自动化之旅：基于图像的路面纹理提取与评估
Journey into Automation: Image-Derived Pavement Texture Extraction and Evaluation
AI总结：
本文提出基于路面图像的自动化纹理提取与MTD评估方法，通过低成本3D数据采集、改进图像处理及GBT多变量预测，实现R²=0.9858且现场误差低于10%。
摘要：
平均纹理深度（MTD）对于评估沥青路面的抗滑性能并保障道路安全至关重要。本研究聚焦于开发一套基于路面图像提取纹理特征并评估MTD的自动化系统。本工作的贡献有三方面：首先，提出了一种获取三维（3D）路面纹理数据的经济方法；其次，改进了3D图像处理技术，并构建了能够表征纹理各方面特征的指标；第三，建立了将这些特征与MTD值相关联的多变量预测模型。验证结果表明，Gradient Boosting Tree（GBT，梯度提升树）模型实现了出色的预测稳定性与准确性（R² = 0.9858），现场试验表明所提方法优于其他技术，相对误差低于10%。该方法提供了从图像输入到MTD预测输出的全面端到端路面质量评估解决方案。
英文摘要：
Mean texture depth (MTD) is pivotal in assessing the skid resistance of asphalt pavements and ensuring road safety. This study focuses on developing an automated system for extracting texture features and evaluating MTD based on pavement images. The contributions of this work are threefold: firstly, it proposes an economical method to acquire three-dimensional (3D) pavement texture data; secondly, it enhances 3D image processing techniques and formulates features that represent various aspects of texture; thirdly, it establishes multivariate prediction models that link these features with MTD values. Validation results demonstrate that the Gradient Boosting Tree (GBT) model achieves remarkable prediction stability and accuracy (R2 = 0.9858), and field tests indicate the superiority of the proposed method over other techniques, with relative errors below 10%. This method offers a comprehensive end-to-end solution for pavement quality evaluation, from images input to MTD predictions output.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
