ArticleComputational intelligence and neuroscience2020
Driver Fatigue Detection Based on Convolutional Neural Networks Using EM-CNN.
Article in Computational intelligence and neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 139 citations in OpenAlex.
- Driver Drowsiness Detection using Machine Learning and Deep Learning Techniques: A Systematic Review.Archives of academic emergency medicine · 2026Article
- YOLO-FDCL: Improved YOLOv8 for Driver Fatigue Detection in Complex Lighting Conditions.Sensors (Basel, Switzerland) · 2025Article
- A Comprehensive Review of Unobtrusive Biosensing in Intelligent Vehicles: Sensors, Algorithms, and Integration Challenges.Bioengineering (Basel, Switzerland) · 2025Review
- EffRes-DrowsyNet: A Novel Hybrid Deep Learning Model Combining EfficientNetB0 and ResNet50 for Driver Drowsiness Detection.Sensors (Basel, Switzerland) · 2025Article
- Optimized driver fatigue detection method using multimodal neural networks.Scientific reports · 2025Article
- Artificial Intelligence-Driven Approaches to Managing Surgeon Fatigue and Improving Performance.Cureus · 2024Article
- Vision-based estimation of fatigue and engagement in cognitive training sessions.Artificial intelligence in medicine · 2024Article
- Miner Fatigue Detection from Electroencephalogram-Based Relative Power Spectral Topography Using Convolutional Neural Network.Sensors (Basel, Switzerland) · 2023Article
- Sensor-Based Classification of Primary and Secondary Car Driver Activities Using Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2023Article
- Fatigue Driving Detection Method Based on Combination of BP Neural Network and Time Cumulative Effect.Sensors (Basel, Switzerland) · 2022Article
- A Review of Recent Developments in Driver Drowsiness Detection Systems.Sensors (Basel, Switzerland) · 2022Review
- Research on the Training and Management of Industrializing Workers in Prefabricated Building with Machine Vision and Human Behaviour Modelling Based on Industry 4.0 Era.Computational intelligence and neuroscience · 2022Article
- Classifying Driving Fatigue by Using EEG Signals.Computational intelligence and neuroscience · 2022Article
- Convolutional Neural Networks Cascade for Automatic Pupil and Iris Detection in Ocular Proton Therapy.Sensors (Basel, Switzerland) · 2021Article
- SAI-YOLO: A Lightweight Network for Real-Time Detection of Driver Mask-Wearing Specification on Resource-Constrained Devices.Computational intelligence and neuroscience · 2021Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With a focus on fatigue driving detection research, a fully automated driver fatigue status detection algorithm using driving images is proposed. In the proposed algorithm, the multitask cascaded convolutional network (MTCNN) architecture is employed in face detection and feature point location, and the region of interest (ROI) is extracted using feature points. A convolutional neural network, named EM-CNN, is proposed to detect the states of the eyes and mouth from the ROI images. The percentage of eyelid closure over the pupil over time (PERCLOS) and mouth opening degree (POM) are two parameters used for fatigue detection. Experimental results demonstrate that the proposed EM-CNN can efficiently detect driver fatigue status using driving images. The proposed algorithm EM-CNN outperforms other CNN-based methods, i.e., AlexNet, VGG-16, GoogLeNet, and ResNet50, showing accuracy and sensitivity rates of 93.623% and 93.643%, respectively.
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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.