复杂光照下的缩微道路车道线检测方法

计算机科学 ›› 2014, Vol. 41 ›› Issue (3): 46-49.

复杂光照下的缩微道路车道线检测方法

罗强,王国胤,储卫东

计算智能重庆市重点实验室重庆400065;计算智能重庆市重点实验室重庆400065;计算智能重庆市重点实验室重庆400065

出版日期:2018-11-14 发布日期:2018-11-14
基金资助:
本文受国家自然科学基金(61073146,0)资助

Lane Detection in Micro-traffic under Complex Illumination

LUO Qiang,WANG Guo-yin and CHU Wei-dong

Online:2018-11-14 Published:2018-11-14

摘要/Abstract

摘要： 为了解决复杂光照下的缩微道路车道线检测问题,提出了一种适用于复杂光照下的缩微道路车道线检测方法。首先运用朴素贝叶斯对不同光照下的车道图像进行分类,然后对分类后的车道图像采取相应的图像处理方法,最后运用改进的大津法和改进概率霍夫变换来检测车道线。对多段不同光照模式下的车道图像进行的仿真实验表明,该方法对缩微道路车道线检测的成功率达到95.5%,具有很强的鲁棒性和抗干扰能力。

关键词: 智能交通系统,朴素贝叶斯,大津法,改进概率霍夫变换中图法分类号TP39文献标识码A

Abstract: In order to resolve the problem of lane detection in Micro-traffic under complex illumination,a lane detection method was proposed for the Micro-traffic under complex illumination．Firstly,the lane images for different illumination are classified by using Naive Bayes．Then,the classified lane images are proceed by using the corresponding image processing method.Finally,improved Otsu and revised Probabilistic Hough Transform are introduced to detect lanes．Simulation experiment on the different lane images under different illumination shows that the lane detection success rate in Micro-traffic is up to 95.5% and the method possesses strong robustness and anti-interference．

Key words: Intelligent transportation system(ITS),Naive bayes,Otsu,PPHT

罗强,王国胤,储卫东. 复杂光照下的缩微道路车道线检测方法[J]. 计算机科学, 2014, 41(3): 46-49. https://doi.org/

LUO Qiang,WANG Guo-yin and CHU Wei-dong. Lane Detection in Micro-traffic under Complex Illumination[J]. Computer Science, 2014, 41(3): 46-49. https://doi.org/

参考文献

[1] Wang Guo-yin,Chen Qiao-song,Wang Jin.Discussion on the Intelligent Vehicle Technologies[J]．Computer Science,2012,39(5):1-8
[2] Lakhoyia A,Golconda S,Maida A,et al．CajunBot:Architecture and Algorithms[M]．Springer Tracts in Advanced Robotics,2007,36:245-280
[3] Bruce J,Balch T,Veloso M.Fast and Inexpensive Color ImageSegmentation for Interactive Robots[J]．IEEE/RSJ InternationalConference on Intelligent Robots and Systems,2000(10):2061-2066
[4] Ji Xue-jiao．Vision-based Autonomous Driving in Miniature Traf-fic Environment[D]．Beijing:Tsinghua University,2011
[5] Ma Yu-lin,Yan Xin-ping,Wu qing.Research on Intelligent Vehicle Platoon Driving Simulation Experiment System under the Coordination between Vehicle and Highway[J]．Joural of Computers,2010,5(11):1767-1768
[6] Chu Wei-dong,Wang Guo-yi,Wang Jin．Lane Detection in Micro-Traffic Environment[J]．Journal of Frontiers of Computer Science and Technology,2012,6(10):921-926
[7] Zhao Jia-jia．The Design of Illumination Classifier for Lanes[D]．Jilin:Jilin University,2007
[8] Huang Zhi-kai,Liu De-hui．Segmentation of Color Image Using EM algorithm in HSV Color Space[C]∥Proceedings of the 2007International Conference on Information Acquisition,2007．Jeju,Korea,2007:316-319
[9] Bradski G,Kaebler A.Learning OpenCV Computer Vision with the OpenCV Library[M]．O’Reilly Media,2008
[10] 贾永红.计算机图像处理与分析[M]．武汉:武汉大学出版社,2001
[11] Otsu N．A threshold selection method from gray-level histo-gram．IEEE Transactions on Systems[J]．Man and Cybernetics,1979,9(1):62-66
[12] Wang Zhi,He Sai-xian．An Adaptive Edge-detection MethodBased on Canny Algorithm[J]．Journal of Image and Graphics,2004,9(8):957-961
[13] Galamhos C,Matas J,Kittler J.Progressive Probablistic Hough Transform for line detection[C]∥Proceedings of Computer Vision and Pattern Recognition．1999:23-25
[14] http://cs.cqupt.edu.cn/MemberReadNews.asp?NewsID=113-99&SpecialID=0

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed