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

Abstract

PC sleepers are important components of railway tracks for stable transportation and improvement of safety. In recent years, some PC sleepers have cracks in the longitudinal direction due to the alkali-silica reaction, and the efficiency of maintenance of these PC sleepers has become an issue. Therefore, in this study, we propose a detection method using deep learning model as a method of estimating the crack position and length from the top surface image of PC sleepers taken by a camera mounted on a maintenance vehicle. As a result of examining the applicability of this method, it was confirmed that this method can estimate the position and length of cracks on PC sleepers by suppressing erroneous detection of ballast and fastening devices. In addition, it was shown that this method can be applied to identify areas with many cracks and to understand the tendency of cracks on actual commercial lines.