SynDRA: Synthetic Dataset for Railway Applications

Gianluca D'Amico, Federico Nesti, Giulio Rossolini, Mauro Marinoni, Salvatore Sabina, Giorgio Buttazzo; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 3437-3446

The use of deep learning techniques in railway environments faces significant obstacles especially for computer vision tasks. Such obstacles are mainly due to the inherent safety concerns required for installing the proper equipment on a train and the substantial effort required to precisely annotate large datasets especially for segmentation tasks. Public datasets of real-world images are quite scarce and suffer from severe limitations such as coarse manual annotation or narrow range of scenarios. In addition real-world datasets often do not contain scenes that represent critical situations. To address such limitations this paper introduces SynDRA a synthetic dataset of photo-realistic images generated using a railway simulator built on Unreal Engine 5. SynDRA offers precise pixel-level annotations across diverse scenarios thereby facilitating more effective testing and training of deep learning models for semantic segmentation tasks in railway settings. The advantages of the proposed dataset are validated through a series of experiments that highlight the potential of SynDRA to enhance the performance of deep learning models in scenarios where real-world annotated data is scarce. The dataset is publicly available at the following link: https://syndra.retis.santannapisa.it.