IMPROVING AUTONOMOUS VEHICLES NAVIGATION THROUGH TRAFFIC SIGN RECOGNITION

Abstract

Traffic sign recognition is an essential component of intelligent transportation systems (ITS), which
aims to improve road safety and assist drivers in navigating through road networks efficiently. This paper
presents a system designed to recognize traffic sign boards using computer vision and machine learning
techniques. The system processes images of road signs, classifies them into various categories, and provides
relevant information to drivers, contributing to safer driving. This approach integrates image processing
algorithms, deep learning models, and realtime data analytics to ensure high accuracy and fast processing. The
proposed system improves upon traditional methods by reducing false positives and enhancing recognition
speed. To ensure a smooth and secure flow of traffic, road signs are essential. A major cause of road accidents
is negligence in viewing the Traffic signboards and interpreting them incorrectly. The proposed system helps in
recognizing the Traffic sign and sending a voice alert through the speaker to the driver so that he/ she may take
necessary decisions. The proposed system is trained using Convolutional Neural Network (CNN) which helps in
traffic sign image recognition and classification. A set of classes are defined and trained on a particular dataset
to make it more accurate.