基于卷积神经网络多特征联合的车辆识别模型

计算机科学 ›› 2019, Vol. 46 ›› Issue (6A): 254-258.

基于卷积神经网络多特征联合的车辆识别模型

刘泽康, 孙华志, 马春梅, 姜丽芬

天津师范大学计算机与信息工程学院天津300387

出版日期:2019-06-14 发布日期:2019-07-02
通讯作者: 孙华志男,博士,教授,主要研究方向为人工智能、模式识别、机器学习等,E-mail:sunhuazhi@tjnu.edu.cn
作者简介:刘泽康男,硕士生,主要研究方向为图像处理、模式识别,E-mail:lzk100953@163.com;马春梅博士,讲师,主要研究方向为人工智能、群智感知、智能交通等;姜丽芬女,博士,教授,主要研究方向为人工智能、普适计算、机器学习等。
基金资助:
本文受国家自然科学基金(61702370),天津市国际科技合作项目(14RCGFGX00847),天津市自然科学基金(17JCYBJC16400),天津市科技计划项目(17ZLZXZF00530),天津师范大学131三层次人选(043/135305QS20),天津师范大学博士基金(043/135202XB1615,043/135202XB1705)资助。

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

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

College of Computer and Information Engineering,Tianjin Normal University,Tianjin 300387,China

Online:2019-06-14 Published:2019-07-02

摘要/Abstract

摘要： 车辆识别在智能交通领域中发挥着重要的作用,其可被用于违章抓拍、交通拥堵报警和自动驾驶等众多领域。文中提出结合车辆边缘联合建模的方法进行车辆识别。边缘联合卷积神经网络(E-CNN)通过简单有效的多特征联合方法提高了识别精度和模型收敛速度。为了验证E-CNN的性能,将多特征联合模型与VGG16和GoogLeNet模型进行对比。实验结果表明,所提模型的收敛速度相比VGG16和GoogLeNet有明显的优势,并且在有效时间内识别率达到了99.90%,高于VGG16的99.82%和GoogLeNet的99.35%。

关键词: 边缘联合卷积神经网络, 边缘特征, 车辆识别, 特征融合

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

刘泽康, 孙华志, 马春梅, 姜丽芬. 基于卷积神经网络多特征联合的车辆识别模型[J]. 计算机科学, 2019, 46(6A): 254-258. https://doi.org/

LIU Ze-kang, SUN Hua-zhi, MA Chun-mei, JIANG Li-fen. Vehicle Recognition Model Based on Multi-feature Combination inConvolutional Neural Network[J]. Computer Science, 2019, 46(6A): 254-258. https://doi.org/

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