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Classification of Road Surface and Weather-Related Condition Using Deep Convolutional Neural Networks

  • Alexander BuschEmail author
  • Daniel Fink
  • Max-Heinrich Laves
  • Zygimantas Ziaukas
  • Mark Wielitzka
  • Tobias Ortmaier
Conference paper
  • 5 Downloads
Part of the Lecture Notes in Mechanical Engineering book series (LNME)

Abstract

In order to achieve the goal of autonomous driving, a precise perception of the vehicle’s environment is required. In particular, the weather-related road condition has a major influence on vehicle dynamics and thus on driving safety.

In this paper, we compare Deep Convolutional Neural Networks of different computational effort, namely Inception-v3, GoogLeNet and the much smaller SqueezeNet, for classification of road surface and its weather-related condition. Previously, different regions of interest were compared in order to provide the networks with optimal input data.

Keywords

Computer vision Road condition Classification 

Notes

Acknowledgment

The authors would like to thank the German Research Foundation (DFG) for founding this project.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Alexander Busch
    • 1
    Email author
  • Daniel Fink
    • 1
  • Max-Heinrich Laves
    • 1
  • Zygimantas Ziaukas
    • 1
  • Mark Wielitzka
    • 1
  • Tobias Ortmaier
    • 1
  1. 1.Institute of Mechatronic SystemsGottfried Wilhelm Leibniz UniversitätHanoverGermany

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