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Driver Assistance in Fog Environment Based on Convolutional Neural Networks (CNN)

  • Allach SamirEmail author
  • Ben Ahmed Mohamed
  • Boudhir Anouar Abdelhakim
Conference paper
Part of the Lecture Notes in Intelligent Transportation and Infrastructure book series (LNITI)

Abstract

Driver Assistance Systems (ADAS) are designed to assist the driver and improve road safety. For this, various sensors are generally embedded in vehicles to alert the driver in case of danger present on the road. Unfortunately, the performance of such systems degrades in the presence of adverse weather conditions. In addition, eliminating the fog of a single image captured by a camera is a very difficult and ill-posed phenomenon in Advanced Driver Assistance Systems (ADAS). Recent developments in the field of deep learning have allowed researchers to build relevant models using various tools available. We propose in this paper a new architecture based on fast R-CNN for the detection of objects in fogged images, and a convolutional neuron network (CNN) is designed on the basis of a reformulated model of atmospheric diffusion for fog elimination to restore the sharp image.

Keywords

ADAS CNN Fast R-CNN Fog New architecture 

References

  1. 1.
    Tan, R.T.: Visibility in bad weather from a single image. In: IEEE Conference on Computer Vision and Pattern Recognition, 2008. CVPR 2008, pp. 1–8. IEEE (2008)Google Scholar
  2. 2.
    Narasimhan, S.G., Nayar, S.K.: Vision and the atmosphere. Int. J. Comput. Vis. 48(3), 233–254 (2002)CrossRefGoogle Scholar
  3. 3.
    Pavlic, M., Belzner, H., Rogoll, G., Ilic, S.: Image based fog detection in vehicles. IEEE Intelligent Vehicles Symposium, pp. 1132–1137 (2012)Google Scholar
  4. 4.
    Alami, S., Ezzine, A., Elhassouni, F.: Local fog detection based on saturation and RGB-correlation. In: Proceedings of the IEEE International Conference Computer Graphics, Imaging and Visualization, pp. 1–5, Mar 2016Google Scholar
  5. 5.
    McCartney, E.J.: Optics of the atmosphere: scattering by molecules and particles. Wiley, New York, p 421 (1976)Google Scholar
  6. 6.
    Narasimhan, S.G., Nayar, S.K.: Chromatic framework for vision in bad weather. In: IEEE Conference on Computer Vision and Pattern Recognition, 2000. Proceedings, vol. 1, pp. 598–605. IEEE (2000)Google Scholar
  7. 7.
    Narasimhan, S.G., Nayar, S.K.: Vision and the atmosphereGoogle Scholar
  8. 8.
    Fattal, R.: Single image dehazing. ACM Trans. Graph (TOG) 27(3), 72 (2008)CrossRefGoogle Scholar
  9. 9.
    He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2011)CrossRefGoogle Scholar
  10. 10.
    Tang, K., Yang, J., Wang, J.: Investigating haze-relevant features in a learning framework for image dehazing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2995–3000 (2014)Google Scholar
  11. 11.
    Meng, G., Wang, Y., Duan, J., Xiang, S., Pan, C.: Efficient image dehazing with boundary constraint and contextual regularization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 617–624 (2013)Google Scholar
  12. 12.
    Sulami, M., Glatzer, I., Fattal, R., Werman, M.: Automatic recovery of the atmospheric light in hazy images. In: 2014 IEEE International Conference on Computational Photography (ICCP), pp. 1–11. IEEE (2014)Google Scholar
  13. 13.
    Zhu, Q., Mai, J., Shao, L.: A fast single image haze removal algorithm using color attenuation prior. IEEE Trans. Image Process. 24(11), 3522–3533 (2015)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Hussain, F., Jeong, J.: Visibility enhancement of scene images degraded by foggy weather conditions with deep neural networks. J. Sens., 9 (2016). Article ID 3894832 (Hindawi Publishing Corporation)Google Scholar
  15. 15.
    Li, C., Guo, J., Porikli, F., Fu, H., Pang, Y.: A cascaded convolutional neural network for single image dehazing. Accepted by IEEE ACCESS, Computer Vision and Pattern Recognition (CS.CV), Submitted on 21 Mar 2018Google Scholar
  16. 16.
    Li, B., Peng, X., Wang, Z., Xu, J., Feng, D.: An all-in-one network for dehazing and beyond. J. Latex Class Files 14(8) (2015)Google Scholar
  17. 17.
    Ren, W., Liu, S., Zhang, H., Pan, J., Cao, X., Yang, M.-H.: Single image dehazing via multi-scale convolutional neural networks. In: European Conference on Computer Vision, pp. 154–169. Springer (2016)Google Scholar
  18. 18.
    Cai, B., Xu, X., Jia, K., Qing, C., Tao, D.: Dehazenet: an end-to-end system for single image haze removal. IEEE Trans. Image Process. 25(11) (2016)MathSciNetCrossRefGoogle Scholar
  19. 19.
    LeCun, Y., Kavukvuoglu, K., Farabet, C.: Convolutional networks and applications in vision. In: International Symposium on Circuits and Systems, pp. 253–256 (2010)Google Scholar
  20. 20.
    LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2324 (1998) [CrossRef]CrossRefGoogle Scholar
  21. 21.
    Girshick, R.: Fast R-CNN. In: IEEE International Conference on Computer Vision (ICCV) (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Allach Samir
    • 1
    Email author
  • Ben Ahmed Mohamed
    • 1
  • Boudhir Anouar Abdelhakim
    • 1
  1. 1.Laboratory (LIST) FST of TangierUAE UniversityTangierMorocco

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