The measurement of the longitudinal rail profile is relevant to the condition monitoring of the rail infrastructure. The running surface is recognizable as a shiny metallic area on top of the rail head. The detection of the running surface is crucial for vehicle-based rail profile measurements, as well as for defect detection. This paper presents a methodology for the automatic detection of the running surface based on a laser profilometer. The detection of the running surface is performed based on the light reflected from the rail surface. Three rail surfaces with different surface conditions are considered. Supervised machine learning is applied to classify individual surface elements as part of the running surface. Detection by a linear support vector machine is performed with accuracy of >90%. The lateral position of the running surface and its width are calculated. The average deviation from the labeled widths varies between −1.2mm and 5.6mm. The proposed measurement approach could be installed on a train for the future onboard monitoring of the rail network.

中文翻译：

---

基于激光轮廓仪数据和监督机器学习的铁路轨道运行面自动检测

铁路纵向轮廓的测量与铁路基础设施的状态监测相关。运行表面可被识别为轨头顶部的闪亮金属区域。运行表面的检测对于基于车辆的轨道轮廓测量以及缺陷检测至关重要。本文提出了一种基于激光轮廓仪自动检测运行表面的方法。运行表面的检测是基于从轨道表面反射的光来进行的。考虑了具有不同表面条件的三个轨道表面。应用监督机器学习将各个表面元素分类为跑步表面的一部分。通过线性支持向量机进行检测，准确度>90%。计算运行表面的横向位置及其宽度。与标记宽度的平均偏差在 -1.2 毫米到 5.6 毫米之间变化。所提出的测量方法可以安装在火车上，以便将来对铁路网络进行车载监测。