TRAFFIC SIGN RECOGNIZER USING MACHINE LEARNING

Abstract

This paper presents a study of model which is capable of detecting different traffic signs which are present on the roads.Traffic signs play a vital role in ensuring road safety by providing critical information to drivers.However, human error and fatigue can lead to missed or misinterpreted signs, resulting in potentially dangerous situations.The Traffic Sign Recognizer project aims to develop an automated system that can detect and classify traffic signs in real-time, assisting drivers in identifying and comprehending the information conveyed by road signs accurately.The project utilizes computer vision techniques and deep learning algorithms to analyze video streams from onboard sensors.The system first performs object detection to identify potential traffic signs within the captured frames.Then, a deep neural network is employed to classify the detected signs into specific categories, such as speed limits, stop signs, yield signs, and more.This system is designed to operate in real-time, providing immediate feedback to the driver.Upon sign detection and classification, the system can generate visual or auditory alerts to notify the driver of the sign's presence and convey the appropriate action to be taken.This technology aims to enhance road safety by reducing the likelihood of sign misinterpretation and preventing potential accidents caused by human error.This project has the potential to revolutionize road safety by leveraging computer vision and deep learning techniques to assist drivers in comprehending and responding to traffic signs accurately.