RailNet: An information aggregation network for rail track segmentation. IEEE International Joint Conference on Neural Networks

Haoran Li, Dongbin Zhao, Yaran Chen

Abstract: As the basis of scenes understanding for the track inspection task, track segmentation is challenging due to the various illumination conditions, track crossing, and plant coverage. Since the rail has a strong shape prior, strict rail spacing and special distribution in the image, making full use of the spatial information of the rail features becomes an important factor to improve the accuracy of rail segmentation. In this paper, an information aggregation module is proposed to enhance the spatial relationship between pixels of the rail features. In other words, this module expands the receptive field. Furthermore, we build an information aggregation network based on this module, which is called as RailNet. Finally, the RailNet is evaluated in an open train track dataset. Experimental results show that RailNet can achieve the best performance so far in the dataset of trains.