The project titled "Traffic Sign Detection and Recognition Using Convolutional Neural Network" focuses on enhancing road safety by developing a system that can automatically detect and recognize traffic signs. This system is critical for driverless cars and autonomous systems, aiming to prevent accidents caused by missed traffic signs. It employs Convolutional Neural Networks (CNNs), which process images of traffic signs captured by cameras, extracting features and classifying them into predefined categories. The project utilizes the German Traffic Signs Dataset consisting of 72 types of traffic signs and integrates residual blocks in the CNN architecture to improve feature extraction and accuracy. The proposed system runs on low-power devices for real-time use and can be fine-tuned for specific regional requirements, ensuring a flexible and adaptive driver assistance solution.
The system also features a user-friendly graphical interface, created using Python's Tkinter, which allows users to upload traffic sign images and receive instant classification results. This project demonstrates a significant improvement in accuracy and speed over previous models, making it suitable for real-world applications in autonomous driving and traffic management.