Fundamental Study on Crack Detection Method for Prestressed Concrete Sleepers Using Deep Learning Model

Prestressed concrete sleepers are an important component of railway tracks, contributing to the speed and safety of train operations. Cracks appearing in the longitudinal direction of some prestressed concrete sleepers in recent years due to alkali-silica reactions have raised concerns about the efficiency of their maintenance. Therefore, this study proposes the use of a deep learning model to estimate the position and length of cracks on top surface images of prestressed concrete sleepers, as captured by a camera mounted on a maintenance vehicle. The applicability test confirmed that the method can accurately estimate the position and length of cracks in prestressed concrete sleepers, while minimizing the likelihood of false detection of ballast and fastening devices. In addition, it was demonstrated that this method can be employed to identify areas with a high concentration of cracks and analyze crack patterns on commercial lines.