A Practical Approach of Recognizing and Detecting Traffic Signs using Deep Neural Network Model

Abstract

As a result of many technological developments and growing usage of artificial intelligence in our daily routines, the number of autonomous and self-driving vehicles has expanded dramatically. To be effective and efficient, autonomous cars must be able to recognize and interpret a variety of traffic signs and take appropriate responses. A technology known as Traffic Sign Recognition can be used to determine a large number of different traffic signs. Traffic Sign Recognition is a technique that enables self-driving or autonomous vehicles to recognize traffic signs on the road. We must classify the images into their appropriate categories or groupings once they have been recognized. We accomplish this by creating a Convolutional Neural Network model. We must apply a discipline of artificial intelligence known as computer vision to derive information from the images acquired and recognized by the Traffic Sign Recognition and make recommendations based on that knowledge. In our paper, we'll use a Convolutional Neural Network model to create distinct indications in the image that may be sorted into several categories. As a result, the system can read and understand traffic signs, which is a crucial duty in the creation and improvement of autonomous vehicles. Because this would be used in a real-time setting, we have included blurry images in our dataset to emulate real-time capturing scenarios like as when the vehicle is moving or when direct light is shining on the subject.