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.
Traffic Sign Detection Using a Multi-Scale Recurrent Attention Network
IEEE Transactions on Intelligent Transportation Systems ; 20 , 12 ; 4466-4475
2019-12-01
3367140 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Multi-scale traffic sign detection model with attention
SAGE Publications | 2021
|Scale-variant traffic sign detection
SPIE | 2019
|Traffic sign recognition using weighted multi‐convolutional neural network
Wiley | 2018
|