Estimation of crack width based on shape‐sensitive kernels and semantic segmentation

Abstract

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.