Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Determination of Road Image Quality Using Fuzzy-Neural Network

퍼지신경망을 이용한 도로 영상의 양불량 판정

  • 이운근 (부산대학교 전자공학과) ;
  • 백광렬 (부산대학교 전자전기정보컴퓨터공학부) ;
  • 이준웅 (전남대학교 산업공학과)
  • Published : 2002.06.01
Download PDF
⟨ Previous Next ⟩

Abstract

The confidence of information from image processing depends on the original image quality. Enhancing the confidence by an algorithm has an essential limitation. Especially, road images are exposed to lots of noisy sources, which makes image processing difficult. We, in this paper, propose a FNN (fuzzy-neural network) capable oi deciding the quality of a road image prior to extracting lane-related information. According to the decision by the FNN, road images are classified into good or bad to extract lane-related information. A CDF (cumulative distribution function), a function of edge histogram, is utilized to construct input parameters of the FNN, it is based on the fact that the shape of the CDF and the image quality has large correlation. Input pattern vector to the FNN consists of ten parameters in which nine parameters are from the CDF and the other one is from intensity distribution of raw image. Correlation analysis shows that each parameter represents the image quality well. According to the experimental results, the proposed FNN system was quite successful. We carried out simulations with real images taken by various lighting and weather conditions and achieved about 99% successful decision-making.

Keywords

References

  1. S. Ozawa, 'Image processing for intelligent tracsport systems,' IEICE Trans. Information and Systems, vol. E82-D, no. 3, pp. 629-636, 1999
  2. M. Bertozzi, A. Broggi and A. Fascioli, 'Vision-based interlligent vehicles : State of the art and perspectives,' Robotics and Autonomous Systems, vol. 32, pp. 1-16, 2000 https://doi.org/10.1016/S0921-8890(99)00125-6
  3. M. Aoki, 'Image processing in ITS,' Proc. IEEE Intelligent Vehicles '98, pp.1-4, 1998
  4. J. W. Lee, K. S. Kim, S. S. Jeong, and Y. W. Jeon, 'Lane departure warning system : Its logic and on-board equipment(20005331),' Proc. JSAE,pp. 9-11, Japan, 2000
  5. A. Takahashi, Y. Ninomiya, M. Ohta, and K. Tange, 'A robust lane detection using real-time voting processor,' Proc. IEEE/IEE.JSAI Int. Conf. on Intelligent Transportation Systems, pp.577-580, 1999 https://doi.org/10.1109/ITSC.1999.821123
  6. C. Kreucher and S. Lakshmanan, 'A frequency domain approach to lane detection on roadway images,' Proc. 1999 Int. Conf. on Image Processing, vol. 2, pp. 31-35, 1999 https://doi.org/10.1109/ICIP.1999.822849
  7. J. W. Lee, U. K. Yi, and K. R. Baek, 'A Cumulative distribution function of edge direction for road-lane detection,' IEICE Trans. Information and Systems, vol. E84-D, no. 9, pp. 1206-1216, 2001
  8. R. O. Duda, P. E. Hart, Pattern Classification and Scene Analysis, Jone Wiley & Sons, Inc., 1973
  9. C. T. Lin and C. S. George Lee, Neural Fuzzy Systems, Prentice Hall, Inc., 1996
  10. S. Horikawa, T. Furuhashi and Y. Uchikawa, 'On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm,' IEEE Trans. Neural Networks, vol. 3, no. 5, pp. 801-806, 1992 https://doi.org/10.1109/72.159069
  11. Y. Maki and K. A. Laparo, 'A neural-network approach to fault detection and diagnosis in industrial process,' IEEE Trans. on Control Systems, vol. 5, no. 6, pp. 529-541, 1997 https://doi.org/10.1109/87.641399
  12. R. G. Gnzalez and R. E. Woods, Digital Image Processing, Addison-Wesley, Reading, Massachusetts, 1992