  Reliable obstacle detection on railways could help prevent collisions that
result in injuries and potentially damage or derail the train. Unfortunately,
generic object detectors do not have enough classes to account for all possible
scenarios, and datasets featuring objects on railways are challenging to
obtain. We propose utilizing a shallow network to learn railway segmentation
from normal railway images. The limited receptive field of the network prevents
overconfident predictions and allows the network to focus on the locally very
distinct and repetitive patterns of the railway environment. Additionally, we
explore the controlled inclusion of global information by learning to
hallucinate obstacle-free images. We evaluate our method on a custom dataset
featuring railway images with artificially augmented obstacles. Our proposed
method outperforms other learning-based baseline methods.
