Deep Learning-Based Drowsiness Detection System Using IoT

Himaswi, Nunnagoppula; Sathvika, Kalivarapu; Pathima, Shaik; K, Sita Kumari.

doi:10.1109/iitcee57236.2023.10090863

Cited by 2 publications

(1 citation statement)

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“…In [17], a solution was established that addresses all the difficulties of the Raspberry Pi camera but remains quite portable and proficient. By employing the OpenCV technique and tasks, the Raspberry Pi camera converts the video stream of a driver to grayscale and frame images.…”

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%