Recently,the development and application of lane line departure warning systems have been in the market.For any of the systems,the key part of lane line tracking,lane line identification,or lane line departure warning...Recently,the development and application of lane line departure warning systems have been in the market.For any of the systems,the key part of lane line tracking,lane line identification,or lane line departure warning is whether it can accurately and quickly detect lane lines.Since 1990 s,they have been studied and implemented for the situations defined by the good viewing conditions and the clear lane markings on road.After then,the accuracy for particular situations,the robustness for a wide range of scenarios,time efficiency and integration into higher-order tasks define visual lane line detection and tracking as a continuing research subject.At present,these kinds of lane marking line detection methods based on machine vision and image processing can be divided into two categories:the traditional image processing and semantic segmentation(includes deep learning)methods.The former mainly involves feature-based and model-based steps,and which can be classified into similarity-and discontinuity-based ones;and the model-based step includes different parametric straight line,curve or pattern models.The semantic segmentation includes different machine learning,neural network and deep learning methods,which is the new trend for the research and application of lane line departure warning systems.This paper describes and analyzes the lane line departure warning systems,image processing algorithms and semantic segmentation methods for lane line detection.展开更多
基于传统暗原色先验原理的图像去雾算法存在的“halo”效应,且图像中明亮区域存在颜色失真现象,针对此问题,本文提出了多尺度窗口的自适应透射率修复交通图像去雾方法。首先,利用新的8方向边缘检测算子求取图像中景深突变区域,根据暗通...基于传统暗原色先验原理的图像去雾算法存在的“halo”效应,且图像中明亮区域存在颜色失真现象,针对此问题,本文提出了多尺度窗口的自适应透射率修复交通图像去雾方法。首先,利用新的8方向边缘检测算子求取图像中景深突变区域,根据暗通道先验理论和前一步求得的景深突变区域,在景深变化较大区域使用5 X 5的窗口,景深变化较小区域则使用15 x 15的窗口得到暗原色估计图。同时,针对暗通道先验原理对近景部分存在白色区域时透射率估计不准确的问题,引人了自适应透射率修复方法,通过引导滤波器得到边缘增强后的暗原色图像,并利用其与原暗原色图像的纹理差对近景区域的透射率进行修正,完成图像去雾。实验结果表明:双边滤波和梯度双边滤波两种算法均存在halo现象,并且在包含白色物体的明亮区域色彩失真严重,客观评价指标失去意义;相比于引导滤波,本文去雾算法的各项指标均有所提高,其中平均梯度平均提高了8.305%,PSNR平均提高了12.455%,边缘强度因子平均提高了7.77%。本文算法有效解决了复原图像中“halo”效应现象和明亮区域颜色失真现象,去雾效果最优。展开更多
基金financially supported by the National Natural Science Foundation of China(grant No.61170147)the Scientific and Technological Project of Shaanxi Province in China(grant No.2019GY-038)。
文摘Recently,the development and application of lane line departure warning systems have been in the market.For any of the systems,the key part of lane line tracking,lane line identification,or lane line departure warning is whether it can accurately and quickly detect lane lines.Since 1990 s,they have been studied and implemented for the situations defined by the good viewing conditions and the clear lane markings on road.After then,the accuracy for particular situations,the robustness for a wide range of scenarios,time efficiency and integration into higher-order tasks define visual lane line detection and tracking as a continuing research subject.At present,these kinds of lane marking line detection methods based on machine vision and image processing can be divided into two categories:the traditional image processing and semantic segmentation(includes deep learning)methods.The former mainly involves feature-based and model-based steps,and which can be classified into similarity-and discontinuity-based ones;and the model-based step includes different parametric straight line,curve or pattern models.The semantic segmentation includes different machine learning,neural network and deep learning methods,which is the new trend for the research and application of lane line departure warning systems.This paper describes and analyzes the lane line departure warning systems,image processing algorithms and semantic segmentation methods for lane line detection.
文摘基于传统暗原色先验原理的图像去雾算法存在的“halo”效应,且图像中明亮区域存在颜色失真现象,针对此问题,本文提出了多尺度窗口的自适应透射率修复交通图像去雾方法。首先,利用新的8方向边缘检测算子求取图像中景深突变区域,根据暗通道先验理论和前一步求得的景深突变区域,在景深变化较大区域使用5 X 5的窗口,景深变化较小区域则使用15 x 15的窗口得到暗原色估计图。同时,针对暗通道先验原理对近景部分存在白色区域时透射率估计不准确的问题,引人了自适应透射率修复方法,通过引导滤波器得到边缘增强后的暗原色图像,并利用其与原暗原色图像的纹理差对近景区域的透射率进行修正,完成图像去雾。实验结果表明:双边滤波和梯度双边滤波两种算法均存在halo现象,并且在包含白色物体的明亮区域色彩失真严重,客观评价指标失去意义;相比于引导滤波,本文去雾算法的各项指标均有所提高,其中平均梯度平均提高了8.305%,PSNR平均提高了12.455%,边缘强度因子平均提高了7.77%。本文算法有效解决了复原图像中“halo”效应现象和明亮区域颜色失真现象,去雾效果最优。
