TY  - JOUR
PY  - 2019//
TI  - Traffic sign detection using a multi-scale recurrent attention network
JO  - IEEE transactions on intelligent transportation systems
A1  - Tian, Yan
A1  - Gelernter, Judith
A1  - Wang, Xun
A1  - Li, Jianyuan
A1  - Yu, Yizhou
SP  - 4466
EP  - 4475
VL  - 20
IS  - 12
N2  - 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>
LA  - en
SN  - 1524-9050
UR  - http://dx.doi.org/10.1109/TITS.2018.2886283
ID  - ref1
ER  -