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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)

Abstract

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.

Keywords

ADASDL Deep learning model SSD EAR Resnet Drowsiness 

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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

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