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Lane Detection Algorithm Based on Inverse Perspective Mapping

  • Dong Chen
  • Zonghao Tian
  • Xiaolong ZhangEmail author
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 576)

Abstract

The lane detection and recognition is very important in unmanned driving technology. In order to improve the accuracy and robustness of lane detection and overcome the influence of changes in illumination, curvature, and road interference, a lane detection algorithm based on reverse perspective mapping is established. The binarization image of a lane with less noise is obtained by the global optimal threshold method. Then through the reverse perspective mapping, the binary lane image was converted into the top view to overcome the shortcomings of different resolution and geometric deformation of the image caused by the perspective effect. Then, the lane images transformed by reverse perspective were clustered and fitted by k-mean algorithm, and the clear lane detection results were obtained. Finally, by analyzing the lane detection of road images under different imaging conditions, the robustness of the lane detection algorithm under the conditions of high curvature, large change in brightness, and multiple interference factors were verified.

Keywords

Inverse perspective mapping Lane detection Image binarization K-means 

References

  1. 1.
    Fang MX (2017) Researches on intelligent traffic light based on machine vision. University of Electronic Science and Technology of China, ChenduGoogle Scholar
  2. 2.
    Zhang R, Wang H, Zhou X et al (2012) Lane detection algorithm at night based on distribution feature of boundary dots for vehicle active safety. Inf Technol J 11(5):642–646CrossRefGoogle Scholar
  3. 3.
    Fang H, Jia R, Lu J (2010) Segmentation of full vision images based on color and texture features. J Beijing Inst Technol 30(8):935–939Google Scholar
  4. 4.
    Wang Y, Teoh EK, Shen D (2004) Lane detection and tracking using B-Snake. Image Vis Comput 22(4):269–280CrossRefGoogle Scholar
  5. 5.
    Gao F, Jiang D, Xu G et al (2012) A 3d curve lane detection and tracking system based on stereovision. CICTP 1247–1258Google Scholar
  6. 6.
    Gualain DO, Hughes C, Glavin M et al (2012) Automotive standards grade lane departure warning system. IET Intel Transp Syst 6(1):44–57CrossRefGoogle Scholar
  7. 7.
    Zhang Z (2010) Digital image processing and machine vision. People’s Posts and Telecommunications Publishing House, BeijingGoogle Scholar
  8. 8.
    Ostu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66CrossRefGoogle Scholar
  9. 9.
    Liu XJ (2017) Research on lane detection and recognition algorithm under complex road image. Henan University of Technology, ZhengzhouGoogle Scholar
  10. 10.
    Hang YG, Yang JH (2009) Lane detection based on inverse perspective mapping and hough transform. J Yunnan Univ 31(1):104–108Google Scholar
  11. 11.
    Bertozzi M, Broggi A, Conte G (2000) Vision based automated vehicle guidance: the experience of the ARGO vehicle. Real Time Imaging 6(4):313–324CrossRefGoogle Scholar
  12. 12.
    Zhou SB, Xu ZY, Tang XQ (2010) New method for determining optimal number of clusters in K –means clustering algorithm. Comput. Eng. Appl. 46(16):27–31Google Scholar
  13. 13.
    Tao Y, Yang F, Liu Y, Dai B (2018) Research and optimization of K-means clustering algorithm. Comput Technol Dev 6(28):91–93Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Army Academy of Artillery and Air DefenseHefeiChina

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