论文标题
FRSIGN：自动火车的大规模交通灯数据集
FRSign: A Large-Scale Traffic Light Dataset for Autonomous Trains
论文作者
论文摘要
在自动运输领域，有许多用于开放式自动驾驶汽车数据集的举措，但对于替代运输方法（例如火车），却少得多。在本文中，我们旨在通过引入FRSIGN（用于基于视觉的铁路交通灯检测和识别）的大规模和准确数据集来弥合差距。我们的录音是在法国选定的跑步火车上进行的，并受益于精心贴标签的注释。迄今为止，该数据集的说明性数据集对应于10％的获得的数据，该数据集用论文发表在开源中。它包含100,000多个图像，说明了六种类型的法国铁路交通信号灯及其可能的颜色组合，以及有关其获取的相关信息，例如日期，时间，传感器参数和边界框。该数据集以开源\ url {https://frsign.irt.irt-systemx.fr}发表。我们比较，分析数据集的各种属性，并提供指标以表达其可变性。与自动驾驶汽车相比，我们还讨论了与自主火车有关的具体挑战和特殊性。
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.
