Abstract :

Traffic Signs Play A Critical Role In Ensuring Road Safety By Providing Drivers With Important Information Regarding Speed Limits, Warnings, Prohibitions, And Navigation Instructions. The Increasing Number Of Vehicles On Roadways Has Created A Growing Need For Intelligent Transportation Systems Capable Of Automatically Recognizing And Interpreting Traffic Signs. Manual Monitoring And Interpretation Of Traffic Signs Can Be Challenging, Particularly In Complex Driving Environments And Adverse Weather Conditions. This Paper Presents A Traffic Sign Recognition And Detection Framework Using Advanced Image Processing And Machine Learning Techniques. The Proposed System Captures Road Images Through Vehiclemounted Cameras And Performs Image Preprocessing, Feature Extraction, Object Detection, And Classification To Identify Traffic Signs Accurately. Deep Learning Models, Particularly Convolutional Neural Networks (CNNs), Are Utilized To Recognize Various Categories Of Traffic Signs Under Different Lighting, Weather, And Viewing Conditions. The Framework Enables Real-time Detection And Interpretation Of Traffic Signs, Supporting Driver Assistance Systems And Autonomous Vehicles. Experimental Analysis Demonstrates That The Proposed Approach Achieves High Recognition Accuracy, Robust Detection Performance, And Efficient Real-time Operation. The Developed System Contributes To Improved Road Safety, Enhanced Driving Assistance, And The Advancement Of Intelligent Transportation Technologies.

Published:

26-9-2026

Issue:

Vol. 26 No. 9 (2026)

Page Nos:

404-411

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite