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

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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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Authors and Affiliations

  1. LaSTIC Laboratory, Departement of Computer Science, University of Batna 2, 53, Route de Constantine., 05078, Fésdis, Batna, Algeria

    Yassmina Saadna, Ali Behloul & Saliha Mezzoudj

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  1. Yassmina Saadna
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  2. Ali Behloul
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  3. Saliha Mezzoudj
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Correspondence to Yassmina Saadna.

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Saadna, Y., Behloul, A. & Mezzoudj, S. Speed limit sign detection and recognition system using SVM and MNIST datasets. Neural Comput & Applic 31, 5005–5015 (2019). https://doi.org/10.1007/s00521-018-03994-w

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