Year: 2025
Type: article
Abstract: Traffic sign recognition and detection is a critical component of intelligent transportation systems aimed at enhancing road safety and driver assistance. This technology involves the use of computer vision and machine learning algorithms to detect and classify various road signs such as stop signs, speed limits, and warning signs in real-time. By utilizing cameras or sensors mounted on vehicles, the system identifies these signs and provides timely alerts to the driver. The addition of voice message alerts further improves driver awareness, offering immediate notifications like “Speed limit 50 km/h” or “Stop sign ahead.” technique for detecting traffic signs that in reducing human errors but also enhances the overall driving experience by ensuring that drivers are well- informed about changing road conditions, contributing to safer roadways navigation. This report examines the development of a technique for detecting traffic signs that utilizing machine learning (ML) and as well as artificial intelligence (AI) techniques. The system integrates various technologies, including Flutter for user interface development, OpenCV for image processing, Large Language Models (LLM) for enhanced decision-making, networking tunnels for real-time data transmission, and Text- to-Speech (TTS) for auditory feedback. The implementation aims to to increase the precision and efficiency of traffic sign recognition, contributing to the advancement of intelligent transportation systems.
Author: Nagarjuna G R
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4409764746