复杂光照环境下的标识线图像增强方法
Mark Line Image Enhancement Method in Complex Illumination Environment
吴静 1樊绍胜 1胡成扬1
作者信息
- 1. 长沙理工大学电气与信息工程学院 长沙 410114
- 折叠
摘要
自动驾驶汽车在行驶过程中需要识别道路标识线以确保行驶在车道上,变电站巡检机器人通过识别道路标识线实现准确巡检.但由于复杂光照环境的影响,道路标识线信息难以准确提取.传统的图像增强方法无法对所有复杂光照环境下的道路标识线图像都产生良好的增强效果,对此提出一种复杂光照环境下的道路标识线图像增强方法.利用HSV色域空间的亮度图像的亮度差进行分层处理,对高亮度差的图像使用自适应伽马校正的方法进行图像增强,对低亮度差的图像先使用直方图锥形拉伸扩大图像灰度级,再利用自适应伽马校正提升图像对比度.实验结果表明,该算法能有效解决低光照、曝光等复杂光照环境所导致的道路标识线难以识别的问题,是一种有效的图像增强方法.
Abstract
In the process of driving,autonomous vehicles need to recognize road sign lines to ensure that they stay in the lane.Substation inspection robots realize accurate inspection by recognizing road sign lines.However,due to the influence of complex lighting environment,road sign line information is difficult to be accurately extracted.However,the traditional image enhance-ment methods can not produce good enhancement effect on all road sign line images in complex lighting environment,so this pa-per proposes a road sign line image enhancement method in complex lighting environment.The luminance difference of the lumi-nance image in the HSV color gambit space is processed by layers.The image with high luminance difference is enhanced by the method of adaptive gamma correction.For the image with low luminance difference,histogram conical stretching is first used to enlarge the image gray level,and then adaptive gamma correction is used to enlarge the image contrast.Experimental results show that this algorithm can effectively solve the problem of road sign line recognition caused by low illumination,exposure and other complex lighting environment,and is an effective image enhancement method.
关键词
图像增强/伽马校正/直方图锥形拉伸/HSV色彩空间/复杂光照Key words
Image enhancement/Gamma correction/Histogram conical stretching/HSV color space/Complex illumination引用本文复制引用
出版年
2024
NETL
NSTL
万方数据