Research on vehicle type recognition method based on convolutional neural network

JI Ye; LI Yu-hui; WANG Meng

doi:10.13873/j.1000-9787(2017)11-0042-02

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Research on vehicle type recognition method based on convolutional neural network

更新时间：2025-09-11

- Research on vehicle type recognition method based on convolutional neural network
- Vol. 36, Issue 11, Pages: 42-43+46(2017)
- 作者机构：
  
  1. 昆明理工大学信息工程与自动化学院
  2. 云南省智能交通系统工程技术研究中心智能图像处理研究室
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-9787(2017)11-0042-02
  CLC： TP183;TP391.41
- Published：2017
- 稿件说明：
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[1]纪野,李玉惠,王蒙.基于卷积神经网络的车型识别方法研究[J].传感器与微系统,2017,36(11):42-43+46.

JI Ye, LI Yu-hui, WANG Meng. Research on vehicle type recognition method based on convolutional neural network[J]. 2017, 36(11): 42-43+46.
[1]纪野,李玉惠,王蒙.基于卷积神经网络的车型识别方法研究[J].传感器与微系统,2017,36(11):42-43+46. DOI： 10.13873/j.1000-9787(2017)11-0042-02.

JI Ye, LI Yu-hui, WANG Meng. Research on vehicle type recognition method based on convolutional neural network[J]. 2017, 36(11): 42-43+46. DOI： 10.13873/j.1000-9787(2017)11-0042-02.

摘要

针对高速公路环境下的车型识别问题

利用深度学习Caffe框架中的Alex Net和传统方法支持向量机（SVM）

分别对车辆图像进行网络训练与测试

得到两种车型识别方法的准确率并进行比较验证。实验结果表明:卷积神经网络的分类方法具有较高的车型识别精度

车型识别准确率高。

Abstract

Aiming at vehicle type identification problem in highway environment

use Caffe Alex Net in deep learning framework and the traditional method of support vector machine（ SVM）

vehicle images are trained and tested

get accuracy of two vehicle models recognition method is carried out comparison and verification.

关键词

Keywords

references

有监督Kohonen网络的车型识别方法 [J]. 纪野,李玉惠,李勃,杨敏. 传感器与微系统 . 2016(08)

基于深度卷积神经网络的车型识别研究 [J]. 邓柳,汪子杰. 计算机应用研究 . 2016(03)

基于SVM的ECG传感器信号身份识别方法 [J]. 陈曦,陈冠雄,沈海斌. 传感器与微系统 . 2014(10)

Backpropagation Applied to Handwritten Zip Code Recognition [J] . Y. LeCun,B. Boser,J. S. Denker,D. Henderson,R. E. Howard,W. Hubbard,L. D. Jackel. Neural Computation . 1989 (4)

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