Comprehensive Approach to Road Sign Detection and Recognition for Autonomous Driving Systems and Road Safety

This study provides a comprehensive system designed to perform critical functions such as detecting and recognizing traffic signs. These tasks are critical in modern transportation systems, improving road safety and enabling autonomous driving. To achieve its goals, the system uses multiple technologies such as image processing, natural language processing, transfer learning, and deep learning. The system is made up of several components, including image acquisition, pre-processing, sign detection, classification, text detection, translation, comparison, and audio generation. Convolutional neural networks (CNNs) and other object detection models, such as YOLO, are investigated for their use in identifying traffic signs, demonstrating their effectiveness under a variety of conditions. Real-time functionality is a key feature, making the system applicable to autonomous driving scenarios. Moreover, it accommodates different languages, enhancing user accessibility. The study addresses the important issue of detecting and recognizing traffic signs, with consequences for traffic management and road safety.