FRSign: A Large-Scale Traffic Light Dataset for Autonomous Trains
This addresses the problem of data scarcity for researchers and developers in autonomous train systems, though it is incremental as it extends existing dataset efforts from cars to trains.
The authors tackled the lack of open-source datasets for autonomous trains by introducing FRSign, a large-scale dataset for railway traffic light detection, containing over 100,000 images with hand-labeled annotations and six types of French railway traffic lights.
In the realm of autonomous transportation, there have been many initiatives for open-sourcing self-driving cars datasets, but much less for alternative methods of transportation such as trains. In this paper, we aim to bridge the gap by introducing FRSign, a large-scale and accurate dataset for vision-based railway traffic light detection and recognition. Our recordings were made on selected running trains in France and benefited from carefully hand-labeled annotations. An illustrative dataset which corresponds to ten percent of the acquired data to date is published in open source with the paper. It contains more than 100,000 images illustrating six types of French railway traffic lights and their possible color combinations, together with the relevant information regarding their acquisition such as date, time, sensor parameters, and bounding boxes. This dataset is published in open-source at the address \url{https://frsign.irt-systemx.fr}. We compare, analyze various properties of the dataset and provide metrics to express its variability. We also discuss specific challenges and particularities related to autonomous trains in comparison to autonomous cars.