基于改进Retinex算法的雾霾天气车道线识别
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摘要
在雾霾天气下,针对常规车道线识别方法提取车道线准确性差以及多尺度Retinex算法去雾图像质量较低的缺点,提出了一种基于改进视网膜大脑皮层理论(Retinex)的雾霾天气车道线识别算法.首先,利用改进的暗通道优先算法对雾天图像进行去雾,将去雾图像作为多尺度Retinex算法的输入图像做进一步增强;然后将多尺度Retinex算法增强的图像进行亮度修正,从而获取理想去雾图像;再利用Scharr滤波器和大津法得到包含清晰道路边缘的二值化图像;最后利用Hough变换对车道线精确提取.实验表明,该算法不但能够在雾霾天气下对车道线进行准确的识别,与常规算法相比,能够有效地提高图像质量,并且具有良好的实时性,对于提高车辆主动安全性具有重大意义.
In this paper,a new algorithm based on an improved Retinex algorithm was proposed for lane detection in haze weather which couldn't be detected by traditional algorithm.Firstly,the lane images in haze weather was enhanced by an improved dark channel prior algorithm and using defogged images as the input image for MSR algrithm to have a further enhancement,ideal image was obtained after brightness enhancement.Binary images of road edges was obtained by Scharr filter and Ostu algorithm then.Finally,the road lane was extracted by Hough transform.Experimental results showed this new algorithm could not only detect road lane in haze weather accurately,but also could improve image quality effectively and has better real-time.Thus has great influence on the improvement of automobile active safety.
In this paper,a new algorithm based on an improved Retinex algorithm was proposed for lane detection in haze weather which couldn't be detected by traditional algorithm.Firstly,the lane images in haze weather was enhanced by an improved dark channel prior algorithm and using defogged images as the input image for MSR algrithm to have a further enhancement,ideal image was obtained after brightness enhancement.Binary images of road edges was obtained by Scharr filter and Ostu algorithm then.Finally,the road lane was extracted by Hough transform.Experimental results showed this new algorithm could not only detect road lane in haze weather accurately,but also could improve image quality effectively and has better real-time.Thus has great influence on the improvement of automobile active safety.
引文
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[9]肖钟捷,余文森,程仁贵,等.基于暗通道先验的快速图像去雾算法[J].吉林师范大学学报(自然科学版),2014,8(3):106-110.
[10]江兴方,王戈,沈为民.一种色彩改进型Retinex彩色图像增强方法[J].光电子·激光,2008,19(10):1402-1403.
[11]JOBSON D J,RAHMAN Z U,WOODELL G A.Properties and performance of a center/surround Retinex[J].IEEE Transactions on Image Processing,1997,6(3):451-462.
[12]杨炜.客运车辆危险行驶状态机器视觉辨识系统研究[D].西安:长安大学,2013.
[13]金辉,吴乐林,陈慧岩,等.结构化道路车道线识别的一种改进算法[J].北京理工大学学报,2007,27(6):01-505.
[14]鞠乾翱.基于机器视觉的快速车道线辨识研究[D].上海:上海交通大学,2013.
[15]杨喜宁,段建民,高德芝,等.基于改进Hough变换的车道线检测技术[J].计算机测量与控制,2010,18(2):292-294.
[2]KIM Z W.Robust lane detection and tracking in challenging scenarios[J].IEEE Transactions on Intelligent Transportation Systems,2008,9(1):16-26.
[3]刘伟,黎宁,张丹,等.基于自适应分割阈值的夜间车道标志线识别[J].计算机与数字工程,2015,43(2):308-309.
[4]陈军.基于DSP的高速公路车道偏离报警系统研究[D].天津:天津大学,2010.
[5]祝培,朱虹,钱学明,等.一种有雾天气图像景物影像的清晰化方法[J].中国图像图形学报,2004,9(1):124-128.
[6]JOBSON D J,RAHMAN Z U,WOODELL G A.Properties and performance of a center/surround Retinex[J].IEEE Transactions on Image Processing,1997,6(3):451-462.
[7]舒婷,邓波,陈炳权,等.基于暗原色先验与Retinex理论的去雾算法[J].吉首大学学报(自然科学版),2014,35(2):52-56.
[8]肖胜笔,李燕.具有颜色保真性的快速多尺度Retinex去雾算法[J].计算机工程与应用,2015,51(6):176-180.
[9]肖钟捷,余文森,程仁贵,等.基于暗通道先验的快速图像去雾算法[J].吉林师范大学学报(自然科学版),2014,8(3):106-110.
[10]江兴方,王戈,沈为民.一种色彩改进型Retinex彩色图像增强方法[J].光电子·激光,2008,19(10):1402-1403.
[11]JOBSON D J,RAHMAN Z U,WOODELL G A.Properties and performance of a center/surround Retinex[J].IEEE Transactions on Image Processing,1997,6(3):451-462.
[12]杨炜.客运车辆危险行驶状态机器视觉辨识系统研究[D].西安:长安大学,2013.
[13]金辉,吴乐林,陈慧岩,等.结构化道路车道线识别的一种改进算法[J].北京理工大学学报,2007,27(6):01-505.
[14]鞠乾翱.基于机器视觉的快速车道线辨识研究[D].上海:上海交通大学,2013.
[15]杨喜宁,段建民,高德芝,等.基于改进Hough变换的车道线检测技术[J].计算机测量与控制,2010,18(2):292-294.