Signal noise estimation and removal of sub‐mm 3D pavement texture data using 1D residual denoising network

Signal noise removal is an indispensable and critical procedure in obtaining clean pavement texture data for reliable pavement evaluation and management. Nevertheless, the presently established denoising approaches to pavement texture data still rely on traditional techniques that have long struggled with removing noise accurately and consistently. This paper innovatively initiates a one-dimensional (1D) residual denoising network (R1DNet) to achieve the noise removal of 3D pavement texture data. R1DNet is proposed as a 1D architectural encoder–decoder that considers the unique characteristics of 3D texture data from 3D laser imaging technology. The encoder extracts diverse profile features of input noisy texture data through two favorably developed 1D modular structures: a cascade deep convolutional module and a parallel multi-scale attention module. The decoder gradually parses the extracted profile features and estimates noise, with which the clean texture data are obtained based on a simple subtraction operation. The architecture of R1DNet is determined to be optimal in both accuracy and efficiency, using a customized performance-balancing evaluation function. For model development in a supervised manner, a systematic labeling method is specifically developed, which can build the baseline clean texture data from real 0.1 mm noisy 3D texture data. The experimental results show that the proposed R1DNet can effectively eliminate noise and produce clean texture data closely matching the baseline, presenting significant improvements in accuracy and consistency, compared to the traditional denoising methods.