An efficient implementation of traffic signs recognition system using CNN

• Published in: Microprocessors and microsystems Vol. 98; p. 104791
• Main Authors: Fredj, Hana Ben, Chabbah, Amani, Baili, Jamel, Faiedh, Hassen, Souani, Chokri
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.04.2023
• Subjects: AlexNet; Computer vison; Convolutional neural networks; GoogleNet; Optimized LeNet; ResNet34; Traffic signs recognition; VGGNet; GoogleNet; Traffic signs recognition; Computer vison; ResNet34; AlexNet; Convolutional neural networks; VGGNet; Optimized LeNet
• ISSN: 0141-9331; 1872-9436
• DOI: 10.1016/j.micpro.2023.104791

In the modern world, road traffic signs are vital for drivers safety. In fact, multistep traffic forecasting on road networks can help to avoid many problems on the streets. In this context, there are several methods which allow to achieve excellent results in the field of traffic signs recognition. Recently, Deep Convolutional Neural Network (CNN) have achieved excellent results in this area. In this paper, CNN is used to develop a Traffic and Road Sign recognition system. The performance of the proposed architecture is measured using a novel dataset, namely the Tunisian traffic signs dataset. In addition, we minimize the number of layers in the LeNet network, lowering the number of parameters in the network to accelerate the computation. Our architecture was used with varying parameters in order to achieve the best recognition rates in uncontrolled environment including weather conditions, complex background, variable illumination, and sign color fading. The Experimental results show that the proposed CNN architecture achieved a significant accuracy, thus higher than those achieved in similar previous studies.