@article{ref1,
title="A novel algorithm for detecting pedestrians on rainy image",
journal="Sensors (Basel)",
year="2021",
author="Liu, Yuhang and Ma, Jianxiao and Wang, Yuchen and Zong, Chenhong",
volume="21",
number="1",
pages="e112-e112",
abstract="Pedestrian detection is widely used in cooperative vehicle infrastructure systems. Traditional pedestrian detection methods perform sufficiently well under sunny  scenarios and obtain trustworthy traffic data. However, the detection drastically  decreases under rainy scenarios. This study proposes a pedestrian detection  algorithm with a de-raining module that improves detection accuracy under various  rainy scenarios. Specifically, this algorithm determines the density information of  rain and effectively removes rain streaks through the de-raining module. Then the  algorithm detects pedestrians as a pair of keypoints through the pedestrian  detection module to solve the problem of occlusion. Furthermore, a new pedestrian  dataset containing rain density labels is established and used to train the  algorithm. For the scenarios of light, medium, and heavy rain, extensive experiments  on synthetic datasets demonstrate that the proposed algorithm increases AP (average  precision) of pedestrian detection by 21.1%, 48.1%, and 60.9%. Moreover, the  proposed algorithm performs well on real datasets and achieves improvements over the  state-of-the-art methods, which reveals that the proposed algorithm can  significantly improve the accuracy of pedestrian detection in rainy scenarios.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s21010112",
url="http://dx.doi.org/10.3390/s21010112"
}