@article{ref1,
title="Traffic sign detection using a multi-scale recurrent attention network",
journal="IEEE transactions on intelligent transportation systems",
year="2019",
author="Tian, Yan and Gelernter, Judith and Wang, Xun and Li, Jianyuan and Yu, Yizhou",
volume="20",
number="12",
pages="4466-4475",
abstract="Traffic sign detection plays an important role in intelligent transportation systems. But traffic signs are still not well-detected by deep convolution neural network-based methods because the sizes of their feature maps are constrained, and the environmental context information has not been fully exploited by other researchers. What we need is a way to incorporate relevant context detail from the neighboring layers into the detection architecture. We have developed a novel traffic sign detection approach based on recurrent attention for multi-scale analysis and use of local context in the image. Experiments on the German traffic sign detection benchmark and the Tsinghua-Tencent 100K data set demonstrated that our approach obtained an accuracy comparable to the state-of-the-art approaches in traffic sign detection.<p /> <p>Language: en</p>",
language="en",
issn="1524-9050",
doi="10.1109/TITS.2018.2886283",
url="http://dx.doi.org/10.1109/TITS.2018.2886283"
}