Paper Key : IRJ************372
Author: Chandan B Ram,Chaithra B C,Ajay M,Bhavana N,K Pavan Kalyan,Kruthika N S
Date Published: 20 Oct 2024
Abstract
Traffic sign detection and identification is a critical component of autonomous vehicle systems, especially in the diverse and challenging environments of Indian roads. This paper introduces a YOLOv9-based approach designed for accurate and instantaneous identification and classification of traffic signs specifically tailored to Indian conditions. Utilizing a comprehensive dataset of Indian traffic signs from the Ministry of Road Transport and Highways, our system incorporates advanced preprocessing and data augmentation techniques to enhance model performance. By training the YOLOv9 model from scratch, we achieve real-time processing capabilities and high accuracy. Experimental results affirm the reliability of our approach, highlighting its potential to significantly advance vehicle automation, safety protocols, and traffic efficiency in the Indian context.
DOI LINK : 10.56726/IRJMETS62435 https://www.doi.org/10.56726/IRJMETS62435