Road Detection by Boundary Extraction Technique and Hough Transform

  • Namboodiri Sandhya Parameswaran
  • E. Revathi Achan
  • V. Subhashree
  • R. ManjushaEmail author
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)


Visual perception of road images captured by cameras mounted within a vehicle is the main element of an autonomous vehicle system. Road detection plays a vital role in a visual routing system for a self-governing vehicle. Effective detection of roads under varying illumination conditions plays a vital role to prevent majority of the road accidents that occur currently. In the current study, a new method using “boundary extraction” technique along with “Hough transform” is proposed for effective road detection. Here, two different algorithms, one using “Canny edge detection” and “Hough transform” and another using “boundary extraction” technique and “Hough transform” were implemented and tested on the same dataset. The comparison of the results of both the techniques showed that the algorithm using “boundary extraction” technique worked better than that which used “Canny edge” detection technique.


Image processing Boundary extraction Hough transform Canny edge detection Image processing 


  1. 1.
    Kumar D, Kaur G (2008) Lane detection techniques: a review. IEEE Trans Intell Transp Syst 9(1):16–26CrossRefGoogle Scholar
  2. 2.
    Rasmussen C (2004) Texture-based vanishing point voting for road shape estimation. In: BMVC, pp 1–10Google Scholar
  3. 3.
    Kong H, Audibert JY, PonceJ (2009) Vanishing point detection for road detection. In: IEEE conference on computer vision and pattern recognition, 2009. CVPR 2009. IEEE, pp 96–103Google Scholar
  4. 4.
    Aly M (2008) Real time detection of lane markers in urban streets. In: Intelligent vehicles symposium, 2008 IEEE, pp 7–12Google Scholar
  5. 5.
    Saha A, Roy DD, Alam T, Deb K (2012) Automated road lane detection for intelligent vehicles. Glob J Computer Sci TechnolGoogle Scholar
  6. 6.
    Hu H, Gu Q, Zhou J (2010) HTF: a novel feature for general crack detection. In: 2010 17th IEEE international conference on image processing (ICIP). IEEE, pp 1633–1636Google Scholar
  7. 7.
    Danti A, Kulkarni JY, Hiremath PS (2012) An image processing approach to detect lanes, pot holes and recognize road signs in Indian roads. Int J Model Optim 2(6):658CrossRefGoogle Scholar
  8. 8.
    Cui D, Xue J, Zheng N (2016) Real-time global localization of robotic cars in lane level via lane marking detection and shape registration. IEEE Trans Intell Transp Syst 17(4):1039–1050CrossRefGoogle Scholar
  9. 9.
    Moewes C, Kruse R (2011) On the usefulness of fuzzy SVMs and the extraction of fuzzy rules from SVMs. In: EUSFLAT Conf., pp 943–948Google Scholar
  10. 10.
    Guan J, An F, Zhang X, Chen L, Mattausch HJ (2017) Parallelization of Hough transform for high-speed straight-line detection in XGA-size videos. In: 2017 IEEE international conference on consumer electronics-Taiwan (ICCE-TW). IEEE, pp 313–314Google Scholar
  11. 11.
    Aminuddin NS, Masrullizam MI, Ali NM, Radzi SA, Saad WHM, Darsono AM (2017) A new approach to highway lane detection by using hough transform technique. J Inf Commun Technol 16(2):244Google Scholar
  12. 12.
    Fernandes LAF, Oliveira MM (2008) Real-time line detection through an improved Hough transform voting scheme. Pattern Recogn 41(1):299–314CrossRefGoogle Scholar
  13. 13.
    Hari CV et al (2009) Mid-point hough transform: a fast line detection method. In: India conference (INDICON), 2009 Annual IEEEGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Namboodiri Sandhya Parameswaran
    • 1
  • E. Revathi Achan
    • 1
  • V. Subhashree
    • 1
  • R. Manjusha
    • 1
    Email author
  1. 1.Department of Computer Science and EngineeringAmrita School of EngineeringCoimbatoreIndia

Personalised recommendations