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.

Introduction

Traffic sign recognition is crucial for the development of self-driving cars and other intelligent road systems. This project employs a CNN model to classify images of traffic signs into 43 distinct classes. Using image processing and data augmentation techniques, the model achieves robust performance, making it suitable for real-world applications.

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Dataset

The project uses the German Traffic Sign Recognition Benchmark (GTSRB) dataset, which contains over 35,000 images of 43 different traffic sign classes. The images are of varying sizes, lighting conditions, and angles, making this a challenging classification task.

Data Split

• Training Set: 80% of the dataset
• Validation Set: 20% of the training data
• Test Set: 20% of the total dataset

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Model Architecture

The CNN model has been built using the Keras framework. The architecture includes:

• Convolutional Layers: Extract features from the input images.
• MaxPooling Layers: Reduce the spatial dimensions of feature maps.
• Dropout Layers: Prevent overfitting by dropping nodes during training.
• Dense Layers: Perform final classification using a softmax activation function.

Model Summary

The network includes:

• Two sets of Convolutional and MaxPooling layers.
• Dropout layers with a 50% drop rate to enhance generalization.
• A fully connected layer followed by a softmax output layer.

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Installation and Setup

Prerequisites

Make sure you have the following packages installed:

• Python (>= 3.6)
• TensorFlow
• Keras
• NumPy
• OpenCV
• Matplotlib
• Scikit-learn

Installation

1. Clone the repository:

   git clone https://github.com/waliulrayhan/Traffic-Signs-Classification.git
   cd Traffic-Signs-Classification

2. Install the required dependencies:

   pip install -r requirements.txt

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Training and Evaluation

Preprocessing

Images are preprocessed using OpenCV:

• Grayscale Conversion: Simplifies the image by reducing color channels.
• Histogram Equalization: Normalizes the lighting conditions.
• Normalization: Scales pixel values to the [0, 1] range.

Data Augmentation

To improve model generalization, data augmentation techniques such as width/height shifts, zoom, shear, and rotations are applied.

Training

To train the model, run:

python TrafficSigns_main.py

The model is trained using the Adam optimizer and categorical cross-entropy loss function. The training history, including accuracy and loss curves, is plotted for analysis.

Evaluation

The model is evaluated on the test set to measure performance. The final model is saved as model_trained.p for later use.

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Results and Visualizations

Accuracy Curves

Loss Curves

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Live Testing

You can test the model in real-time using a webcam. The model detects and classifies traffic signs with a specified probability threshold.

Running the Live Test

python TrafficSigns_test.py

• Press q to quit the live demo.
• The TrafficSigns_test.py script captures images from the webcam, preprocesses them, and classifies the traffic signs using the trained model.

Live Test Results

Below are some sample screenshots from the live testing:

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.