Gomez-Donoso Francisco, Moreno-Martinez Marcos, Cazorla Miguel
University Institute for Computer Research, University of Alicante. PO Box 99, Alicante 03080, Spain.
Data Brief. 2022 Apr 14;42:108172. doi: 10.1016/j.dib.2022.108172. eCollection 2022 Jun.
In the past years, several works on urban object detection from the point of view of a person have been made. These works are intended to provide an enhanced understanding of the environment for blind and visually challenged people. The mentioned approaches mostly rely in deep learning and machine learning methods. Nonetheless, these approaches only work with direct and bright light, namely, they will only perform correctly on daylight conditions. This is because deep learning algorithms require large amounts of data and the currently available datasets do not address this matter. In this work, we propose UrOAC, a dataset of urban objects captured in a range of different lightning conditions, from bright daylight to low and poor night-time lighting conditions. In the latter, the objects are only lit by low ambient light, street lamps and headlights of passing-by vehicles. The dataset depicts the following objects: pedestrian crosswalks, green traffic lights and red traffic lights. The annotations include the category and the bounding-box of each object. This dataset could be used for improve the performance at night-time and under low-light conditions of any vision-based method that involves urban objects. For instance, guidance and object detection devices for the visually challenged or self-driving and intelligent vehicles.
在过去几年里，已经有一些从人的视角进行城市物体检测的工作。这些工作旨在为盲人和视力有障碍的人提供对环境的增强理解。上述方法大多依赖深度学习和机器学习方法。然而，这些方法仅在直射强光下有效，也就是说，它们仅在日光条件下能正确运行。这是因为深度学习算法需要大量数据，而当前可用的数据集并未解决这个问题。在这项工作中，我们提出了UrOAC，这是一个在一系列不同光照条件下捕捉的城市物体数据集，从明亮的日光到昏暗的夜间照明条件。在后者情况下，物体仅由低环境光、路灯和过往车辆的前照灯照亮。该数据集描绘了以下物体：人行横道、绿色交通信号灯和红色交通信号灯。注释包括每个物体的类别和边界框。这个数据集可用于提高任何涉及城市物体的基于视觉的方法在夜间和低光照条件下的性能。例如，用于视力有障碍者的引导和物体检测设备或自动驾驶和智能车辆。
