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Small traffic sign detection from large image

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Abstract

Automatic traffic sign detection has great potential for intelligent vehicles. The ability to detect small traffic signs in large traffic scenes enhances the safety of intelligent devices. However, small object detection is a challenging problem in computer vision; the main problem involved in accurate traffic sign detection is the small size of the signs. In this paper, we present a deconvolution region-based convolutional neural network (DR-CNN) to cope with this problem. This method first adds a deconvolution layer and a normalization layer to the output of the convolution layer. It concatenates the features of the different layers into a fused feature map to provide sufficient information for small traffic sign detection. To improve training effectiveness and distinguish hard negative samples from easy positive ones, we propose a two-stage adaptive classification loss function for region proposal networks (RPN) and fully connected neural networks within DR-CNN. Finally, we evaluate our proposed method on the new and challenging Tsinghua-Tencent 100K dataset. We further conduct ablation experiments and analyse the effectiveness of the fused feature map and the two-stage classification loss function. The final experimental results demonstrate the superiority of the proposed method for detecting small traffic signs.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 61502094, Grant 51774090, and Grant 51104030, in part by the Natural Science Foundation of HeiLongjiang Province under Grant F2016002, Grant E2016008, and Grant F2015020, in part by the Youth Science Foundation of Northeast Petroleum University under Grant 2017PYZL-06 and Grant 2018YDL-22.

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Authors and Affiliations

  1. School of Computer and Information Technology, Northeast Petroleum University, Daqing, China

    Zhigang Liu & Feng Tian

  2. Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, China

    Dongyu Li

  3. Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore

    Shuzhi Sam Ge

  4. Institute for Future (IFF), Qingdao University, Qingdao, China

    Shuzhi Sam Ge

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  1. Zhigang Liu
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  2. Dongyu Li
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  3. Shuzhi Sam Ge
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  4. Feng Tian
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Correspondence to Zhigang Liu.

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Liu, Z., Li, D., Ge, S.S. et al. Small traffic sign detection from large image. Appl Intell 50, 1–13 (2020). https://doi.org/10.1007/s10489-019-01511-7

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  • DOI: https://doi.org/10.1007/s10489-019-01511-7

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