雾霾天气下交通图像的边缘检测算子

黄鹤; 宋京; 盛广峰; 许哲; 张懿璞

doi:10.3788/OPE.20172513.0244

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雾霾天气下交通图像的边缘检测算子

信息科学 | 更新时间：2020-08-13

- 雾霾天气下交通图像的边缘检测算子
- Detection operator for traffic image in hazy weather
- 光学精密工程 2017年25卷第10s期页码：244-249
- 作者机构：
  
  1. 长安大学电子与控制工程学院, 陕西西安 710064
  2. 陕西省道路交通智能检测与装备工程技术研究中心, 陕西西安 710064
  3. 中电科第二十研究所, 陕西西安 710068
- 作者简介：
- 基金信息：
  
  国家自然科学基金青年科学基金资助项目（No.51505037，No.61402052，No.61203233），陕西省自然科学基础研究计划资助项目（No.2016JQ6
- DOI：10.3788/OPE.20172513.0244
  中图分类号： TP391.41
- 收稿日期：2017-06-28，
  
  修回日期：2017-07-10，
  
  纸质出版日期：2017-11-25
- 稿件说明：
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黄鹤, 宋京, 盛广峰等. 雾霾天气下交通图像的边缘检测算子[J]. 光学精密工程, 2017,25(10s): 244-249

HUANG He, SONG Jing, SHENG Guang-feng etc. Detection operator for traffic image in hazy weather[J]. Editorial Office of Optics and Precision Engineering, 2017,25(10s): 244-249
黄鹤, 宋京, 盛广峰等. 雾霾天气下交通图像的边缘检测算子[J]. 光学精密工程, 2017,25(10s): 244-249 DOI： 10.3788/OPE.20172513.0244.

HUANG He, SONG Jing, SHENG Guang-feng etc. Detection operator for traffic image in hazy weather[J]. Editorial Office of Optics and Precision Engineering, 2017,25(10s): 244-249 DOI： 10.3788/OPE.20172513.0244.

摘要

针对现有图像边缘检测方法难以实现对雾霾天气下交通图像完整提取边缘的问题，提出了一种新的雾霾天气下交通图像边缘检测方法。首先利用新提出的8方向边缘检测算子来提取交通图像的粗边缘，接着结合雾霾图像的特点，通过增加部分方向像素点的权重，克服了雾霾颗粒折射所引起的离焦模糊，通过调整权值，设计新的图像边缘细化算子对得到的图像边缘进行细化处理，减少假边缘的出现，最后将细化后的图像边缘输出显示。实验结果表明：与已有的边缘检测方法相比，本文提出的方法能够检测到雾霾天气下交通图像的大多数边缘细节，同时得到比较细的图像边缘，区分度更好，对图像边缘定位比较准确，且得到的图像边缘强度较传统方法提升了50%以上，有效提高了雾霾天气下边缘检测的效果。

Abstract

A new edge detection method of traffic image in hazy weather was proposed pertinent to the problem that complete edge extraction cannot be achieved for traffic image in hazy weather in current edge detection method of image. Coarse edge of traffic image was extracted with newly proposed eight-direction edge detection operators pertinent to edge detection for traffic image in hazy weather

then weight of pixel points in partial directions were increased to overcome defocus blur due to refraction of haze particles in combination with features of hazy images; weight value was adjusted to design new refining operators of image edge so as to conduct refinement treatment on image edge and reduce quantity of fake edges; finally refined image edge was output for display. Experimental result indicates that most edge details of traffic image in hazy weather can be detected in the method proposed and more fine image edge can be achieved simultaneously compared with existing edge detection methods; it has better distinction degree and more accurate image edge positioning; image edge strength increases by over 50% compared with traditional method

thus effect of edge detection in hazy weather is effectively increased.

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references

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