🔥 Wildfire Detection Using YOLOv8

An AI-powered wildfire detection system that uses YOLOv8 to analyze images and identify potential fire-related patterns. This project enhances environmental safety by providing an intuitive Streamlit-based interface for easy interaction.

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🚀 Features

✅ Image-Based Wildfire Detection – Upload images for fire detection using AI.
✅ YOLOv8 Object Detection – Utilizes advanced deep learning models for accuracy.
✅ Streamlit Web Interface – Provides a simple UI for users to upload and analyze images.
✅ Adjustable Confidence & IOU Thresholds – Customize detection precision.

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🛠️ Tech Stack

• YOLOv8 – Deep learning-based object detection.
• OpenCV – Image processing.
• Streamlit – Web-based UI for easy interaction.
• Python – Core programming language.

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📌 Installation & Setup

1️⃣ Create and Activate Virtual Environment

To keep dependencies organized, it's best to create a virtual environment:

# On Windows
python -m venv venv
venv\Scripts\activate

# On macOS/Linux
python3 -m venv venv
source venv/bin/activate

2️⃣ Install Dependencies

Once the virtual environment is activated, install the required packages:

pip install -r requirements.txt

3️⃣ Run the Streamlit App

Launch the Wildfire Detection UI using:

streamlit run 1_🔥_Home.py

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📊 How It Works

1️⃣ Upload an image through the web interface.
2️⃣ Set confidence and IOU thresholds for detection accuracy.
3️⃣ View detected wildfire zones with bounding boxes.

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📜 License

This project is open-source and available for further development.

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💡 Notes on Virtual Environments in README

✔ Yes, including virtual environment setup in the README is recommended for better dependency management.
✔ It ensures consistency across different setups.
✔ Especially useful in ML/DL projects where library versions matter.