Traffic Signs Classification Project

This repository contains the code for both a Web Application and an Android Application for Traffic Signs Classification using a Convolutional Neural Network (CNN). The project is designed to recognize various types of traffic signs and classify them accurately, aiding in applications such as driver assistance and autonomous vehicle systems.

Overview

This project leverages a trained CNN model to classify traffic signs into different categories. It includes:

• A web application built with Python, Flask, and Tailwind CSS.
• An Android application that uses the TensorFlow Lite version of the CNN model for on-device classification.

The CNN model is trained using Keras and TensorFlow and can classify traffic signs into 43 different categories.

Features

• Web Application:

  • Real-time traffic sign classification.
  • Displays the identified traffic sign with a probability score.
  • User-friendly interface built using Flask and styled with Tailwind CSS.

• Android Application:

  • Real-time traffic sign detection using the device's camera.
  • Intuitive and mobile-optimized user interface.
  • Efficient and fast on-device classification with TensorFlow Lite.

Technology Stack

• Backend: Python, Flask
• Frontend: HTML, Tailwind CSS
• Mobile Frameworks: Android SDK, TensorFlow Lite, OpenCV, CameraX
• Deep Learning Frameworks: TensorFlow, Keras
• Data Handling and Visualization: NumPy, Pandas, Matplotlib, OpenCV
• Miscellaneous: ImageDataGenerator for data augmentation, Pickle for model storage

Contributing

We welcome contributions from the community! If you wish to contribute:

• Fork the repository.
• Create a new branch for your feature or bug fix.
• Commit your changes and open a pull request for review.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Acknowledgements

• Keras and TensorFlow: For providing an excellent framework to build and train deep learning models.
• OpenCV: For image preprocessing and handling.
• Flask and Tailwind CSS: For web app development.
• Android SDK and TensorFlow Lite: For mobile optimization.

Contact

For any queries or issues, please contact [mdwaliulislamrayhan@gmail.com] or open an issue on the repository.