Abstract
Abstract: Traffic Sign Recognition and detection system is specifically useful for drivers and driver-less cars. By identifying traffic signs accurately and effectively it improves the driving safety of autonomous cars. This research paper presents a comprehensive approach for the development of a traffic sign detection system using Convolution Neural Network, TensorFlow and OpenCV to classify the traffic signs in real-time effectively. TensorFlow and OpenCV play an important role in shaping effective traffic detection and recognition system. We have explained the process of data collection, preparation, model architecture and the integration of TensorFlow for training and interference. OpenCV for image processing and real time feed processing, ensuring seamless implementation on various hardware platform. The model uses German Traffic Sign Recognition dataset. The results show that the proposed system achieves high accuracy in detecting and recognizing traffic sign making it a valuable system for both autonomous vehicles and human drivers
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