[1]鲁 曼,蔡自兴,李 仪.道路区域分割的车道线检测方法[J].智能系统学报,2010,5(6):505-509.
LU Man,CAI Zi-xing,LI Yi.A lane detection method based on road segmentation[J].CAAI Transactions on Intelligent Systems,2010,5(6):505-509.
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LU Man,CAI Zi-xing,LI Yi.A lane detection method based on road segmentation[J].CAAI Transactions on Intelligent Systems,2010,5(6):505-509.
道路区域分割的车道线检测方法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
5
期数:
2010年第6期
页码:
505-509
栏目:
学术论文—机器感知与模式识别
出版日期:
2010-12-25
- Title:
- A lane detection method based on road segmentation
- 文章编号:
- 1673-4785(2010)06-0505-05
- 分类号:
- TP391.4
- 文献标志码:
- A
- 摘要:
- 为了满足无人驾驶车在高速公路行驶的实时性和鲁棒性要求，提出了一种基于道路区域分割的车道线检测方法.该方法分道路区域分割和车道线检测2个阶段.在道路区域分割阶段，首先提取的道路颜色值，然后在二值边缘图像中搜索连通域，通过将连通域的颜色特征值与道路颜色特征值比较来快速定位道路区域，并将这一区域划定为车道线检测的感兴趣区域.车道线检测阶段则使用改进的概率Hough变换方法提取车道线点，并使用最小二乘法对车道线点集进行拟合，获得车道线模型的参数.实验证明该方法相比传统的利用标准Hough变换算法准确率提升23%，有效地排除了道路区域外的直线像素干扰，具备较好的鲁棒性和实时性.
- Abstract:
- A lane detection method was developed based on road segmentation to satisfy the realtime and robustness requirements of an automated vehicle when driven on the highway. The proposed method was divided into two phases of road segmentation and lane detection. After extracting the color feature of the highway, the road segmentation phase quickly located the road region through searching connected regions of binary edge images and comparing the color feature of connected regions with the highway, and then set the road region as the region of interest (ROI) for lane detection. An improved probabilistic Hough transformation method was used to extract the lane pixels in the lane detection phase, and the least squares method was used for fitting the lane pixels to get the parameters of the lane model. Experiments show that the accuracy is improved 23% by the proposed method compared with the conventional method using standard Hough transformation, and linear pixels outside the road region were effectively ruled out. The proposed method has high robustness and realtime performance.
- 参考文献/References:
-
［1］TASI J F, HUANG S S, CHAN Y M. Road detection and classification in urban environments using conditional random field models［C］//Proceedings of 2006 IEEE Intelligent Transportation Systems Conference. Toronto, Canada, 2006: 963967.
［2］HSIAO P Y, YEH C W, HUANG S S, et al. A portable visionbased realtime lane departure warning system: day and night［J］. IEEE Transactions on Vehicular Technology, 2009, 4(58): 20892094.
［3］GUO Y, BAI Z, LIU Y. Genetic algorithm and region growing based road detection in SAR images［C］//IEEE 2007 3rd International Conference on Natural Computation. Haikou, China, 2007(4): 330334.
［4］LI Q, ZHENG N N, CHENG H. Springrobot: a prototype autonomous vehicle and its algorithms for lane detection［J］. IEEE Transactions on Intelligent Transportation Systems, 2004, 5(4): 300308.
［5］张荣, 王勇, 杨榕. TM图像中道路目标识别方法的研究［J］. 遥感学报， 2005, 9(2)： 220224.
ZHANG Rong, WANG Yong, YANG Rong. Researches on road recognition in landsat TM images［J］. Journal of Remote Sensing, 2005, 9(2): 220224.
［6］HE Y, WANG H, ZHANG B. Colorbased road detection in urban traffic scenes［J］. IEEE Transaction on Intelligent Transportation Systems, 2000, 12(5): 309317.
［7］谢贵, 彭嘉雄. 基于上下边缘点匹配的连通域搜索算法［J］. 华中科技大学学报：自然科学版, 2002, 30(4): 6468.
XIE Gui, PENG Jiaxiong. An algorithm for searching connective regions based on matching between the points of upcontour and downcontour［J］. Journal of Huangzhong University of Science and Technology: Natural Science Edition, 2002, 30(4): 6468.
［8］HUANG D, WANG C. Optimal multilevel thresholding using a twostage Otsu optimization approach［J］. Pattern Recognition Letters, 2009, 30(2): 275284.
［9］ASSUDIQ A A M, KHALIFA O O, ISLAM R, et al. Real time lane detection for autonomous vehicles［C］// Proceedings of the International Conference on Computer and CommunicationEngineering 2008. Kuala Lumpur, Malaysia, 2008:8288.
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备注/Memo
收稿日期：2009-11-15.
基金项目：国家自然科学基金资助项目(90820302，60805027)；国家博士点基金资助项目(200805330005)；湖南省院士基金资助项目(2009FJ4030).
通信作者：鲁 曼. E-mail: sophia.luman@gmail.com.
作者简介：
鲁 曼，女，1986年生，硕士研究生，主要研究方向为图像处理与机器视觉.
?蔡自兴，男，1938年生，教授，博士生导师, 国际导航与运动控制科学院院士、中国人工智能学会副理事长、中国自动化学会理事.主要研究方向为人工智能、机器人、智能控制,发表学术论文500余篇.
李 仪，男，1979年生，讲师，博士，主要研究方向为智能车系统、机器视觉等.
更新日期/Last Update:
2011-03-03