Citation

Tian Y, Gelernter J, Wang X, Li J, Yu Y. IEEE Trans. Intel. Transp. Syst. 2019; 20(12): 4466-4475.

Copyright

(Copyright © 2019, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/TITS.2018.2886283

PMID

unavailable

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.

Language: en