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.
Keywords: Low-light conditions; Object recognition; Urban environments.
© 2022 The Author(s). Published by Elsevier Inc.