Computer Vision Project | Driver drowsiness detection | Full code explanation | OpenCV Python Dlib

Science & Technology

Introduction

In this article, we will explore a computer vision project focused on detecting driver drowsiness using eye blink detection. This project aims to alert drivers when they appear to be falling asleep. We will explain the complete code step-by-step, including model setup, dependencies, and algorithm functionalities.

Overview of the Project

Our drowsiness detection project leverages a simple model that has evolved since its initial demonstration three years ago. Originally, our project could only track eye blinks and alert users if they were sleeping or not. We have since expanded its capabilities significantly.

Downloading the Code

To get started, you can download the full code from the provided link. In addition to the main script, you will also need to download a specific model file (named model), which is crucial for the functionality of the detection system. Once you have downloaded the necessary files and libraries, we can move on to setting up the environment.

Environment Setup

This project operates on Python 3.8.8, and it is necessary to install the required libraries. The code is structured in a way that it first validates the availability of the necessary libraries and then loads the detection model to initiate the process.

Code Walkthrough

  1. Importing Libraries: The code begins by importing all necessary libraries.

  2. Model Configuration: The model is loaded, and face detection capabilities are initialized, including the setup for eye blink detection.

  3. Define Image Class: This portion of the code defines how images will be processed, particularly focusing on histogram-based techniques.

  4. Blink Detection Methodology: We define specific methods for counting blinks and triggering alarms.

  5. Running the Application: The main loop runs the detection algorithm in a specified directory, observing for eyes that blink, and counting each blink occurrence.

Calibration Process

After setting everything up, the algorithm proceeds to assess the calibration process. It checks for instances of blinks—one blink through to several blinks—and measures the frequency over time. This frequency helps determine whether the driver is drowsy or not.

An alarm mechanism is integrated, and if the eyes remain closed for a prolonged period, it triggers an alert. This setup can also be implemented on simpler hardware, such as Raspberry Pi, allowing for a versatile deployment in various settings.

Final Remarks

This project is an excellent way to apply computer vision and machine learning techniques using Python, OpenCV, and Dlib. For more information and updates, please consider supporting our channel.

Keywords

  • Computer Vision
  • Drowsiness Detection
  • Eye Blink Detection
  • Python
  • OpenCV
  • Dlib
  • Model
  • Alarm System
  • Raspberry Pi

FAQ

Q1: What is the main objective of the drowsiness detection project?
A1: The main objective of the project is to detect driver drowsiness through eye blink monitoring and to alert drivers if they appear to be falling asleep.

Q2: Which libraries are required for this project?
A2: The project requires Python libraries like OpenCV and Dlib, among others, for functionality.

Q3: Can the code be run on a simple laptop?
A3: Yes, the algorithm can be run on a typical laptop or desktop with Python installed and the required libraries.

Q4: Is it possible to implement this project on Raspberry Pi?
A4: Absolutely! The project can be implemented on Raspberry Pi using a camera for real-time detection.

Q5: How can I get the code for this project?
A5: You can download the full code from the specified link in the article.