Experimental investigations of a convolutional neural network model for detecting railway track anomalies

Convolutional neural networks (CNN) have been utilized to detect anomalies on the railway track surfaces whose conditions must be monitored to ensure the safety of railway systems. While CNN has advantages over conventional image processing methods in self-learning features for detecting railway track anomalies, the CNN model and parameters still need to be carefully constructed and examined for the effective application with railway track images. This study presents a systematic investigation of CNN model parameters for detecting anomalies on railway tracks. Parameters such as number of convolutional layers, convolutional kernel size, pooling kernel size and number of epochs were examined. Experiments and analyses were performed to determine how these parameters affect the detection accuracy. The experimental procedures and findings demonstrated the effects of individual parameters, as well as the potential interactions between the factors; thus, systematic procedures are needed to investigate and improve CNN models deployed to detect and classify anomalies on railway tracks.