Traffic Sign Recognition System with CNN

Introduction

This Traffic Sign Recognition System is a computer vision project that uses Convolutional Neural Networks (CNNs) to recognize and classify traffic signs in real-time. It includes both a Graphical User Interface (GUI) for image recognition and a live video capturing method. This README provides an overview of the project, its features, and instructions on how to set it up and use it.

Features

• Real-time traffic sign recognition using a pre-trained CNN model.
• GUI for easy image recognition without any coding knowledge.
• Live video feed for continuous traffic sign recognition.
• High accuracy in recognizing various traffic signs.
• Interactive and user-friendly interface.

Prerequisites

Before using this Traffic Sign Recognition System, ensure you have the following prerequisites installed:

• Python (3.7 or higher)
• OpenCV
• TensorFlow
• Numpy

You can install these dependencies using pip:

pip install opencv-python tensorflow  numpy

Usage

1. Clone the Repository

git clone https://github.com/rdrounak/Traffic-Sign-Recognition.git
cd traffic-sign-recognition

2. Run the GUI

To use the GUI for traffic sign recognition:

python gui.py

The GUI will open, allowing you to either select an image or use your computer's webcam for real-time traffic sign recognition.

3. Live Video Capture

To use the live video capture for traffic sign recognition:

python TrafficSign_Test.py

This will open a window showing your webcam feed with real-time traffic sign recognition displayed on the video.

Model

The CNN model used for traffic sign recognition is pre-trained and included in the repository. You can find it in the models directory.

Training (Optional)

If you wish to train your own CNN model or fine-tune the existing model, you can use the dataset provided in the dataset directory. You can use popular deep learning frameworks like TensorFlow or PyTorch to train and save your model.

Credits

This project is developed by Rounak Dwary. It is based on deep learning techniques and libraries provided by the open-source community.

Acknowledgments

Special thanks to the open-source community for providing the tools and datasets necessary for this project.

Feel free to contribute, report issues, or suggest improvements to this project. Happy traffic sign recognition!