ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2020, Vol. 57 ›› Issue (5): 1022-1036.doi: 10.7544/issn1000-1239.2020.20190445

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A Fast Traffic Sign Detection Algorithm Based on Three-Scale Nested Residual Structures

Li Xudong, Zhang Jianming, Xie Zhipeng, Wang Jin   

  1. (School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114) (Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation (Changsha University of Science and Technology), Changsha 410114)
  • Online:2020-05-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (61972056, 61811530332), the Natural Science Foundation of Hunan Province of China (2019JJ50666), the “Double First-class” International Cooperation and Development Scientific Research Project of Changsha University of Science and Technology (2019IC34), the Postgraduate Training Innovation Base Construction Project of Hunan Province (2019-248-51), and the Postgraduate Scientific Research Innovation Fund of Hunan Province (CX20190695).

Abstract: Automatic driving technology has high requirements for real-time and robustness of traffic sign detection in real world. The YOLOv3-tiny model is a lightweight network with good real-time performance in the object detection, but its accuracy is not high. In this paper, we use YOLOv3-tiny as the basic network and propose a fast traffic sign detection algorithm with three-scale nested residual structure. Firstly, shortcut based on pixel by pixel addition is employed in the YOLOv3-tiny network. It does not increase the number of feature map channels, and a small residual structure is formed in the network at the same time. Secondly, the predictive output with higher spatial resolution is also added through the shortcut, which contains more abundant spatial information, thus forming a large residual structure. Finally, the two residual structures are nested to form a three-scale predictive nested residual network, which makes the main network of Tiny located in these two residual structures and the parameters can be adjusted three times. The results show that the proposed algorithm can quickly and robustly detect traffic signs in real scenes. The F\-1 value of total traffic signs achieves 91.77% on German traffic sign detection benchmark and the detection time is 5ms. On CSUST Chinese traffic sign detection benchmark, F\-1 values of the Mandatory, the Prohibitory and the Warning are 92.41%, 93.91% and 92.03% respectively, and the detection time is 5ms.

Key words: traffic sign detection, you only look once (YOLO) detection algorithm, nested residual network, multi-scale prediction, Changsha University of Science and Technology (CSUST), CSUST Chinese traffic sign detection benchmark (CCTSDB)

CLC Number: