Fast Traffic Sign Recognition Using Color Segmentation and Deep Convolutional Networks

  • Ali Youssef
  • Dario Albani
  • Daniele Nardi
  • Domenico Daniele Bloisi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10016)


The use of Computer Vision techniques for the automatic recognition of road signs is fundamental for the development of intelligent vehicles and advanced driver assistance systems. In this paper, we describe a procedure based on color segmentation, Histogram of Oriented Gradients (HOG), and Convolutional Neural Networks (CNN) for detecting and classifying road signs. Detection is speeded up by a preprocessing step to reduce the search space, while classification is carried out by using a Deep Learning technique. A quantitative evaluation of the proposed approach has been conducted on the well-known German Traffic Sign data set and on the novel Data set of Italian Traffic Signs (DITS), which is publicly available and contains challenging sequences captured in adverse weather conditions and in an urban scenario at night-time. Experimental results demonstrate the effectiveness of the proposed approach in terms of both classification accuracy and computational speed.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Ali Youssef
    • 1
  • Dario Albani
    • 1
  • Daniele Nardi
    • 1
  • Domenico Daniele Bloisi
    • 1
  1. 1.Department of Computer, Control, and Management EngineeringSapienza University of RomeRomeItaly

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