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Traffic sign detection and recognition based on pyramidal convolutional networks

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Abstract

With the development of driverless technology, we are in dire need of a method to understand traffic scenes. However, it is still a difficult task to detect traffic signs because of the tiny scale of signs in real-world images. In complex scenarios, some traffic signs could be very elusive due to the awful weather and lighting conditions. To implement a more comprehensive detection and recognition system, we develop a two-stage network. At the region proposal stage, we adopt a deep feature pyramid architecture with lateral connections, which makes the semantic feature of small object more sensitive. At the classification stage, densely connected convolutional network is used to strengthen the feature transmission and multiplexed, which leads to more accurate classification with less number of parameters. We test on GTSDB detection benchmark, as well as the challenging Tsinghua-Tencent 100K benchmark which is pretty difficult for most traditional networks. Experiments show that our proposed method achieves a very great performance and surpasses the other state-of-the-art methods. Implementation source code is available at https://github.com/derderking/Traffic-Sign.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grants Nos. 61672133, 61832001, 61632007).

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Authors and Affiliations

  1. Center for Future Media, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Zhenwen Liang, Jie Shao, Dongyang Zhang & Lianli Gao

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  1. Zhenwen Liang
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  2. Jie Shao
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  3. Dongyang Zhang
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  4. Lianli Gao
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Correspondence to Jie Shao.

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Liang, Z., Shao, J., Zhang, D. et al. Traffic sign detection and recognition based on pyramidal convolutional networks. Neural Comput & Applic 32, 6533–6543 (2020). https://doi.org/10.1007/s00521-019-04086-z

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