用图像自动提取路面纹理并预测抗滑性能，精度超98%。
Journey into Automation: Image-Derived Pavement Texture Extraction and Evaluation
- 通过图像生成3D纹理数据，低成本实现路面扫描
- 提出多特征融合模型，预测结果相关系数达0.9858
- 适合道路检测、智能养护等工程场景应用
路面平均纹理深度（MTD）是评估沥青路面抗滑性能和保障行车安全的关键指标。本文提出一种基于路面图像的自动化纹理提取与MTD评估系统。首先，设计了一种经济高效的三维（3D）路面纹理数据采集方法；其次，改进3D图像处理技术，提取能表征多种纹理特性的特征；最后，建立多元预测模型，将这些特征与实际MTD值关联。验证结果显示，梯度提升树（GBT）模型预测精度高且稳定（R² = 0.9858），现场测试表明该方法相对误差低于10%，优于现有技术。本方法提供从图像输入到MTD输出的完整端到端解决方案，适用于路面质量评估。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。