Design an Application of Traffic Sign Recognition Based on CNN Deep Learning Network

Abstract

Traffic sign recognition plays a crucial role in intelligent transportation systems by enhancing road safety and enabling autonomous vehicles to understand traffic rules. It ensures proper adherence to traffic regulations, minimizes accidents, and facilitates smoother traffic flow. In this research, we propose a Convolutional Neural Network (CNN) based approach for accurate and efficient traffic sign recognition. The model leverages the power of deep learning to automatically extract and learn features from raw image data, eliminating the need for manual feature engineering.Our approach is evaluated under diverse real-world conditions, including varying lighting, occlusions, and weather disturbances, to ensure robustness and reliability. The proposed method achieves high accuracy in classifying a wide range of traffic signs, making it suitable for integration into advanced driver-assistance systems (ADAS) and autonomous vehicle platforms. Additionally, the system's competitive performance highlights its potential for real-time applications in intelligent transportation systems, paving the way for safer and more efficient road networks.