Table of Contents

• Drone Detection System
• Features
• Dataset
• Files
• Example Outputs
• Getting Started
  • Prerequisites
  • Installation
• Usage
• Training
• Acknowledgments
• Contact
• Troubleshooting
• Future Improvements
• License

Drone Detection System

This is a Python-based drone detection system that utilizes YOLO (You Only Look Once) for object detection on a custom-trained drone dataset. It can detect drones in real time using a webcam or in video files. This project was created using OpenCV and YOLOv8.

Features

• Real-time drone detection using a webcam

• Drone detection on pre-recorded video files

• Custom YOLOv8 model trained on a drone detection dataset

  

Dataset

The model was trained on a custom drone detection dataset from Kaggle's YOLO Drone Detection Dataset.

Files

• data.yaml: Dataset configuration file for YOLOv8 training.
• drone.mp4: Example video file for testing the detection.
• main.py: Script for training the dataset.
• predict_video.py: Script for detection on a video file.
• predict_webcam.py: script for real-time detection using a webcam.
• yolov8n_trained.pt: Trained YOLOv8 model for drone detection.

Example Outputs

Here are some example outputs from the drone detection system:

Webcam Detection Example

• Description: An example of drone detection using a webcam.
• Output:

Video Detection Example

• Description: An example of drone detection on a pre-recorded video.
• Output: https://github.com/user-attachments/assets/092f8b2b-c220-4f24-851d-97a08353f136

Getting Started

Prerequisites

Ensure that Python 3.12.7 is installed on your machine.

Installation

1. Clone the repository:

   git clone https://github.com/bhavishyasingla1/drone_detection.git
   cd drone_detection

2. Install dependencies:

   pip install -r requirements.txt

Usage

1. Running Detection on Webcam:

   Run the following command:

   python predict_webcam.py

   This will open your webcam and start detecting drones in real-time.

2. Running Detection on a Video File:

   Modify video_path in predict_video.py to your desired video file path:

   video_path = 'path/to/your/video.mp4'

   Run the script:

   python predict_video.py

   Output: Detected videos will be saved as output.mp4 in the same directory as the script.

   

Training

To train the model on your dataset, ensure data.yaml is correctly configured, and then run main.py using the following command:

  python main.py

the output yolov8n_trained.pt will be saved in same directory as the script.

You can refer to the Kaggle YOLO Drone Detection Dataset for the dataset used. Watch Computer Vision Engineer - YouTube Video to learn how to train your own custom dataset.

Acknowledgments

• OpenCV for computer vision functionalities.
• YOLOv8 for object detection framework.
• Kaggle for providing the dataset.

Contact

If you have any questions or feedback, feel free to reach out:

• Email: [email protected]
• GitHub: Bhavishyasingla1

Troubleshooting

• ModuleNotFoundError: Ensure all dependencies are installed using pip install -r requirements.txt.

• Performance issues: Make sure your machine meets the required specifications for running YOLOv8.

  Future Improvements

• Implement multi-drone detection capabilities.

• Enhance model accuracy with more diverse datasets.

• Create a user interface for easier interaction.

  

License

This project is licensed under the MIT License - see the LICENSE file for details.