ArticleJournal of imaging2020
Real-Time System for Driver Fatigue Detection Based on a Recurrent Neuronal Network.
Article in Journal of imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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Who cites it
14 citing papers in PubMed.
- Unsupervised Identification of Driving Styles from Naturalistic Driving Data Through a Context-Normalized Framework.Sensors (Basel, Switzerland) · 2026Article
- Emotion-Aware Contextual Modelling for Robust Driver Fatigue Detection.Sensors (Basel, Switzerland) · 2026Article
- STFTransNet: A Transformer Based Spatial Temporal Fusion Network for Enhanced Multimodal Driver Inattention State Recognition System.Sensors (Basel, Switzerland) · 2025Article
- EffRes-DrowsyNet: A Novel Hybrid Deep Learning Model Combining EfficientNetB0 and ResNet50 for Driver Drowsiness Detection.Sensors (Basel, Switzerland) · 2025Article
- Research on low-power driving fatigue monitoring method based on spiking neural network.Experimental brain research · 2024Article
- Article
- Miner Fatigue Detection from Electroencephalogram-Based Relative Power Spectral Topography Using Convolutional Neural Network.Sensors (Basel, Switzerland) · 2023Article
- Real-Time Machine Learning-Based Driver Drowsiness Detection Using Visual Features.Journal of imaging · 2023Article
- EEG driving fatigue detection based on log-Mel spectrogram and convolutional recurrent neural networks.Frontiers in neuroscience · 2023Article
- Computer vision-based approach to detect fatigue driving and face mask for edge computing device.Heliyon · 2022Article
- A Review of Recent Developments in Driver Drowsiness Detection Systems.Sensors (Basel, Switzerland) · 2022Review
- Research on Real-Time Face Key Point Detection Algorithm Based on Attention Mechanism.Computational intelligence and neuroscience · 2022Article
- Convolutional Neural Network for Drowsiness Detection Using EEG Signals.Sensors (Basel, Switzerland) · 2021Article
- Driver Fatigue Detection Systems Using Multi-Sensors, Smartphone, and Cloud-Based Computing Platforms: A Comparative Analysis.Sensors (Basel, Switzerland) · 2020Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, the rise of car accident fatalities has grown significantly around the world. Hence, road security has become a global concern and a challenging problem that needs to be solved. The deaths caused by road accidents are still increasing and currently viewed as a significant general medical issue. The most recent developments have made in advancing knowledge and scientific capacities of vehicles, enabling them to see and examine street situations to counteract mishaps and secure travelers. Therefore, the analysis of driver's behaviors on the road has become one of the leading research subjects in recent years, particularly drowsiness, as it grants the most elevated factor of mishaps and is the primary source of death on roads. This paper presents a way to analyze and anticipate driver drowsiness by applying a Recurrent Neural Network over a sequence frame driver's face. We used a dataset to shape and approve our model and implemented repetitive neural network architecture multi-layer model-based 3D Convolutional Networks to detect driver drowsiness. After a training session, we obtained a promising accuracy that approaches a 92% acceptance rate, which made it possible to develop a real-time driver monitoring system to reduce road accidents.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.