Nighttime Traffic Sign and Pedestrian Detection Using RefineDet with Time‐Series Information
Masaya Yamamoto, Rebeka Sultana, Gosuke Ohashi · IEEJ Transactions on Electrical and Electronic Engineering · 2022
Abstract Object detection is one of the most important tasks in computer vision‐based automation, such as advanced driver assistance systems in driving automation. It is preferable to detect traffic‐related objects at a far distance that appear small in the recorded scene in order to ensure maximum road safety while driving. As drivers tend to miss more traffic‐related objects at nighttime driving, this work focuses on nighttime in‐vehicle camera images. Because videos were recorded using an in‐vehicle camera, objects to be detected in this study, such as traffic signs and pedestrians, occupy a small size in the frame when far away from the own vehicle. Furthermore, it is necessary to take into account time‐series information to detect objects in sequential frames. Therefore, this research proposes an object detection model that combines the RefineDet small object detection model and the TSSD video detection model. Experimental results confirm the effectiveness of the proposed model. Moreover, a publicly available benchmark dataset is used to confirm the performance of the proposed model regardless of daytime or nighttime images. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.