Drowsiness Detection System Using Deep Learning

Sinha, Avigyan; Aneesh, R P; Gopal, Sarada K

doi:10.1109/icbsii51839.2021.9445132

Cited by 21 publications

(2 citation statements)

References 14 publications

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“…A novel downstream deep model is implemented to sufficiently process the driver's PPG signal by reconstructing the corresponding attention stage. Sinha et al [15] implemented various frameworks for analyzing the effectiveness of drowsiness detection based on facial areas. An innovative identification technique was developed by employing DL methods.…”

Section: Relevant Work In the Literaturementioning

confidence: 99%

Enhancing Road Safety: Deep Learning-Based Intelligent Driver Drowsiness Detection for Advanced Driver-Assistance Systems

Yang,

2024

Electronics

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Driver drowsiness detection is a significant element of Advanced Driver-Assistance Systems (ADASs), which utilize deep learning (DL) methods to improve road safety. A driver drowsiness detection system can trigger timely alerts like auditory or visual warnings, thereby stimulating drivers to take corrective measures and ultimately avoiding possible accidents caused by impaired driving. This study presents a Deep Learning-based Intelligent Driver Drowsiness Detection for Advanced Driver-Assistance Systems (DLID3-ADAS) technique. The DLID3-ADAS technique aims to enhance road safety via the detection of drowsiness among drivers. Using the DLID3-ADAS technique, complex features from images are derived through the use of the ShuffleNet approach. Moreover, the Northern Goshawk Optimization (NGO) algorithm is exploited for the selection of optimum hyperparameters for the ShuffleNet model. Lastly, an extreme learning machine (ELM) model is used to properly detect and classify the drowsiness states of drivers. The extensive set of experiments conducted based on the Yawdd driver database showed that the DLID3-ADAS technique achieves a higher performance compared to existing models, with a maximum accuracy of 97.05% and minimum computational time of 0.60 s.

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Section: Relevant Work In the Literaturementioning

confidence: 99%

Enhancing Road Safety: Deep Learning-Based Intelligent Driver Drowsiness Detection for Advanced Driver-Assistance Systems

Yang,

2024

Electronics

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“…This technology continuously monitors the driver's behavior through a camera being placed in front of them. [13] The camera captures the real-time video, which is then analyses for detecting drowsiness of the driver and give timely alerts when needed. [9] When the system detects signs of drowsiness, it triggers an alarm to warn the driver, prompting him to take immediate action such as taking breaks.…”

Section: Introductionmentioning

confidence: 99%

Drowsy Driver Detection System

Dey,

Majhi,

Koda

et al. 2024

IJRASET

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Drowsy driver detection system is a safety technology designed to identify the potential signs of fatigue or drowsiness of the driver to prevent accidents caused as a result of not being alert. It is a significant risk factor for traffic accidents which has led to thousands of accidents, injuries, and fatalities each year. In this system we have used some techniques to monitor the driver’s behavior or state. This involves tracking facial features, eye movements or blink patterns and yawning. The system identifies signs of fatigue, such as slow eyelid closure, increased blink duration and yawn which often indicate drowsiness. As technology advances, these systems are becoming more accurate, efficient and integrated with other vehicle safety features. The continued development of drowsy driver detection technology holds significant promise for reducing traffic accidents and improving overall road safety.

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