Cracks that develop in railway infrastructural components such as tunnel linings and track systems are not easy to detect on high‐speed rail routes, since inspection time is limited during the daytime and visibility is very poor at night. Meanwhile, cracks to structures such as those above mentioned are strictly monitored and treated to prevent possible malfunction or accident. In this regard, a track measurement vehicle is normally deployed to image track components and measure geometric information. The main goal of the present study was to detect cracks in images and to simultaneously measure the maximum crack width by means of newly introduced deep learning technology. For this, a shape‐sensitive kernel, that is, crack‐like kernel, within a semantic segmentation framework and a modified deep layer model were proposed. In addition to the conventional statistical models such as accuracy and intersection over union, the predicted results of the proposed models were verified by considering the boxplot and root mean square errors of the estimated crack widths. According to the results, the proposed shape‐sensitive kernel function was able to predict crack width more precisely by one or two pixels than the conventional semantic segmentation model. Future work will concentrate on the integration of the crack detection model with deterioration prediction of track geometry in order to enable systematic decision making for the predictive maintenance of railway systems.

中文翻译：

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基于形状敏感核和语义分割的裂缝宽度估计

铁路衬砌部件（如隧道衬砌和轨道系统）中产生的裂缝在高铁路线上不易检测，因为白天的检查时间有限，而夜间的可见度很差。同时，对上述结构的裂缝进行严格监控，以防止可能发生的故障或事故。在这方面，通常将轨道测量车辆部署成对轨道部件成像并测量几何信息。本研究的主要目的是通过新引入的深度学习技术检测图像中的裂纹并同时测量最大裂纹宽度。为此，提出了语义分割框架内的形状敏感型内核（即裂纹状内核）和改进的深层模型。除了传统的统计模型（如准确性和交集交集）外，还通过考虑估计的裂缝宽度的箱线图和均方根误差来验证所提出模型的预测结果。根据结果​​，提出的形状敏感核函数能够比传统的语义分割模型更精确地预测一个或两个像素的裂缝宽度。未来的工作将集中在裂缝检测模型与轨道几何形状的劣化预测的集成上，以便为铁路系统的预测性维护做出系统的决策。通过考虑箱形图和估计裂缝宽度的均方根误差，验证了所提出模型的预测结果。根据结果​​，与常规语义分割模型相比，所提出的形状敏感核函数能够更精确地预测一个或两个像素的裂缝宽度。未来的工作将集中在裂缝检测模型与轨道几何形状的劣化预测的集成上，以便为铁路系统的预测性维护做出系统的决策。通过考虑箱形图和估计裂缝宽度的均方根误差，验证了所提出模型的预测结果。根据结果​​，与常规语义分割模型相比，所提出的形状敏感核函数能够更精确地预测一个或两个像素的裂缝宽度。未来的工作将集中在裂缝检测模型与轨道几何形状的劣化预测的集成上，以便为铁路系统的预测性维护做出系统的决策。