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Framework for Machine Vision Based Traffic Sign Inventory

  • Petri Hienonen
  • Lasse Lensu
  • Markus Melander
  • Heikki Kälviäinen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10269)

Abstract

Automatic traffic sign inventory and simultaneous condition analysis can be used to improve road maintenance processes, decrease maintenance costs, and produce up-to-date information for future intelligent driving systems. The goal of this research is to combine automatic traffic sign detection and classification with traffic sign inventory and condition analysis. This paper proposes a complete machine vision framework for the purpose and presents the results of its performance evaluation with three datasets: Traffic Signs Dataset, and two datasets collected for this research. The experimental results show that the system is able to detect, locate, and classify almost all the traffic signs, and is a suitable platform for traffic sign condition analysis.

Keywords

Traffic sign inventory Detection Classification Localization Distributed asset management Condition analysis Machine vision Image processing 

Notes

Acknowledgements

The authors would like to thank the Finnish Transport Agency for funding of the TrafficVision research project.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Petri Hienonen
    • 1
    • 2
  • Lasse Lensu
    • 1
  • Markus Melander
    • 2
    • 3
  • Heikki Kälviäinen
    • 1
  1. 1.Machine Vision and Pattern Recognition Laboratory (MVPR), School of Engineering ScienceLappeenranta University of Technology (LUT)LappeenrantaFinland
  2. 2.Vionice Ltd.LappeenrantaFinland
  3. 3.Finnish Transport Agency (FTA)LappeenrantaFinland

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