Traffic Sign Recognition System

Abstract

Traffic sign detection and recognition have become essential components of intelligent transportation systems and Advanced Driver Assistance Systems (ADAS) due to the increasing need for road safety and automated driving. Conventional traffic sign recognition approaches based on handcrafted features and traditional image processing techniques often struggle to achieve high accuracy under varying environmental conditions such as poor lighting, occlusions, motion blur, and complex backgrounds. To overcome these limitations, this work presents a Traffic Sign Detection and Recognition System Using Convolutional Neural Networks (CNN), designed to accurately detect and classify traffic signs from input images. The proposed framework utilizes computer vision techniques for image preprocessing, including resizing, normalization, and image enhancement, followed by deep learning-based feature extraction and classification using a Convolutional Neural Network (CNN). The CNN automatically learns discriminative visual features such as shapes, colors, and patterns from traffic sign images, eliminating the need for manual feature engineering. The system is trained and evaluated using the German Traffic Sign Recognition Benchmark (GTSRB) dataset, which contains more than 50,000 labeled images belonging to 43 different traffic sign classes. A user-friendly interface is developed using Streamlit, enabling users to upload traffic sign images or capture images through a webcam for real-time prediction. The trained model classifies the detected traffic sign and displays the predicted class along with the confidence score. Experimental results are evaluated using Accuracy, Precision, Recall, F1-Score, Confusion Matrix, and Training Performance Metrics, demonstrating the effectiveness of the proposed CNN-based framework for accurate and reliable traffic sign recognition. The developed system contributes to improving road safety and can be effectively integrated into intelligent transportation systems, driver assistance technologies, and autonomous vehicle applications.