Deep Learning for Detection of Railway Signs and Signals

Abstract

Major railway lines need advance management systems based on accurate maps of their infrastructure. Asset detection is an important tool towards automation of processes and improved decision support on such systems. Due to lack of available data, limited research exists investigating railway asset detection, despite the rise of Artificial Neural Networks and the numerous investigations on autonomous driving. Here, we present a novel dataset used in real world projects for mapping railway assets. Also, we implement Faster R-CNN, a state of the art deep learning object detection method, for detection of signs and signals on this dataset. We achieved 79.36% on detection and a 70.9% mAP. The results were compromised by the small size of the objects, the low resolution of the images and the high similarity across classes.