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Neural Computing and Applications
Published in: Neural Computing and Applications 9/2019

11-01-2019 | Original Article

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

Authors: Yassmina Saadna, Ali Behloul, Saliha Mezzoudj

Published in: Neural Computing and Applications | Issue 9/2019

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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.
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Metadata
Title
Speed limit sign detection and recognition system using SVM and MNIST datasets
Authors
Yassmina Saadna
Ali Behloul
Saliha Mezzoudj
Publication date
11-01-2019
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 9/2019
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-018-03994-w

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