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.

翻译：可靠的铁路轨道障碍物检测有助于预防导致人员受伤、列车损坏或脱轨的碰撞事故。然而，通用目标检测器无法覆盖所有可能场景的类别，且包含铁轨上物体的数据集难以获取。我们提出利用浅层网络从正常铁路图像中学习轨道分割。网络的有限感受野可防止过度自信的预测，使其专注于铁路环境中局部特征明显且重复的模式。此外，我们通过学习幻觉无障碍图像来探索全局信息的可控融合。我们在包含人工增强障碍物的定制铁路图像数据集上评估了该方法。实验结果表明，所提方法优于其他基于学习的基线方法。