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Preprint (Author's original)arXiv.org - Non-exclusive license to distribute, Open
Preprint
Towards Evaluating Driver Fatigue with Robust Deep Learning Models
arXiv.org
08 Feb 2021
Abstract
In this paper, we explore different deep learning based approaches to detect
driver fatigue. Drowsy driving results in approximately 72,000 crashes and
44,000 injuries every year in the US and detecting drowsiness and alerting the
driver can save many lives. There have been many approaches to detect fatigue,
of which eye closedness detection is one. We propose a framework to detect eye
closedness in a captured camera frame as a gateway for detecting drowsiness. We
explore two different datasets to detect eye closedness. We develop an eye
model by using new Eye-blink dataset and a face model by using the Closed Eyes
in the Wild (CEW). We also explore different techniques to make the models more
robust by adding noise. We achieve 95.84% accuracy on our eye model and 80.01%
accuracy on our face model. We also see that we can improve our accuracy on the
face model by 6% via adversarial training and data augmentation. We hope that
our work will be useful to the field of driver fatigue detection to avoid
potential vehicle accidents related to drowsy driving.
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Details
- Title
- Towards Evaluating Driver Fatigue with Robust Deep Learning Models
- Creators
- Ken AlparslanYigit Alparslan - Drexel UniversityMatthew Burlick - Drexel University
- Publication Details
- arXiv.org
- Resource Type
- Preprint
- Language
- English
- Academic Unit
- Computer Science (Computing)
- Other Identifier
- 991021868112004721