DroneVSBird

DroneVSBird is a machine learning web application that distinguishes between drones and birds in images using Histogram of Oriented Gradients (HOG) features and Support Vector Machine (SVM) classification. The project includes both a Flask backend API and a PHP frontend web interface, achieving 98.7% accuracy on the test set after hyperparameter tuning.

• Image Classification: Upload images through a web interface for instant drone/bird classification
• High Accuracy Model: Achieved 98.7% accuracy using SVM with HOG feature extraction
• Data Augmentation: Improved model robustness with augmented training datasets
• Multiple Classifiers: Evaluated and compared different machine learning models
• Docker Support: Easy deployment with Docker Compose for both frontend and backend

Tech Stack

• Backend: Python (Flask), PHP
• ML Libraries: scikit-learn, OpenCV, NumPy, Pandas, SciPy
• Frontend: HTML, CSS, JavaScript, PHP
• Tools: Jupyter Notebook, Docker, Git
• Deployment: Docker Compose

Screenshots

Web Interface

Classification Results

Installation

Option 1: Docker (Recommended)

The easiest way to run DroneVSBird is using Docker, which sets up both the Flask backend and PHP frontend automatically:

git clone https://github.com/JoshuaGlaZ/drone-vs-bird.git
cd drone-vs-bird
docker-compose up --build

Access the application:

• Flask Backend API: http://localhost:5000
• PHP Frontend Web Interface: http://localhost:8085

Option 2: Local Development

Backend Setup

git clone https://github.com/JoshuaGlaZ/drone-vs-bird.git
cd drone-vs-bird
pip install -r requirements.txt
python app.py

Frontend Setup

# Using PHP's built-in server
php -S localhost:8085

1. Navigate to http://localhost:8085
2. Click "Choose File" and select an image of a drone or bird
3. The system will process the image and display the result (Drone or Bird)