Scholarlink

This research paper aims to enhance road safety through effective traffic sign recognition using deep learning models by building a comprehensive system that leverages advanced object detection and classification techniques to improve driver autonomous driving systems. Precision, recall, mean average precision, and other key metrics were used to evaluate the effectiveness of the model and the results indicate significant improvements in real-time object detection contributing to safer navigation. Technical challenges in traffic sign recognition are also discussed along with the implications for future developments.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Enhancing Road Safety: A Multifaceted Approach to Traffic Sign Recognition Using State-of-the-Art Models

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Mohammed Azeem Khan,
  • S. Shwetha Iyer,
  • Daivik Mampally,
  • Archana Bhise

摘要

This research paper aims to enhance road safety through effective traffic sign recognition using deep learning models by building a comprehensive system that leverages advanced object detection and classification techniques to improve driver autonomous driving systems. Precision, recall, mean average precision, and other key metrics were used to evaluate the effectiveness of the model and the results indicate significant improvements in real-time object detection contributing to safer navigation. Technical challenges in traffic sign recognition are also discussed along with the implications for future developments.