Real-Time Deep Learning-Based Drowsiness Detection: Leveraging Computer-Vision and Eye-Blink Analyses for Enhanced Road Safety
Drowsy driving can significantly affect driving performance and overall road safety. Statistically, the main causes are decreased alertness and attention of the drivers. The combination of deep learning and computer-vision algorithm applications has been proven to be one of the most effective approa...
Main Authors: | Furkat Safarov, Farkhod Akhmedov, Akmalbek Bobomirzaevich Abdusalomov, Rashid Nasimov, Young Im Cho |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2023-07-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/23/14/6459 |
Similar Items
-
Blinking Eyes Detection to Monitor Drowsy Drivers Due to Fatigue Using MATLAB Cascade Object Detector
by: Zulfikri Paidi, et al.
Published: (2021-09-01) -
Blink-induced changes in pupil dynamics are consistent and heritable
by: Şükrü Barış Demiral, et al.
Published: (2024-11-01) -
Directional biases for blink adaptation in voluntary and reflexive eye blinks
by: Lau, Wee Kiat, et al.
Published: (2020) -
Designing a glass mounted warning system to prevent drivers to fall in sleep based on neck posture and blinking duration
by: Niloufar Teyfour, et al.
Published: (2021-01-01) -
EYE ASPECT RATIO ADJUSTMENT DETECTION FOR STRONG BLINKING SLEEPINESS BASED ON FACIAL LANDMARKS WITH EYE-BLINK DATASET
by: Eswin Syahputra, et al.
Published: (2023-02-01)