Enhancing safety and operational efficiency in railway systems benefits from robust AI-powered perception, particularly for reliable obstacle and pedestrian detection. However, the prevalent black-box nature of contemporary deep learning models presents significant challenges for verification and trust, especially within safety-critical railway environments characterised by dynamic weather, illumination changes, and high speed, which create undesirable effects such as cluttered backgrounds, and motion blur, which may hinder the performance of computer vision approaches. This paper addresses the need for more transparent models by proposing the application of Prototypical Part Networks (ProtoPNet) for interpretable obstacle and pedestrian classification within the railway domain. Experiments with the OSDaR23 dataset demonstrate that training with a careful selection of data augmentation processes enhances key metrics such as precision, recall and F1-score while yielding transparent results with visually robust prototypes.