Computer Science

Computer Science ›› 2019, Vol. 46 ›› Issue (6A): 254-258.

• Pattern Recognition & Image Processing • Previous Articles     Next Articles

Vehicle Recognition Model Based on Multi-feature Combination inConvolutional Neural Network

LIU Ze-kang, SUN Hua-zhi, MA Chun-mei, JIANG Li-fen   

  1. College of Computer and Information Engineering,Tianjin Normal University,Tianjin 300387,China
  • Online:2019-06-14 Published:2019-07-02

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Abstract: Vehicle recognition plays an important role in intelligent transportation,which can be used in many fields such as illegal snapping,traffic jam warning,and automatic driving,etc.This paper proposed a joint model that combines vehicle edge(E-CNN) to identify vehicles.The simple and effective feature combining not only improves the recognition accuracy,but also accelerates the convergence speed of the model.In order to verify the performance of E-CNN,the multi-features combination model was compared with the model of VGG16 and GoogLetNet.The experimental results show that the convergence speed of the proposed model has obvious advantages compared with VGG16 and GoogLeNet.Further more,the recognition accuracy of the proposed model is up to 99.90%,which is higher than 99.82% of VGG16 and 99.35% of GoogLeNet.

Key words: E-CNN, Edge features, Feature fusion, Vehicle identification

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