Citation map · Study

SZodixStudyAcademic Library

Shelves Open access

Study · an open academic library·Metadata from OpenAlex (CC0)·Shelves·OpenAlex metadata, de-duplicated

Driver Safety Development: Real-Time Driver Drowsiness Detection System Based on Convolutional Neural Network

12 references·6 citing·35 second hop

FromToDepth

this work references cited by second hop

References

2012Detecting Driver Drowsiness Based on Sensors: A Review783 cited
2016Towards Detection of Bus Driver Fatigue Based on Robust Visual Analysis of Eye State328 cited
2014YawDD294 cited
2019Real-Time Driver-Drowsiness Detection System Using Facial Features281 cited
2018Real-time Driver Drowsiness Detection for Android Application Using Deep Neural Networks Techniques251 cited
2019A Realistic Dataset and Baseline Temporal Model for Early Drowsiness Detection218 cited
2017Driver drowsiness detection using behavioral measures and machine learning techniques: A review of state-of-art techniques162 cited
2016The ULg multimodality drowsiness database (called DROZY) and examples of use150 cited
2017Identification and Analysis of Driver Postures for In-Vehicle Driving Activities and Secondary Tasks Recognition147 cited
2019Real-Time Driver Drowsiness Detection System Using Eye Aspect Ratio and Eye Closure Ratio146 cited
2019Convolutional Two-Stream Network Using Multi-Facial Feature Fusion for Driver Fatigue Detection123 cited
2019Vigilance Estimation Using a Wearable EOG Device in Real Driving Environment118 cited

Cited by

2021A Survey on Driver Behavior Analysis From In-Vehicle Cameras107 cited
2025Real-time driver drowsiness detection using transformer architectures: a novel deep learning approach

Only edges whose both ends are imported exist here. Widen the year window, or show the whole map. Back to the work

99 cited

2023A CNN-Based Approach for Driver Drowsiness Detection by Real-Time Eye State Identification90 cited

2025Real-Time Driver Drowsiness Detection Using Facial Analysis and Machine Learning Techniques89 cited

2021A novel approach for driver fatigue detection based on visual characteristics analysis66 cited

2024A Real-Time Embedded System for Driver Drowsiness Detection Based on Visual Analysis of the Eyes and Mouth Using Convolutional Neural Network and Mouth Aspect Ratio45 cited