Authors : Yashshri Kesare

Volume/Issue : Volume 8 - 2023, Issue 8 - August

Google Scholar : https://bit.ly/3TmGbDi

Scribd : https://tinyurl.com/3w5ryaft

DOI : https://doi.org/10.5281/zenodo.8327900

Abstract : In this project a deep learning approach is presented for detecting traffic signs in real-time for autonomous vehicle applications. The suggested method analyzes photos and detects traffic signs using a convolution neural network (CNN). Using real-world datasets, the performance of the suggested solution was assessed and contrasted with currently available state-of- the-art techniques. According to the outcomes, the suggested method performs better than earlier ones in terms of accuracy and processing speed, making it a viable option for autonomous vehicles' traffic sign identification. This approach has the potential to significantly improve the safety and efficiency of autonomous vehicle navigation and facilitate the widespread adoption of autonomous vehicles. Traffic sign detection is a crucial aspect of autonomousvehicle technology as it helps vehicles to understand and respond to the road conditions and traffic regulations.

Keywords : Deep Learning, Image Processing, Anaconda, CNN.