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Speed limit sign detection and recognition system using SVM and MNIST datasets

  • Yassmina SaadnaEmail author
  • Ali Behloul
  • Saliha Mezzoudj
Original Article
  • 32 Downloads

Abstract

This article presents a computer vision system for real-time detection and robust recognition of speed limit signs, specially designed for intelligent vehicles. First, a new segmentation method is proposed to segment the image, and the CHT transformation (circle hog transform) is used to detect circles. Then, a new method based on local binary patterns is proposed to filter segmented images in order to reduce false alarms. In the classification phase, a cascading architecture of two linear support vector machines is proposed. The first is trained on the GTSRB dataset to decide whether the detected region is a speed limit sign or not, and the second is trained on the MNIST dataset to recognize the sign numbers. The system achieves a classification recall of 99.81% with a precision of 99.08% on the GTSRB dataset; in addition, the system is also tested on the BTSD and STS datasets, and it achieves a classification recall of 99.39% and 98.82% with a precision of 99.05% and 98.78%, respectively, within a processing time of 11.22 ms.

Keywords

Speed limit sign recognition Pattern recognition SVM Image segmentation LBP Vehicle safety 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare no conflicts of interest associated with this article.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.LaSTIC Laboratory, Departement of Computer ScienceUniversity of Batna 2FésdisAlgeria

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