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.

中文翻译：

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使用 1D 残差降噪网络进行信号噪声估计和去除亚毫米 3D 路面纹理数据

信号噪声消除是获得干净的路面纹理数据以进行可靠的路面评估和管理不可或缺的关键程序。然而，目前建立的路面纹理数据降噪方法仍然依赖于传统技术，这些技术长期以来一直在努力准确、一致地去除噪声。本文创新性地启动了一维 （1D） 残差降噪网络 （R1DNet） 以实现三维路面纹理数据的噪声去除。R1DNet 被提议作为 1D 建筑编码器-解码器，它考虑了来自 3D 激光成像技术的 3D 纹理数据的独特特性。编码器通过两个有利开发的 1D 模块化结构提取输入噪声纹理数据的不同轮廓特征：级联深度卷积模块和并行多尺度注意力模块。解码器逐渐解析提取的轮廓特征并估计噪声，通过简单的减法运算获得干净的纹理数据。R1DNet 的架构使用定制的性能平衡评估功能确定在准确性和效率方面都是最佳的。针对有监督的模型开发，专门开发了一种系统标注方法，该方法可以从真实的 0.1 mm 噪声 3D 纹理数据中构建基线干净的纹理数据。实验结果表明，与传统的降噪方法相比，所提出的 R1DNet 可以有效消除噪声并产生与基线紧密匹配的干净纹理数据，在准确性和一致性方面有显著提高。