Journey into Automation: Image-Derived Pavement Texture Extraction and Evaluation

TL;DR

This paper presents an automated, image-based system for extracting pavement texture features and accurately predicting mean texture depth (MTD), improving pavement safety assessments through innovative 3D data acquisition and machine learning models.

Contribution

It introduces an economical method for 3D pavement texture data collection, enhances image processing techniques, and develops multivariate models linking texture features to MTD.

Findings

Gradient Boosting Tree model achieves R2 = 0.9858 in prediction accuracy.

Proposed method has less than 10% relative error in field tests.

System provides an end-to-end pavement quality evaluation solution.

Abstract

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.