An image processing-based system proposal for real-time detection of drowsiness from a vehicle driver's eye movements

Silahtaroğlu, Murat; Dereli, Serkan

doi:10.33793/acperpro.04.01.15

Cited by 1 publication

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References 9 publications

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“…A study by Alhaddad et al [30] proposed an image-processing-based system for detecting driver drowsiness using EAR and blinking analysis. The study used a private dataset and achieved a detection accuracy of 92.10%.…”

Section: Related Workmentioning

confidence: 99%

A Deep-Learning Approach to Driver Drowsiness Detection

Ahmed,

Alabdulkarem,

Alomair

et al. 2023

Safety

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Drowsy driving is a widespread cause of traffic accidents, especially on highways. It has become an essential task to seek an understanding of the situation in order to be able to take immediate remedial actions to detect driver drowsiness and enhance road safety. To address the issue of road safety, the proposed model offers a method for evaluating the level of driver fatigue based on changes in a driver’s eyeball movement using a convolutional neural network (CNN). Further, with the help of CNN and VGG16 models, facial sleepiness expressions were detected and classified into four categories (open, closed, yawning, and no yawning). Subsequently, a dataset of 2900 images of eye conditions associated with driver sleepiness was used to test the models, which include a different range of features such as gender, age, head position, and illumination. The results of the devolved models show a high degree of accountability, whereas the CNN model achieved an accuracy rate of 97%, a precision of 99%, and recall and F-score values of 99%. The VGG16 model reached an accuracy rate of 74%. This is a considerable contrast between the state-of-the-art methods in the literature for similar problems.

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Section: Related Workmentioning

confidence: 99%