Traffic Sign Classification Using CNN

Abstract

This study presents a novel approach for traffic sign classification leveraging Convolutional Neural Networks (CNNs). With the proliferation of autonomous vehicles and advanced driver assistance systems, accurate and efficient traffic sign recognition is imperative for safe and efficient navigation. The proposed CNN-based method utilizes deep learning techniques to automatically extract discriminative features from traffic sign images, enabling robust classification across diverse environmental conditions and variations in sign appearance. Experimental results demonstrate the effectiveness of the proposed approach. This research contributes to the advancement of intelligent transportation systems by providing a reliable and scalable solution for real-time traffic sign classification. Our findings highlight the potential of deep learning for robust and accurate traffic sign classification in real-world scenarios.