ADASDL—Innovative Approach for ADAS Application Using Deep Learning

  • Ramachandra Guda
  • V. MohanrajEmail author
  • J. V. Kameshwar Rao
  • N. A. Chandan kumar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 862)


The main goal of this paper is to develop an application using deep learning to enhance advanced driver assistance system (ADAS) features based on internal, external and environmental factors from a driver perspective (henceforth called ADASDL). It covers features like drowsiness detection, de-raining and traffic sign detection models. In this approach, we will analyse driver eye blinking to detect drowsiness of driver and also removing rain streaks from individual images based on the deep convolutional neural network (CNN). This proposed approach is implemented with deep learning models. The results including inference time are discussed.


ADASDL Deep learning model SSD EAR Resnet Drowsiness 


  1. 1.
  2. 2.
    H.N. Dean, K.V.T. Jabir, Real time detection and recognition of Indian traffic signs using Matlab. Int. J. Sci. Eng. Res. 4(5), 684 (2013)Google Scholar
  3. 3.
    H. Ueno, M. Kaneda, M. Tsukino, Development of drowsiness detection system, in Proceedings of 1994 Vehicle Navigation and Information Systems Conference, Yokohama, Japan (IEEE, New York, 1994), pp. 15–20Google Scholar
  4. 4.
    T.C. Kao, T.Y. Sun, Head pose recognition in advanced driver assistance system, in 2017 IEEE 6th Global Conference on Consumer Electronics (GCCE), Nagoya (2017), pp. 1–3.
  5. 5.
    A. Simić, O. Kocić, M.Z. Bjelica, M. Milošević, Driver monitoring algorithm for advanced driver assistance systems, in 2016 24th Telecommunications Forum (TELFOR), Belgrade (2016), pp. 1–4.
  6. 6.
    A. Møgelmose, M.M. Trivedi, T.B. Moeslund, Vision based traffic sign detection and analysis for intelligent driver assistance systems: perspectives and survey. IEEE Trans. Intell. Transp. Syst. (2012)Google Scholar
  7. 7.
    X. Fu, J. Huang, D. Zeng, Y. Huang, X. Ding, J. Paisley, Removing rain from single images via a deep detail network, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)Google Scholar
  8. 8.
    W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, A.C. Berg, SSD: single shot multibox detector, in ECCV (2016)Google Scholar
  9. 9.
    T. Soukupová, J. Čech, Real-time eye blink detection using facial landmarks, in 21st Computer Vision Winter Workshop (2016)Google Scholar
  10. 10.
    V. Kazemi, J. Sullivan, One millisecond face alignment with an ensemble of regression trees, in 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus (2014), pp. 1867–1874Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Ramachandra Guda
    • 1
  • V. Mohanraj
    • 1
    Email author
  • J. V. Kameshwar Rao
    • 1
  • N. A. Chandan kumar
    • 1
  1. 1.HCL Technologies LimitedNoidaIndia

Personalised recommendations