An AI-Based Efficient Model for the Classification of Traffic Signals Using Convolutional Neural Network

Abstract

The objective of this study is to build a model for the classification of traffic signs available in the image into many categories using a CNN and Keras library to detect the traffic sign. The goal of the traffic sign recognition is to build a deep neural network (DNN), which is used to classify traffic signs. The authors suggest training the model so it can decode traffic signs from natural images using the German Traffic Sign Dataset. This data should be firstly preprocessed in order to maximize the model performance. After choosing model architecture, fine tuning, and training, the model will be tested on new images of traffic signs found on the web. Because it deals with image classification, a convolutional neural network is chosen as a type of DNN, which is a common choice for this type of problem. The code is written in Python with use of tensor flow library. The proposed CNN model identifies traffic signs and classifies them with 95% accuracy. GUI of this model makes it easy to understand how signs are classified into several classes.