TRAFFIC SIGN DETECTION AND RECOGNITION USING DEEP LEARNING

Abstract

Traffic sign detection and recognition is an important task for autonomous vehicles and intelligent transportation systems. This task involves detecting and recognizing traffic signs from camera images in real-time. Convolutional Neural Networks (CNN) have shown to be effective in achieving high accuracy in various computer vision tasks. In this paper, we propose a CNN-based approach for traffic sign detection and recognition. Our approach involves using a deep CNN architecture that can detect and classify traffic signs simultaneously. We train the CNN model on a large dataset of traffic sign images and evaluate its performance on a real-world dataset. Our experimental results demonstrate that the proposed approach achieves high accuracy and can detect and recognize traffic signs in real-time with low computational cost. This approach can be utilized in various applications such as advanced driver assistance systems, traffic management, and autonomous driving. Key Words: Traffic sign detection, Traffic sign recognition, Deep Learning, Convolutional Neural Network (CNN), Graphical User Interface (GUI)