Real-Time Traffic Sign Recognition System

A Real-Time Traffic Sign Recognition System is a mini project that leverages artificial intelligence and machine learning to identify and classify traffic signs in real-time using video feeds from cameras. The project involves collecting a dataset of traffic sign images, which is then preprocessed and used to train a convolutional neural network (CNN) model. The model learns to recognize various traffic signs, such as stop signs, yield signs, and speed limits. During deployment, the system processes live video input, detecting and classifying signs within milliseconds, providing critical information for autonomous vehicles or driver assistance systems. Additionally, integrating techniques like image augmentation and transfer learning can enhance accuracy and robustness, ensuring the system performs well under different lighting and weather conditions. This project not only demonstrates practical applications of computer vision but also emphasizes safety in transportation.