Application of Rail Segmentation in the Monitoring of Autonomous Train's Frontal Environment

Mohamed Amine Hadded (1) , Ankur Mahtani (1) , Sébastien Ambellouis (2) , Jacques Boonaert (3) , Hazem Wannous (3)

1 IRT Railenium - L'Institut de Recherche Technologique (IRT) de la filière Ferroviaire
2 COSYS-LEOST - Laboratoire Électronique Ondes et Signaux pour les Transports
3 IMT Lille Douai - Ecole nationale supérieure Mines-Télécom Lille Douai

One of the key factors in achieving an autonomous vehicle is understanding and modeling the driving environment. This step requires a considerable amount of data acquired from a wide range of sensors. To bridge the gap between the Roadway and Railway fields in terms of datasets and experimentation, we provide a new dataset called RailSet as the second large dataset after Railsem19, specialized in Rail segmentation. In this paper we present a multiple semantic segmentation using two deep networks UNET and FRNN trained on different data configuration involving RailSet and Railsem19 datasets. We show comparable results and promising performance to be applicable in monitoring autonomous train's ego perspective view.

Mots clés: SEMANTIC SEGMENTATION; RAIL SEGMENTATION; FRONTAL TRAIN MONITORING; RAILWAY

Mots clés: TRANSPORT FERROVIAIRE; SURVEILLANCE; TRAITEMENT DES IMAGES; TRAITEMENT DES DONNEES; SEGMENTATION

Domaines: Traitement du signal et de l'image [eess.SP]

Mohamed Amine Hadded, Ankur Mahtani, Sébastien Ambellouis, Jacques Boonaert, Hazem Wannous. Application of Rail Segmentation in the Monitoring of Autonomous Train's Frontal Environment. ICPRAI 2022, International Conference on Pattern Recognition and Artificial Intelligence, Jun 2022, Paris, France. pp185-197, ⟨10.1007/978-3-031-09037-0_16⟩. ⟨hal-03875603⟩