纹理的f _{8 mac}均与基准显着相关，相关系数分别为 0.88 和 0.96。可以得出结论，所提出的 CNN 可以精确地从单目 RGB 图像中重建宏观纹理，并且重建的宏观纹理可以进一步用于路面宏观纹理评估。