Research on vehicle type identification method based on supervised Kohonen network

JI Ye; LI Yu-hui; LI Bo; YANG Min

doi:10.13873/J.1000-9787(2016)08-0012-03

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Research on vehicle type identification method based on supervised Kohonen network

更新时间：2025-09-11

- Research on vehicle type identification method based on supervised Kohonen network
- Vol. 35, Issue 8, Pages: 12-14+19(2016)
- 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(2016)08-0012-03
  CLC： TP391.41
- Published：2016
- 稿件说明：
移动端阅览
[1]纪野,李玉惠,李勃,杨敏.有监督Kohonen网络的车型识别方法[J].传感器与微系统,2016,35(08):12-14+19.

JI Ye, LI Yu-hui, LI Bo, et al. Research on vehicle type identification method based on supervised Kohonen network[J]. 2016, 35(8): 12-14+19.
[1]纪野,李玉惠,李勃,杨敏.有监督Kohonen网络的车型识别方法[J].传感器与微系统,2016,35(08):12-14+19. DOI： 10.13873/J.1000-9787(2016)08-0012-03.

JI Ye, LI Yu-hui, LI Bo, et al. Research on vehicle type identification method based on supervised Kohonen network[J]. 2016, 35(8): 12-14+19. DOI： 10.13873/J.1000-9787(2016)08-0012-03.

摘要

车型识别已成为智能交通系统研究中的一个重要内容。根据同类车型尺寸特征如长、宽、高特征值都具有相似性特点

利用已有车型数据库

分别使用逆传播（BP）神经网络、支持向量机（SVM）网络、有监督Kohonen网络这三种神经网络分类方法对车型尺寸特征进行分类

得到三个车型识别准确率并进行比较验证。实验结果表明:有监督Kohonen网络的分类方法具有较高的车型识别精度

实验效果明显

车型识别准确率高。

Abstract

Vehicle type identification has become an important research content in intelligent transportation systems. According to the characteristics of size similar vehicle models such as long

width

height features value are similar characteristics

using the existing vehicle models database

respectively using three neural network classification methods

such as BP neural network

SVM network

supervised Kohonen network

to classify features of vehicle type size

obtain identification accuracy rate of three vehicle type and comparison and verification are carried out. Experimental results show that the classification method of supervised Kohonen network has high precision of vehicle identification

the experimental effect is obvious

and the accuracy rate of vehicle type identification is high.

关键词

Keywords

references

有监督的Kohonen神经网络聚类算法在癌症诊断中的应用 [J]. 马卫,马全富. 微电子学与计算机 . 2014(12)

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)

车辆的Harris与SIFT特征及车型识别 [J]. 康维新,曹宇亭,盛卓,李鹏,姜澎. 哈尔滨理工大学学报 . 2012(03)

SVM分类器的扩展及其应用研究 [D]. 梁燕. 湖南大学 2008

MATLAB神经网络30个案例分析[M]. 北京航空航天大学出版社 , MATLAB中文论坛, 2010

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