A novel approach for real-time traffic sign recognition framework

Abstract

Traffic sign recognition plays a critical role in enhancing road safety and enabling autonomous driving systems. This paper presents a comprehensive approach to real-time traffic sign recognition using advanced computer vision techniques and machine learning models. The proposed system employs convolutional neural networks (CNNs) for accurate detection and classification of traffic signs under diverse environmental conditions, including varying lighting, weather, and occlusions. Real-time processing is achieved through the integration of optimized algorithms and hardware acceleration techniques, ensuring minimal latency and high throughput. Experimental results demonstrate that the system achieves state-of-the-art performance on benchmark datasets, with an accuracy of over 95% and a recognition speed suitable for real-world applications. The findings underscore the potential of the system to improve driver assistance systems and pave the way for safer autonomous vehicles.