shib1111111/traffic-sign-recognition

This project aims to build a Traffic Sign Recognition system using deep learning techniques.

Traffic Sign Recognition

Overview

This project aims to build a Traffic Sign Recognition system using deep learning techniques. The system's primary objective is to accurately classify different types of traffic signs, contributing to automated sign detection and interpretation on roads.

Features

• Classification: The system can classify various categories of traffic signs, including speed limits, regulatory signs, and warning signs.
• CNN Architecture: Utilizes a Convolutional Neural Network (CNN) architecture for efficient feature extraction and classification of traffic sign images.
• High Accuracy: Achieves a Test Accuracy of 98%, ensuring reliable and accurate recognition of traffic signs.

Project Architecture

The project architecture consists of several key components:

1. Data Preparation:

   • Collection and annotation of a dataset containing images of different traffic signs.
   • Preprocessing techniques such as resizing, normalization, and augmentation are applied to enhance the dataset's quality and diversity.

2. Model Development:

   • Designing a CNN model using the Keras framework, comprising convolutional layers, max-pooling layers, batch normalization, dropout, and dense layers.
   • Training the model to classify traffic sign images into distinct categories.

3. Training:

   • Splitting the dataset into training and validation sets.
   • Training the model on the training set using optimization techniques and data augmentation to improve performance and generalization.

4. Evaluation:

   • Evaluating the trained model on a separate test set to assess its accuracy and effectiveness.
   • Computing evaluation metrics such as accuracy, precision, recall, and F1-score to quantify the model's performance.

5. Model Deployment:

   • Saving the trained model for deployment in real-world applications.
   • Integrating the model into systems for real-time traffic sign recognition tasks in using streamlit wep app.

Web App

Getting Started

Before you can run the this app, ensure that you have the necessary prerequisites installed on your machine.

Prerequisites

Make sure you have the following installed:

• Python 3.x: The programming language used to run the app.

Installation

Follow these steps to set up the this app on your local machine:

1. Clone the Repository:

   Open your terminal and run the following commands:

   git clone https://github.com/shib1111111/Traffic-Sign-Recognition.git
   cd Traffic-Sign-Recognition
   

2. Install Dependencies:

Run the following command to install the required dependencies:

pip install -r requirements.txt

Usage

To run the this app, execute the following command in your terminal:

streamlit run app.py  

Visit the provided local URL (usually http://localhost:8501) in your web browser to access the app.

or directly go to deployed server url : https://traffic-sign-recognition-00.streamlit.app/

Contributing

We welcome contributions to enhance this. Feel free to open issues or submit pull requests.

License

This project is licensed under the MIT License.

Thank you for using this! Feel free to reach out with any questions or feedback.

✨ --- Designed & made with Love by Shib Kumar Saraf ✨