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

• URL: http://arxiv.org/abs/2511.12438v1
• Date: Sun, 16 Nov 2025 03:39:17 GMT
• Title: Real-Time Drivers' Drowsiness Detection and Analysis through Deep Learning
• Authors: ANK Zaman, Prosenjit Chatterjee, Rajat Sharma,
• Abstract summary: Drowsiness in driving can be life-threatening to any individual and can affect other drivers' safety.<n>This research develops a real-time driver drowsiness detection system utilizing deep convolutional neural networks (DCNNs) and OpenCV.
• Score: 0.1274452325287335
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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