基于LDA与混沌粒子群算法的车道线识别方法研究

黄新; 刘璋

doi:10.3788/YJYXS20173206.0491

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基于LDA与混沌粒子群算法的车道线识别方法研究

图像处理 | 更新时间：2020-07-07

- 基于LDA与混沌粒子群算法的车道线识别方法研究
- Lane detection by LDA and chaos particle swarm optimization
- 液晶与显示 2017年32卷第6期页码：491-498
- 作者机构：
  
  桂林电子科技大学电子工程与自动化学院, 广西桂林 541004
- 作者简介：
  
  [ "黄新(1978-), 男, 湖北黄冈人, 硕士, 副教授, 主要研究方向:可测性设计, 图像处理。Email:hxgl@guet.edu.cn" ]
- 基金信息：
  
  广西自动检测技术与仪器重点实验室主任基金项目(YQ14105);广西省科学研究与技术开发计划资助项目(桂科攻：11107001-40);Guangxi Key Laboratory of Automatic Detecting Technology and Instruments(YQ14105);Scientific Research and Technology Development Program of Guangxi Province(桂科攻: 11107001-40)
- DOI：10.3788/YJYXS20173206.0491
  中图分类号： TP394.1;TH691.9
- 收稿日期：2016-12-27，
  
  录用日期：2017-3-13，
  
  纸质出版日期：2017-06-05
- 稿件说明：
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黄新, 刘璋. 基于LDA与混沌粒子群算法的车道线识别方法研究[J]. 液晶与显示, 2017,32(6):491-498.

Xin HUANG, Zhang LIU. Lane detection by LDA and chaos particle swarm optimization[J]. Chinese journal of liquid crystals and displays, 2017, 32(6): 491-498.
黄新, 刘璋. 基于LDA与混沌粒子群算法的车道线识别方法研究[J]. 液晶与显示, 2017,32(6):491-498. DOI： 10.3788/YJYXS20173206.0491.

Xin HUANG, Zhang LIU. Lane detection by LDA and chaos particle swarm optimization[J]. Chinese journal of liquid crystals and displays, 2017, 32(6): 491-498. DOI： 10.3788/YJYXS20173206.0491.

摘要

与传统的车道线检测算法不同，本文采用LDA算法对道路图像进行针对性灰度化处理。加大车道线与道路的差异，然后使用抛物线模型对车道线进行拟合，采用混沌粒子群算法对抛物线参数进行优化，以车道线的灰度特征和梯度特征作为混沌粒子群的适应度函数，经过多次的迭代得到抛物线拟合车道线的参数最优值，进而识别出车道线。实验结果表明，本文算法能在复杂环境下识别出车道线，对视频帧序列中的车道线连续追踪具有良好效果。

Abstract

The method proposed in this paper is different from the traditional road lane detection algorithm. Firstly

LDA(Linear Discriminant Analysis) is used to convert road images to gray images directly

increasing the difference between the lane and the road. Secondly

the road lane is fitted by parabola model

and CPSO(Chaos Particle Swarm Optimization) is applied to optimize the parameters of parabolic model. The CPSO fitness function consists of features of grayscale and gradient. After several iterations

the optimal parameter value of parabola can be obtained

and then the road lane in the image can be detected out. Experiment results show that the proposed algorithm could detect road lane in complex environment. Also

it has a good performance on road lane tracking in the video frame sequence.

关键词

Keywords

references

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