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Neural Computing and Applications

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04-03-2019 | Multi-Source Data Understanding (MSDU)

Traffic sign detection and recognition based on pyramidal convolutional networks

Authors: Zhenwen Liang, Jie Shao, Dongyang Zhang, Lianli Gao

Published in: Neural Computing and Applications | Issue 11/2020

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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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Title: Traffic sign detection and recognition based on pyramidal convolutional networks
Authors: Zhenwen Liang
Jie Shao
Dongyang Zhang
Lianli Gao
Publication date: 04-03-2019
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 11/2020
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-019-04086-z

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