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FireGuard: Automated Fire Detection and Monitoring System

FireGuard is an intelligent fire detection and monitoring system designed to proactively identify fire hazards using thermal imaging technology and provide real-time alerts through a web interface.

System Overview

FireGuard consists of three main components:

  • Hardware: An embedded system built on AVR microcontroller with thermal imaging camera, stepper motors for scanning, and other sensors
  • Firmware: C code running on the AVR microcontroller
  • Web Interface: Python Flask server and responsive web UI for monitoring and control

System Features

  • 360° Thermal Scanning: Automated patrol mode that continuously scans the environment
  • High-Precision Fire Detection: Uses the MLX90640 thermal imaging sensor to detect heat signatures
  • Real-time Monitoring: Web interface displays temperature data and fire detection status
  • Automatic Targeting: When a potential fire is detected, the system locks onto the target for monitoring
  • Distance Measurement: Uses ultrasonic sensor to measure distance to the fire
  • Alert System: Visual and audible alerts through LCD display and buzzer
  • Remote Control: Web interface allows remote testing and system reset

Hardware Components

Sensors

  • MLX90640 Thermal Camera: 32x24 IR array thermal imaging sensor (I²C)
  • HC-SR04 Ultrasonic Sensor: Measures distance to fire
  • 16x2 LCD Display: Provides status information

Actuators

  • Bipolar Stepper Motors (x2): For horizontal and vertical scanning movements
  • Servo Motor: For fine adjustment of thermal camera position
  • Buzzer: For audible fire alerts

Microcontroller

  • AVR ATmega328P: Main processing unit running at 7.3728MHz

Connectivity

  • UART-to-USB Converter: For communication with the web interface (230400 baud)

Circuit Connections

MLX90640 Thermal Camera

  • SDA → PC4 (AVR I²C Data)
  • SCL → PC5 (AVR I²C Clock)
  • VCC → 3.3V
  • GND → GND

Stepper Motors (using EasyDriver)

  • STEP_PIN_BTM → PC1 (Bottom stepper step control)
  • STEP_PIN_TOP → PC2 (Top stepper step control)
  • DIR_PIN → PC3 (Direction control for both steppers)
  • VCC → 5V
  • GND → GND

Ultrasonic Sensor (HC-SR04)

  • TRIG → PD6
  • ECHO → PD7
  • VCC → 5V
  • GND → GND

LCD Display (16x2)

  • RS → PB0
  • E → PB1
  • D4 → PB2
  • D5 → PB3
  • D6 → PB4
  • D7 → PB5
  • VCC → 5V
  • GND → GND

Buzzer

  • Signal → PD5
  • GND → GND

UART Connection

  • TX → PD1
  • RX → PD0
  • Connected to computer via USB-to-UART adapter

Software Architecture

Firmware (AVR C)

  • FireGuard.c: Main program logic
  • I2C.c/h: Communication with MLX90640 thermal camera
  • stepper.c/h: Stepper motor control for scanning
  • servo.c/h: Servo motor control for fine positioning
  • ultrasonic.c/h: Distance measurement
  • buzzer.c/h: Alert system
  • lcd.c/h: Display interface

Web Interface

  • server.py: Flask server that handles serial communication and API endpoints
  • FireGuard.html: Responsive web UI with real-time data visualization
  • assets/: CSS, JavaScript, and image resources

Installation and Setup

Firmware

  1. Connect the AVR programmer to your computer
  2. Navigate to the Firmware directory
  3. Compile and upload using the included Makefile: 
    cd FireGuard/Firmware/src
make
make flash

Web Interface

  1. Install Python 3.7 or higher
  2. Install required packages: 
    cd FireGuard/App
pip install -r requirements.txt
  3. Connect the FireGuard hardware via USB
  4. Run the server: 
    python server.py
  5. Open a web browser and navigate to http://localhost:3000

Troubleshooting

Serial Connection Issues

  • The default serial port is /dev/cu.usbserial-A101167E. If your system uses a different port, modify the specific_port variable in server.py
  • Ensure the baud rate is set to 230400
  • Check USB connections and drivers

Temperature Sensor Issues

  • The MLX90640 requires a warm-up period of approximately 10 seconds
  • Ensure proper I²C connections and address (0x33)

Motor Movement Issues

  • Check stepper motor connections and power supply
  • Verify the EasyDriver configuration (microstepping, current limiting)

Future Enhancements

  • Integration with smart home systems (HomeKit, Google Home)
  • Cloud connectivity for remote monitoring
  • Machine learning for improved fire detection accuracy
  • Automatic fire suppression capabilities

License

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

Contributors

  • Owen Zeng
  • Leikhang Xiong
  • Robert Zhang