Article: Traffic sign recognition using deep learning Journal: International Journal of Vehicle Autonomous Systems (IJVAS) 2022 Vol.16 No.2/3/4 pp.97 - 107 Abstract: Recognition of traffic signs is an integral step towards achieving Advanced Driver Assistance Systems (ADAS) as distracted driving is one of the primary causes of road accidents and fatalities. This paper attempts to exploit the capabilities of Convolutional Neural Networks (CNN) to recognise traffic signs under various computational and environmental constraints. The German Traffic Sign Recognition Benchmark (GTSRB) dataset is used for the classification of images. The dataset is subjected to various image processing techniques like greyscaling, denoising, filtering, and thresholding to obtain a generalised model for the recognition of traffic signs. The neural network used here comprises three convolution layers each followed by a max pooling layer which further are followed by four fully connected dense layers. The models are trained for 100 epochs with a validation split of 20%. The model performs best with 'Adam' optimiser with a learning rate of 0.001. Inderscience Publishers - linking academia, business and industry through research

Title: Traffic sign recognition using deep learning

Authors: Vraj Patel; Joy Mehta; Saurab Iyer; Ankit K. Sharma

Addresses: Instrumentation and Control Engineering Department, Institute of Technology, Nirma University, Gujarat, India ' Instrumentation and Control Engineering Department, Institute of Technology, Nirma University, Gujarat, India ' Instrumentation and Control Engineering Department, Institute of Technology, Nirma University, Gujarat, India ' Instrumentation and Control Engineering Department, Institute of Technology, Nirma University, Gujarat, India

Abstract: Recognition of traffic signs is an integral step towards achieving Advanced Driver Assistance Systems (ADAS) as distracted driving is one of the primary causes of road accidents and fatalities. This paper attempts to exploit the capabilities of Convolutional Neural Networks (CNN) to recognise traffic signs under various computational and environmental constraints. The German Traffic Sign Recognition Benchmark (GTSRB) dataset is used for the classification of images. The dataset is subjected to various image processing techniques like greyscaling, denoising, filtering, and thresholding to obtain a generalised model for the recognition of traffic signs. The neural network used here comprises three convolution layers each followed by a max pooling layer which further are followed by four fully connected dense layers. The models are trained for 100 epochs with a validation split of 20%. The model performs best with 'Adam' optimiser with a learning rate of 0.001.

Keywords: traffic sign recognition; ADAS; advanced driver assistance systems; GTSRB; German Traffic Sign Recognition Benchmark; deep learning; computer vision; image processing; CNN; convolutional neural network.

DOI: 10.1504/IJVAS.2022.133005

International Journal of Vehicle Autonomous Systems, 2022 Vol.16 No.2/3/4, pp.97 - 107

Received: 18 Dec 2020
Accepted: 26 Jun 2021
Published online: 24 Aug 2023 *

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Title: Traffic sign recognition using deep learning

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