Rail fasteners are critical components in high-speed railway. Therefore, they are inspected periodically to ensure the safety of high-speed trains. Manual inspection and two-dimensional visual inspection are the commonly used methods. However, both of them have drawbacks. In this paper, a rigorous high-speed railway fastener inspection approach from structured light sensors is proposed to detect damaged and loose fasteners. Firstly, precise and extremely dense point cloud of fasteners are obtained from commercial structured light sensors. With a decision tree classifier, the defects of the fasteners are classified in detail. Furthermore, a normal vector based center extraction method for complex cylindrical surface is proposed to extract the centerline of the metal clip of normal fasteners. Lastly, the looseness of the fastener is evaluated based on the extracted centerline of the metal clip. Experiments were conducted on high-speed railways to evaluate the accuracy, effectiveness, and the influence of the parameters of the proposed method. The overall precision of the decision tree classifier is over 99.8% and the root-mean-square error of looseness check is 0.15 mm, demonstrating a reliable and effective solution for high-speed railway fastener maintenance.

铁路紧固件是高速铁路中的关键组件。因此，应定期检查它们，以确保高速列车的安全。手动检查和二维外观检查是常用的方法。但是，它们都有缺点。本文提出了一种结构光传感器的严格的高速铁路紧固件检查方法，以检测损坏和松动的紧固件。首先，从商业结构的光传感器中获得了紧固件的精确且密度极高的点云。使用决策树分类器，可以对紧固件的缺陷进行详细分类。此外，提出了一种基于法向矢量的复杂圆柱面中心提取方法，以提取普通紧固件金属夹的中心线。最后，紧固件的松动度是根据提取的金属夹中心线评估的。在高速铁路上进行了实验，以评估该方法的准确性，有效性和参数的影响。决策树分类器的整体精度超过99.8％，松动检查的均方根误差为0.15 mm，这为高速铁路紧固件维护提供了可靠而有效的解决方案。