Traffic sign recognition using deep learning

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. © 2022 Inderscience Enterprises Ltd.