基于纹理的图像分析和可解释的机器学习用于在路面状况监测中识别抛光青
Mansour Fakhri1, Seyed Vahid Pourjafar2, Mohammad Hassan Daneshvari3
1Faculty of Civil Engineering Tehran, K N Toosi University of Technology, Tehran, Iran. fakhri@kntu.ac.ir.
Scientific reports
|December 4, 2025
概括
本研究介绍了一种机器学习 (ML) 框架,使用灰色级别共发生矩阵 (GLCM) 纹理分析来检测抛光青路面表面. 可解释的方法准确地识别了路面退化,提高了道路安全和监测.
科学领域:
- 土木工程 土木工程是指土木工程.
- 材料科学 材料科学 材料科学
- 计算机科学 计算机科学
背景情况:
- 抛光的青路面表面降低了滑动阻力,造成了严重的道路安全风险.
- 聚合物降解和青老化是路面抛光和纹理改变的主要原因.
研究的目的:
- 开发和验证可解释的机器学习 (ML) 框架,用于检测抛光青路面表面.
- 分析纹理特征,以了解路面抛光机制,并改善条件监测.
主要方法:
- 利用了12480张路面图像的数据集,使用灰色水平共发生矩阵 (GLCM) 提取了24个纹理特征.
- 使用Hyperopt框架训练和优化各种ML模型,包括一个反向传播神经网络 (BPNN).
- 雇佣了SHapley添加剂解释 (SHAP) 来解释特征贡献和理解纹理驱动的抛光.
主要成果:
- 在检测抛光青表面时,BPNN实现了96.1%的分类精度.
- SHAP分析提供了基于表面纹理的路面抛光机制的物理洞察力.
- 一个基于ResNet50的卷积神经网络 (CNN) 显示出更高的准确性 (98.7%),但具有更高的计算需求.
结论:
- 拟议的GLCM-ML框架为路面状况监测提供了一个可解释和有效的工具.
- 这种方法增强了对表面纹理演变的理解,并支持路面维护的预测建模.
- 该方法为评估道路表面安全性和完整性提供了物理意识的替代方案.
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