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.


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    Title :

    Traffic Sign Detection Using a Multi-Scale Recurrent Attention Network


    Contributors:
    Tian, Yan (author) / Gelernter, Judith (author) / Wang, Xun (author) / Li, Jianyuan (author) / Yu, Yizhou (author)


    Publication date :

    2019-12-01


    Size :

    3367140 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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