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Journal of Real-Time Image Processing

, Volume 16, Issue 1, pp 5–14 | Cite as

Real-time vehicle type classification with deep convolutional neural networks

  • Xinchen Wang
  • Weiwei ZhangEmail author
  • Xuncheng Wu
  • Lingyun Xiao
  • Yubin Qian
  • Zhi Fang
Special Issue Paper

Abstract

Vehicle type classification technology plays an important role in the intelligent transport systems nowadays. With the development of image processing, pattern recognition and deep learning, vehicle type classification technology based on deep learning has raised increasing concern. In the last few years, convolutional neural network, especially Faster Region-convolutional neural networks (Faster R-CNN) has shown great advantages in image classification and object detection. It has superiority to traditional machine learning methods by a large margin. In this paper, a vehicle type classification system based on deep learning is proposed. The system uses Faster R-CNN to solve the task. Experimental results show that the method is not only time-saving, but also has more robustness and higher accuracy. Aimed at cars and trucks, it reached 90.65 and 90.51% accuracy. At last, we test the system on an NVDIA Jetson TK1 board with 192 CUDA cores that is envisioned to be forerunner computational brain for computer vision, robotics and self-driving cars. Experimental results show that it costs around 0.354 s to detect an image and keeps high accurate rate with the network embedded on NVDIA Jetson TK1.

Keywords

Convolutional neural network Vehicle type classification Deep learning Intelligent transportation system Object detection 

Notes

Acknowledgements

This work was supported in part by National Fund for Fundamental Research (No. 282017Y-5303), in part by the Fund of National Automobile Accident In-depth Investigation System (No. HT2016X-007), in part by National Natural Science Foundation of China (No. 51675324), in part by Training and funding Program of Shanghai College young teachers (No. ZZGCD15102), in part by Scientific Research Project of Shanghai University of Engineering Science (No. 2016-19) and in part by the Shanghai University of Engineering Science Innovation Fund for Graduate Students (No. 16KY0602).

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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Xinchen Wang
    • 1
  • Weiwei Zhang
    • 1
    Email author
  • Xuncheng Wu
    • 1
  • Lingyun Xiao
    • 2
  • Yubin Qian
    • 1
  • Zhi Fang
    • 1
  1. 1.College of Automotive EngineeringShanghai University of Engineering ScienceShanghaiChina
  2. 2.China National Institution of StandardizationBeijingChina

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