International Journal of Automotive Technology

The Korean Society of Automotive Engineers (한국자동차공학회)
권호 표지

A FUZZY NEURAL NETWORK-BASED DECISION OF ROAD IMAGE QUALITY FOR THE EXTRACTION OF LANE-RELATED INFORMATION

  • YI U. K. (Department of Digital Animation, Busan Kyungsang College) ;
  • LEE J. W. (Department of Industrial Engineering, Automotive Research Center, Chonnam National University) ;
  • BAEK K. R. (Department of Electronics Engineering, Research Institute of Computer Information and Communication, Pusan National University)
  • Published : 2005.02.01
Download PDF
⟨ Previous Next ⟩

Abstract

We propose a fuzzy neural network (FNN) theory capable of deciding the quality of a road image prior to extracting lane-related information. The accuracy of lane-related information obtained by image processing depends on the quality of the raw images, which can be classified as good or bad according to how visible the lane marks on the images are. Enhancing the accuracy of the information by an image-processing algorithm is limited due to noise corruption which makes image processing difficult. The FNN, on the other hand, decides whether road images are good or bad with respect to the degree of noise corruption. A cumulative distribution function (CDF), a function of edge histogram, is utilized to extract input parameters from the FNN according to the fact that the shape of the CDF is deeply correlated to the road image quality. A suitability analysis shows that this deep correlation exists between the parameters and the image quality. The input pattern vector of the FNN consists of nine parameters in which eight parameters are from the CDF and one is from the intensity distribution of raw images. Experimental results showed that the proposed FNN system was quite successful. We carried out simulations with real images taken in various lighting and weather conditions, and obtained successful decision-making about $99\%$ of the time.

Keywords

References

  1. Aoki, M. (1998). Image processing in ITS. Proc. IEEE Intelligent Vehicles '98, Germany, 1-4
  2. Bertozzi, M. Broggi, A. and Fascioli, A. (2000). Visionbased intelligent vehicles: State of the art and perspectives. Robotics and Autonomous Systems 32, 1-16 https://doi.org/10.1016/S0921-8890(99)00125-6
  3. Duda, R. O. and Hart, P. E. (1973). Pattern Classification and Scene Analysis. Jone Wiley & Sons. New York
  4. Gonzalez, R. G. and Woods, R. E. (1992). Digital Image Processing. Addison-Wesley. Reading. Massachusetts
  5. Horikawa, S. Furuhashi, T. and Uchikawa, Y. (1992). On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm. IEEE Trans. Neural Networks 3, 5, 801-806 https://doi.org/10.1109/72.159069
  6. Lee, J. W. Kim, K. S. Jeong, S. S. and Jeon, Y. W (2000). Lane departure warning system: Its logic and on-board equipment (20005331). Proc. lSAE, Japan, 9-11
  7. Kreucher, C. and Lakshmanan, S. (1999). A frequency domain approach to lane detection on roadway images. Proc. 1999 Int. Conl on Image Processing 2,31-35
  8. Lee, J. W (2002). A machine vision system for lanedeparture detection. CVIU 86, 1, 52-78 https://doi.org/10.1006/cviu.2002.0958
  9. Lee, J. W, Yi, U. K. and Baek, K. R. (2001). A cumulative distribution function of edge direction for road-lane detection. IEICE Trans. Information and Systems E84-D, 9,1206-1216
  10. Lin, C. T. and Lee, C. S. G. (1996). Neural Fuzzy Systems. Prentice Hall. New Jersey
  11. Maki, Y. and Laparo, K. A. (1997). A neural-network approach to fault detection and diagnosis in industrial process. IEEE Trans. on Control Systems 5, 6, 529-54l https://doi.org/10.1109/87.641399
  12. Ozawa, S. (1999). Image processing for intelligent transport systems. IEICE Trans. Information and Systems E82-D, 3, 629-636
  13. Takahashi, A. Ninomiya, Y. Ohta, M. and Tange, K. (1999). A robust lane detection using real-time voting processor. Proc. IEEE/IEE1/1SAI Int. Conf on Intelligent Transportation Systems, 577-580