Real-Time Traffic Sign Recognition using Convolutional Neural Networks (CNNs) in ADAS

Pratik Brahmbhatt

Title:
Real-Time Traffic Sign Recognition using Convolutional Neural Networks (CNNs) in ADAS

Authors:
Pratik Brahmbhatt

Cite This Article :

Pratik Brahmbhatt "Real-Time Traffic Sign Recognition using Convolutional Neural Networks (CNNs) in ADAS" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-9 | Issue-3, June 2025, pp.523-529, URL: https://www.ijtsrd.com/papers/ijtsrd80051.pdf

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Abstract :
Traffic Sign Recognition (TSR) is a critical function in Advanced Driver Assistance Systems (ADAS), contributing significantly to road safety and the progression of autonomous driving technologies. This research proposes a real-time TSR system built on Convolutional Neural Networks (CNNs), optimized for deployment on embedded automotive platforms. Our approach emphasizes a balance between model accuracy and computational efficiency, making it suitable for real-world vehicular scenarios. Using the German Traffic Sign Recognition Benchmark (GTSRB) dataset, we demonstrate that our CNN model achieves high accuracy and meets real-time processing requirements, validating its application in ADAS.

Keywords :
Traffic Sign Recognition, Convolutional Neural Networks, ADAS, Real-Time Systems, Embedded AI, GTSRB

Publication Details:

Unique Identification Number : IJTSRD80051

Published In : Volume-9 | Issue-3, June 2025

Page Number(s) : 523-529

Publisher Name : IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470

Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

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