Project Hub /Automotive EV & Transportation/Drowsiness Alert System/Driver Drowsiness Detector System using Raspberry Pi and OpenCV

Intermediate

RASPBERRY PI

₹5,000 – ₹15,000

3 – 6 Hours

Complete Kit

Driver Drowsiness Detector System using Raspberry Pi and OpenCV

Build a real-time driver drowsiness detection and alerting system using a Raspberry Pi, Pi camera module, OpenCV, and Python.

Originally published on CircuitDigest

View original tutorial

Parts You'll Need

This project is sourced from another site, not written by us — this list is our best match of their components against what we stock, not a verified bill of materials. Always check the original blog (linked above) for the actual parts list before buying.

Raspberry Pi 3 - Model B+ 1.4Ghz 64-bit quad-core Processor with 1 GB Ram (Original) Rs. 5,400.00
ESP32 CAM WiFi Module Bluetooth with OV2640 Camera Rs. 810.00
Cable for Arduino Nano (USB 2.0 A to USB 2.0 Mini B) 30cm Rs. 69.00
Piezo Buzzer (Big) 6-12V Rs. 45.00
400 Points Mini Solderless Prototype PCB Breadboard High Quality Rs. 89.00
MB102 Adjustable Breadboard Power Supply Module (3.3V & 5V) for Arduino & Raspberry Pi Rs. 139.00
Jumper Wire 10 Pin Male To Male 20cm Rs. 45.00
Jumper Wire 10 Pin Male To Female 20cm Rs. 45.00

Selected: Rs. 0.00

Browse all components

How It Works

The Driver Drowsiness Detector System operates by combining computer vision and physical output components controlled by a Raspberry Pi 4 Model B. First, the Pi Camera Module continuously captures real-time video frames of the driver's face inside the vehicle. These video frames are processed using Python3 alongside specialized software libraries like OpenCV, dlib, and face_recognition. The system uses these packages to locate facial landmarks, specifically focusing on the driver's eyes. By calculating metrics such as the Eye Aspect Ratio (EAR) using NumPy arrays, the software can determine whether the eyes are open or closed over a consecutive number of frames. If the eyes remain closed past a specific threshold—indicating fatigue or drowsiness—the Raspberry Pi executes a script utilizing RPi.GPIO. This script sends an electrical signal to trigger an active buzzer connected to the general-purpose input/output pins. The buzzer immediately sounds an audible alarm to alert the driver, helping to prevent potential accidents caused by micro-sleeps behind the wheel.

Why Build This

Gain hands-on experience building an advanced automotive safety prototype that can be showcased in high school science fairs or engineering portfolios.

Understand how computer vision algorithms in Python can interact with physical hardware components like buzzers using a Raspberry Pi.

Explore practical solutions for mitigating highway fatigue and improving commercial vehicle safety through modern technology.

Real-World Application

Automotive safety systems, fleet management, and commercial vehicle monitoring to prevent fatigue-related highway accidents.

Skills You'll Learn

Computer vision programming

Facial landmark detection

Python script execution

GPIO pin control

Real-time video processing

Sensor integration

Safety Precautions

No specific precautions noted for this build — always follow general electronics safety practices.

Technology Tags

RASPBERRY PI

OPENCV

PYTHON

DLIB

FACE_RECOGNITION

COMPUTER VISION

IMAGE PROCESSING

SENSORS

BUZZER

SAFETY SYSTEMS

Frequently Asked Questions

Can I use a Raspberry Pi 3 instead of a Raspberry Pi 4 Model B?

Yes, the project data lists compatibility with the Raspberry Pi 3, though processing frame rates may vary slightly depending on the hardware version.

Do I need an internet connection to run the drowsiness detection?

No, once OpenCV, dlib, Python3, and the required libraries are installed on your Raspberry Pi, the system can run locally without needing active internet access.

How does the buzzer connect to the Raspberry Pi?

The buzzer connects to the Raspberry Pi using the RPi.GPIO interface, allowing the Python script to trigger the alarm sound by sending a digital signal to the designated GPIO pin.

Gallery

This page is part of Kitkraft's Project Hub — a curated collection of electronics & robotics projects gathered from the maker community to help students and hobbyists discover and learn to build. Kitkraft does not claim authorship of the original project, and makes no representation as to the accuracy, completeness or safety of third-party source material. The summary and "How It Works" explanation above are AI-generated, rewritten with reference to the original tutorial — not a reproduction of it. Full credit and a direct link to the original source are provided above. If you're the original creator and have a concern about how your work is represented here, please contact us and we'll address it promptly.

Kitkraft Project Hub — curated from the maker community, credited at the source.

PRJ-00269