Deep Neural Networks for Railway Switch Detection and Classification Using Onboard Camera

Abstract

Recent years have seen major advances in Artificial Intelligence (AI) methods for environment perception in intelligent transportation systems. Although most of them have been achieved in the automotive sector there is a similar demand in the railway domain. This paper investigates Deep Neural Network (DNN) based environment perception using vehicle-borne camera images from the rail domain. Specifically, railway switch detection and classification are addressed as a relevant example for a DNN application with potential use for landmark positioning, environment perception, and condition monitoring. The lack of large training data sets in the railway sector (in contrast to the automotive domain) is compensated by an appropriate DNN architecture, an anchor box ratio optimization scheme, and transfer learning. The presented experimental results advocate for the adopted approach.