DEEP LEARNING FOR LARGE-SCALE TRAFFIC-SIGN DETECTION AND RECOGNITION

Abstract

In this project, we developed a comprehensive deep learning system for large-scale traffic-sign detection and recognition leveraging Convolutional Neural Networks (CNNs) and the Rectified Linear Unit (ReLU) activation function. Our model efficiently identifies various traffic signs from complex urban scenes, ensuring improved safety and navigation for autonomous vehicles and driver assistance systems. Benchmark tests on standard datasets showcase a significant boost in accuracy and real-time response compared to traditional methods. The fusion of CNNs and ReLU showcases the potential to revolutionize the efficiency of traffic sign recognition systems, emphasizing scalability and robustness.