Cognitive Deep Learning System for Real-Time Traffic Sign Detection and Driver Alerts

As the number of vehicles on the road rises, making sure that road safety has become a main concern. Traffic signs plays as one of the crucial roles in to guide and control the traffic, but human error and distractions can lead to making a mistake, resulting in cause of accidents and traffic violations. The constructed system uses convolutional neural networks (CNNs) for accurate traffic sign recognition. The model's architecture is designed to capture features of different traffic signs enabling robust recognition even under varying conditions. Once a traffic sign is detected and recognized, the system employs natural language generation techniques to create contextually relevant alert messages. This alert message is produced in the form of voice alert which informs driver about the detected sign. This ensures that drivers receive clear and concise information about the sign, promoting quick and appropriate responses. In this process, we trained various German traffic sign data images and verify the implementation which can be used in further processing. The proposed approach is expected to accurately recognizing a wide range of traffic signs and generating informative alert messages using python libraries. Here, we are implementing YOLOv5 by using this algorithm overall, this research contributes to enhancing road safety by imposing deep learning techniques for automated traffic sign recognition and real-time generation of alert messages, ultimately reducing the risk of accidents and traffic violations.