Real-Time Drivers' Drowsiness Detection and Analysis through Deep Learning

TL;DR

This paper presents a real-time driver drowsiness detection system using deep convolutional neural networks and OpenCV, capable of accurately identifying drowsiness through facial landmarks to enhance road safety.

Contribution

The study develops and implements a cost-effective, non-invasive deep learning-based system for real-time driver drowsiness detection using facial landmarks.

Findings

Achieved 99.6% accuracy on NTHU-DDD dataset.

Achieved 97% accuracy on Yawn-Eye-Dataset.

System successfully triggers alerts in real-time.

Abstract

A long road trip is fun for drivers. However, a long drive for days can be tedious for a driver to accommodate stringent deadlines to reach distant destinations. Such a scenario forces drivers to drive extra miles, utilizing extra hours daily without sufficient rest and breaks. Once a driver undergoes such a scenario, it occasionally triggers drowsiness during driving. Drowsiness in driving can be life-threatening to any individual and can affect other drivers' safety; therefore, a real-time detection system is needed. To identify fatigued facial characteristics in drivers and trigger the alarm immediately, this research develops a real-time driver drowsiness detection system utilizing deep convolutional neural networks (DCNNs) and OpenCV.Our proposed and implemented model takes real- time facial images of a driver using a live camera and utilizes a Python-based library named OpenCV to examine the facial images for facial landmarks like sufficient eye openings and yawn-like mouth movements. The DCNNs framework then gathers the data and utilizes a per-trained model to detect the drowsiness of a driver using facial landmarks. If the driver is identified as drowsy, the system issues a continuous alert in real time, embedded in the Smart Car technology.By potentially saving innocent lives on the roadways, the proposed technique offers a non-invasive, inexpensive, and cost-effective way to identify drowsiness. Our proposed and implemented DCNNs embedded drowsiness detection model successfully react with NTHU-DDD dataset and Yawn-Eye-Dataset with drowsiness detection classification accuracy of 99.6% and 97% respectively.