🚗 DashSentinel

Real-time Driver Drowsiness Detection System with Embedded Feedback Interface

Left: Real-time facial tracking and attentiveness scoring

Right: ESP8266 hardware interface

Overview

The system combines:

• Computer Vision (MediaPipe / OpenCV) for facial feature extraction
• Machine Learning / Heuristics for drowsiness classification
• Embedded System (ESP8266) for real-time display and user interaction

It is designed as a prototype for a deployable driver safety product in fleets/personal use.

Features

• Real-time face tracking and feature extraction

• Driver attentiveness scoring (%)

• Drowsiness detection based on:

  • eye closure patterns
  • yawning detection
  • attentiveness %

• OLED display output:

  • status (ALERT / WARNING / DROWSY)

• LED indicators:

  • 🟢 ALERT → solid green
  • 🟡 WARNING → blinking yellow
  • 🔴 DROWSY → fast blinking red

• Physical buttons:

  • Reset baseline (user-specific calibration)
  • Reset stats (data stored over-time)

Tech Stack

💾 Software

• Python 3
• OpenCV
• MediaPipe
• TensorFlow (optional for model extensions)
• PySerial

⚙️ Hardware

• ESP8266 (NodeMCU)
• SSD1306 OLED (I2C, 128x64)
• Push buttons (x2)
• LEDs (red, yellow, green)
• Resistors (3x 330 ohms)

📁 Project Structure

DashSentinel/
├── run_dashsentinel.py        # main entrypoint for execution
│
├── src/                       # core application logic
│   ├── app.py                 # main app orchestration
│   ├── features.py            # feature extraction (ear, yawning, etc.)
│   ├── model.py               # scoring / detection logic
│   └── serial.py              # esp8266 communication layer
│
├── DisplayModule/             # esp8266 firmware (platformio project)
│   └── src/
│       └── main.cpp           # oled, leds, buttons logic
│
├── nodemcu_carrier_pcb/       # hardware design (KiCad)
|
├── schematic/                 # schematic for all connections
│
├── data/                      # runtime-generated data
│   └── driver_profile.json    # user-specific baseline + stats
|
├── media/                     # Images showing the system in operation
│
├── requirements.txt           # python dependencies
└── README.md                  # project documentation

Install requirements

pip install -r requirements.txt

▶️ Run the Project

With UI and ESP8266 Interface

python3 run_dashsentinel.py --show-ui --draw-landmarks --enable-esp-serial --esp-port /dev/ttyUSB0

Headless mode (logging enabled)

python3 run_dashsentinel.py --headless --log-csv --enable-esp-serial --esp-port /dev/ttyUSB0

Optional flags (see src/cli.py for full list of options)

python3 run_dashsentinel.py --show-ui --draw-landmarks --refine-landmarks

Startup Calibration

At startup:

• system learns user-specific facial metrics
• establishes baseline EAR and behavior

Reset options

• Hardware button → reset baseline
• CLI flag → rebuild baseline at launch

python3 run_dashsentinel.py --show-ui --mirror --rebuild-baseline-on-start

🚀 Future Improvements

• Replace heuristic model with trained ML model
• Mobile app integration
• Full standalone embedded unit (dashcam form factor)