Year: 2025
Type: article
Abstract: Driver drowsiness is a major contributor to road accidents worldwide. Fatigue and lack of sleep are common issues among drivers, often resulting in decreased alertness and increased risk of collisions. One effective way to prevent such accidents is by detecting and alerting drivers about their drowsiness in advance. Various methods exist for identifying signs of drowsiness. In this study, we propose a deep learning-based solution for detecting driver fatigue. Our approach leverages transfer learning using MobileNet, a lightweight convolutional neural network architecture. We utilized a dataset focused on the eye region to accurately identify signs of drowsiness.
Author: Mrs. V. Ratnasri
Language: English
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4409956687