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Deep Learning for Driver Drowsiness Detection

Duggal, Ravjyot Singh (2022) Deep Learning for Driver Drowsiness Detection. Masters thesis, Dublin, National College of Ireland.

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There is a state between awake and sleep which is drowsiness. Driver fatigue is the most common cause of road accidents in India. One of the recent studies carried out by a journal 1 states that the India accounts for 11% of casualties around the world in road accidents even though the country has only 1% of the global vehicle count. In contrast, the global deaths from road accidents is 11%. A system needs to be in place to overcome this problem. This research has tried to create a solution to alleviate this problem not only in India but in the entire globe. A model was developed with deep learning techniques for detecting driver drowsiness by facial points obtainment. The images were recorded at 15 frames per second (fps), state of the art Haar-cascade technology was used for detecting open and closed eyes. Convolutional neural network has been used here for detecting driver drowsiness particularly eyes blink, open and closure rate, and the face position.

Item Type: Thesis (Masters)
Horn, Christian
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
T Technology > TL Motor vehicles. Aeronautics. Astronautics
Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Tamara Malone
Date Deposited: 18 May 2023 12:54
Last Modified: 18 May 2023 12:54

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