Iot Based Speed Control And Accident Avoidance Using Ai Road Sign Detection System

Abstract

Through a traffic sign detection system built using deep learning YOLO framework India achieves better road safety. The system achieves local traffic condition-specific high accuracy performance through training and validation utilizing the Indian Traffic Sign Recognition dataset. Real-time traffic sign detection from changes in road conditions becomes possible through the YOLO architecture which performs both sign classification and feature extraction processes. Real-time video processing enables the system to find and label traffic signs that appear for moving vehicles throughout different locations. Vehicle systems embedded within vehicles detect traffic signs by adjusting vehicle speeds according to safety needs for drivers and pedestrians. This system operates reliably under both nsufficient street lighting conditions and adverse weather and damaged or obscured signs. Testing automotive systems under Indian traffic conditions leads to increased system reliability which results in better practical use. The diverse conditions enable developers to refine their models adequately to excel with Indian road traffic features. The proposed system integrates modern traffic systems with local safety regulations to simultaneously reduce accidents and enhance driving quality.