Mini C-RAM System

Table of Contents

• Project Overview
• Key Features
• System Architecture
• Installation
• Usage
• Hardware Setup
• Testing
• Contributing
• License
• Maintainers
• Citations and Acknowledgements

Project Overview

A real-time counter-drone system using computer vision and object tracking. Detects UAVs in video streams and simulates countermeasures (laser activation).

Object Detection Demo Video

Key Features

• Real-time object detection using YOLOv11n (or any other compatible AI model)
• Centroid-based object tracking
• Frame processing pipeline (1920x1080 @ 120FPS)
• Hardware control interface for laser systems
• Configurable detection thresholds and tracking parameters

System Architecture

.
├── images
│   ├── drone_mock_test_1.jpg         # Sample mock test image
│   ├── drone_mock_test_2.jpg         # Sample mock test image
│   ├── drone_mock_test_3.jpg         # Sample mock test image
│   ├── drone_real_test_1.jpg         # Sample real test image
│   ├── drone_real_test_2.jpg         # Sample real test image
│   ├── drone_real_test_3.jpg         # Sample real test image
│   ├── drone_real_test_4.jpg         # Sample real test image
│   ├── drone_real_test_5.jpg         # Sample real test image
│   ├── drone_real_test_6.jpg         # Sample real test image
│   ├── drone_real_test_7.jpg         # Sample real test image
│   ├── drone_real_test_8.jpg         # Sample real test image
│   ├── drone_real_test_9.jpg         # Sample real test image
│   ├── drone_real_test_10.jpg        # Sample real test image
│   ├── drone_real_test_11.jpg        # Sample real test image
│   ├── drone_real_test_12.jpg        # Sample real test image
│   ├── drone_real_test_13.jpg        # Sample real test image
│   ├── drone_real_test_14.jpg        # Sample real test image
│   └── drone_real_test_15.jpg        # Sample real test image
├── src
│   ├── ai_model_interface.py         # AI model wrapper
│   ├── control_output_manager.py     # Laser control interface (GPIO/PWM)
│   ├── detection_processor.py        # Filters/processes AI detections
│   ├── drone_detector_ai.pt          # YOLOv11n drone detector model
│   ├── frame_pipeline.py             # Main processing workflow
│   ├── frame_processor.py            # Frame resizing/normalization
│   ├── main.py                       # Program execution code
│   ├── tracking_system.py            # Object tracking implementation
│   └── video_stream_manager.py       # Camera/stream input handling
└── test
    ├── test_ai_model_interface.py    # Unit test for AI model interface
    ├── test_ai.py                    # Detection test for AI model
    ├── test_detection_processor.py   # Unit test for detection processor
    ├── test_frame_processor.py       # Unit test for frame processor
    ├── test_tracking_system.py       # Unit test for tracking system
    └── test_video_stream_manager.py  # Unit test for video stream manager

Installation

Prerequisites

• Python 3.11.1+
• Windows 11 or Ubuntu 20.04
• USB Webcam or IP Camera

# Clone repository
git clone https://github.com/alonsovazqueztena/Mini_C-RAM_Capstone.git
cd Mini_C-RAM_Capstone

# Create virtual environment
python -m venv env
source env/bin/activate      # Linux/MacOS
source env/Scripts/activate  # Windows

# Install dependencies
pip install -r requirements.txt

Usage

Basic Operation

# Execute the program using the source code (must be in src folder)
python main.py

Keyboard Control

Key	Description
q	Quit system
p	Pause processing
d	Toggle debug overlay

Hardware Setup

Camera Connection

• USB Webcam: Plug into available USB port
• IP Camera: Set RTSP URL

Laser Control

• Connect laser module to GPIO 18
• Power: 5V PWM compatible laser diode

Testing

Run validation tests in test folder:

# Run all unit tests of the program.
pytest

# Run an AI model detection test of image.

python test_ai.py   # Results in runs folder

Contributing

1. Fork the repository
2. Create a feature branch:

git checkout -b feature/new-tracker

3. Add tests for new functionality
4. Submit a pull request

Coding Standards

• PEP8 compliance
• Type hints for public methods
• Docstrings for all modules
• 80%+ test coverage

License

MIT License - See LICENSE for details

Maintainers

• Alonso Vazquez Tena
• Daniel Saravia

Mentor: Ryan Woodward
University of Advanced Robotics, 2023

Citations and Acknowledgements

YOLOv11n

Glenn Jocher and Jing Qiu.
Ultralytics YOLO11, version 11.0.0 (2024)
GitHub Repository
ORCID: 0000-0001-5950-6979, 0000-0002-7603-6750, 0000-0003-3783-7069
License: AGPL-3.0