Applied Sciences (Oct 2022)

Object-Level Data Augmentation for Deep Learning-Based Obstacle Detection in Railways

  • Marten Franke,
  • Vaishnavi Gopinath,
  • Danijela Ristić-Durrant,
  • Kai Michels

Journal volume & issue
Vol. 12, no. 20
p. 10625


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This paper presents a novel method for generation of synthetic images of obstacles on and near rail tracks over long-range distances. The main goal is to augment the dataset for autonomous obstacle detection (OD) in railways, by inclusion of synthetic images that reflect the specific need for long-range OD in rail transport. The presented method includes a novel deep learning (DL)-based rail track detection that enables context- and scale-aware obstacle-level data augmentation. The augmented dataset is used for retraining of a state-of-the-art CNN for object detection. The evaluation results demonstrate significant improvement of detection of distant objects by augmentation of training dataset with synthetic images.