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.

铁路上可靠的障碍物检测可以帮助防止导致伤害、潜在的损坏或脱轨的碰撞。我们提出利用浅层网络从正常的铁路图像中学习铁路分割，并通过学习虚构无障碍图像的全局信息的受控包含来改进方法。我们在一个包含人工增强障碍物的铁路图像数据集上评估我们的方法，结果表明我们的方法优于其他基于学习的基准方法。