ICTS: Indian Cautionary Traffic Sign Classification Using Deep Learning

Abstract

Automatic traffic sign detection is a research topic for various applications, including driver assistance, inventory management, and autonomous driving. The visual appearance of traffic signs varies significantly from country to country, making classification systems more challenging to implement. In this work classification of traffic signs in the Indian roadways is considered. But there is currently no publicly available dataset for Indian traffic signs. This paper proposes the Indian Cautionary Traffic Sign (ICTS) data-set with 19,775 samples collected under challenging environmental conditions. The dataset is preprocessed using image processing techniques before being labeled into 40 categories. Then, to classify ICTS traffic signs, deep learning classification models such as LeNET, Vgg-16, AlexNet, and ResNet are used. The performance of all of these trained models is compared and analyzed on the proposed dataset and the GTSRB-benchmark dataset in terms of specificity, precision, recall, F-measure, and accuracy. The proposed dataset is available at the IEEE data port repository (http://dx.doi.org/10.21227/yy4h-rc98).