The routine inspection of railheads for defects such as wear and surface cracks is a tedious process, which, if not detected, can alter the wheel-rail contact interaction leading to catastrophic events. This study investigates and implements a railhead measurement method using image-based three-dimensional reconstruction, which enables rapid scanning of railheads and production of detail cross-sectional measurements for rail-wheel interface analysis. A complete workflow with a methodology for reconstructing railheads from images and extracting cross-sectional measurements from the reconstructed model is presented. In order to validate the proposed method in the field, a mobile automated system was equipped with an array of cameras specifically spaced to cover the areas of interest on the railhead. The system can automatically transverse along the railhead, acquiring images synchronously. Two case studies in the laboratory environment and the real railway site have been performed to evaluate the performance and accuracy against industry practices. The results show that the proposed method can accurately measure the railhead cross-sectional profile at an root mean square error (RMSE) less than 0.3 mm compared with MiniProf. Furthermore, continuous cross-sectional data and intuitive color information are provided by our method which can help inspectors to locate defects easily and more efficiently.

对轨头的磨损和表面裂纹等缺陷进行例行检查是一个繁琐的过程，如果未检测到，可能会改变轮轨接触相互作用，从而导致灾难性事件。本研究研究并实施了一种使用基于图像的 3D 重建的轨头测量方法，该方法能够快速扫描轨头并生成用于铁路-车轮界面分析的详细横截面测量值。提出了一个完整的工作流程，其中包含从图像中重建铁路头并从重建模型中提取横截面测量值的方法。为了在现场验证所提出的方法，一个移动自动化系统配备了一系列摄像机，这些摄像机专门隔开以覆盖铁路头上的感兴趣区域。系统可自动沿轨头横向，同步采集图像。已经在实验室环境和真实的铁路现场进行了两个案例研究，以根据行业实践评估性能和准确性。结果表明，与MiniProf相比，所提出的方法可以在均方根误差（RMSE）小于0.3 mm的情况下准确测量轨头横截面轮廓。此外，我们的方法提供了连续的横截面数据和直观的颜色信息，可以帮助检查人员轻松、更有效地定位缺陷。结果表明，与MiniProf相比，所提出的方法可以在均方根误差（RMSE）小于0.3 mm的情况下准确测量轨头横截面轮廓。此外，我们的方法提供了连续的横截面数据和直观的颜色信息，可以帮助检查人员轻松、更有效地定位缺陷。结果表明，与MiniProf相比，所提出的方法可以在均方根误差（RMSE）小于0.3 mm的情况下准确测量轨头横截面轮廓。此外，我们的方法提供了连续的横截面数据和直观的颜色信息，可以帮助检查人员轻松、更有效地定位缺陷。