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Title |
Drowsy Driver Detection System Using Deep Learning |
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Author |
Amal Al-Shahrani, Amjad Saeed Al-Ghamdi, Areej Saud Al-Gurashi, Raghad Saeed Al-Zahrani, Seham Awadh Al-Malki, and Nuha Idris Imam |
| Citation |
Vol. 25 No. 6 pp. 61-68 |
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Abstract |
The research paper provides an overview of various approaches and methodologies for detecting driver drowsiness using computer vision and machine learning techniques. The primary focus is on detecting drowsiness indicators, such as eye closure and yawning, in challenging low-light conditions. Several studies have been conducted, employing different algorithms and models, to achieve accurate drowsiness detection. In this context, our contribution to the field involves incorporating YOLOv8, YOLOv5, and VGG16 as integral components of the methodology. By leveraging these advanced technologies, we aim to enhance the accuracy and effectiveness of driver drowsiness detection systems. This, in turn, has the potential to improve road safety and prevent accidents caused by driver fatigue. |
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Keywords |
Computer vision, CNN, YOLO, VGG |
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