@article{ref1,
title="Real-time traffic sign recognition based on a general purpose GPU and deep-learning",
journal="PLoS one",
year="2017",
author="Lim, Kwangyong and Hong, Yongwon and Choi, Yeongwoo and Byun, Hyeran",
volume="12",
number="3",
pages="e0173317-e0173317",
abstract="We present a General Purpose Graphics Processing Unit (GPGPU) based real-time traffic sign detection and recognition method that is robust against illumination changes. There have been many approaches to traffic sign recognition in various research fields; however, previous approaches faced several limitations when under low illumination or wide variance of light conditions. To overcome these drawbacks and improve processing speeds, we propose a method that 1) is robust against illumination changes, 2) uses GPGPU-based real-time traffic sign detection, and 3) performs region detecting and recognition using a hierarchical model. This method produces stable results in low illumination environments. Both detection and hierarchical recognition are performed in real-time, and the proposed method achieves 0.97 F1-score on our collective dataset, which uses the Vienna convention traffic rules (Germany and South Korea).<p /> <p>Language: en</p>",
language="en",
issn="1932-6203",
doi="10.1371/journal.pone.0173317",
url="http://dx.doi.org/10.1371/journal.pone.0173317"
}