基于自适应分割阈值的夜间车道标识线识别
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摘要
针对夜间获取的车道图像对比度低的问题,提出了一种基于自适应阈值分割的夜间车道标识线识别算法。首先,对预处理后的图像进行分块拉伸以增强边缘信息。再结合Otsu门限法和邻域中值法,通过加权分配获取自适应阈值,对车道图像进行分割。最后,采用分区域搜索方式,利用Hough变换精确地提取车道标识线。现场实测表明,针对结构化道路的车道线,论文采用的车道线提取方法准确率高且实时性好。
For the low contrast of the lane images obtained from the night,a nighttime lane markings recognition based on adaptive threshold segmentation is proposed.The pre-processing images are first blocked and stretched for edge enhancement.An adaptive threshold,determined by Otsu' thresholding and neighborhood median method,is used to segment lane images.The lane markings are finally extracted by using Hough transform via sub-region searching.The estimation shows that the proposed lane recognition has high accuracy and good real-time processing for structured road extraction.
For the low contrast of the lane images obtained from the night,a nighttime lane markings recognition based on adaptive threshold segmentation is proposed.The pre-processing images are first blocked and stretched for edge enhancement.An adaptive threshold,determined by Otsu' thresholding and neighborhood median method,is used to segment lane images.The lane markings are finally extracted by using Hough transform via sub-region searching.The estimation shows that the proposed lane recognition has high accuracy and good real-time processing for structured road extraction.
引文
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[2]Wang Y,Shen D,Teoh E K.Lane detection using spline model[J].Pattern Recognition Letters,2000,21(8):677-689.
[3]Wang Y,Teoh E K,Shen D.Lane detection and tracking using B-Snake[J].Image and Vision Computing,2004,22(4):269-280.
[4]Kim Z W.Robust lane detection and tracking in challenging scenarios[J].Intelligent Transportation Systems,IEEE Transactions on,2008,9(1):16-26.
[5]王荣本,游峰,崔高健,等.基于计算机视觉高速智能车辆的道路识别[J].计算机工程与应用,2004,26(7):18-21.WANG Rongben,YOU Feng,CUI Gaojian,et al.The Detection for High Speed Intelligent Vehicle Based on Machine Vision[J].Computer Engineering and Application,2004,26(7):18-21.
[6]Yu B,Zhang W,Cai Y.A lane departure warning system based on machine vision[C]//Computational Intelligence and Industrial Application,2008.PACIIA08.Pacific-Asia Workshop on.IEEE,2008,1:197-201.
[7]马兆敏,齐保谦,廖凤依,等.基于神经网络的车道偏移自动检测的研究[J].科学技术与工程,2012,20(30):8097-8099.MA Zhaomin,QI Baoqian,LIAO Fengyi,et al.The Research of Detection Automatically for Lane Departure Based on Neural Network[J].Science Technology and Engineering,2012,20(30):8097-8099.
[8]李晗.基于机器视觉的高速车道标志线检测算法的研究[D].沈阳:东北大学,2006.LI Han.The Research on detection of speedway lane based on machine vision[D].Shenyang:Northeastern University,2006.
[9]黄海港,沈永增,闫纪如.基于优化Prewitt的高速夜间车道检测算法[J].计算机系统应用,2013(8).HUANG Haigang,SHEN Yongzeng,YAN Jiru.Nighttime Lane Detection of the Highway Based on Improved Prewitt[J].Computer Systems&Applications,2013(8).
[10]谭立勋,刘缠牢,李春燕.实时图像处理中Sobel算子的改进[J].弹箭与制导学报,2006(S1).TAN Lixun,LIU Chanlao,LI Chunyan.An ImProved Sobel Algorithmin Real-time Image Processing[J].Journal of Projectiles,Rockets,Missiles and Guidance,2006(S1).
[11]张娟,周建江,黎宁.基于单目视觉的道路自主导航模型车[J].信号处理,2007,23(2):309-313.ZHANG Juan,ZHOU Jianjiang,LI Ning.Road-traveling Model Car Based on Monocular Vision[J].Signal Processing,2007,23(2):309-313.
[12]Otsu N.A threshold selection method from gray-level histograms[J].Automatica,1975,11(285-296):23-27.
[13]陈芳,姚建刚,杨迎建,等.灰度拉伸及边缘扫描在红外图像分割中的应用[J].计算机工程与应用,2009,45(14):167-169.CHEN Fang,YAO Jiangang,YANG Yingjian,et al.Segmentation algorithm of infrared image based on gray stretching and edge scanning[J].Computer Engineering and Application,2009,45(14):167-169.
[14]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):501-505.JIN Hui,WU Lelin,CHEN Huiyan,et al.An improved Algorithm of the Lane Recognition of Structured Road[J].Transactions of Beijing Institute of Technology,2007,27(6):501-505.
[15]白冰洁,韩峻峰,潘盛辉,等.基于双阈值分割的车道线检测方法[J].信息技术,2013(3):43-45.BAI Bingjie,HAN Junfeng,PAN Shenghui,et al.Lane detection based on dual-threshold segmentation[J].Information Technology,2013(3):43-45.
[16]杨宏云.复杂光照条件下的车道偏离预警[J].科技信息,2014(6):68-69.YANG Hongyun.Lane Departure Warning Based on Complex Illumination Condition[J].Journal of Information Science and Technology,2014(6):68-69.