Advancing Road Safety through Driver Drowsiness Detection Using Deep Learning Model
- Title
- Advancing Road Safety through Driver Drowsiness Detection Using Deep Learning Model
- Creator
- Pandey S.; Lekha J.; Kadyan S.
- Description
- Driver drowsiness poses a significant threat to public safety, contributing to numerous road accidents and fatalities annually. Drowsy drivers exhibit characteristic changes in facial expressions and behaviors, including eye closure, head nodding, and yawning. These indicators can be detected through various techniques, including image processing, computer vision, and machine learning. This research investigates a promising approach: utilizing a ResNet-101 deep convolutional neural network (CNN) for driver drowsiness detection based on eye, head, and mouth states. The model was trained on a vast dataset of 2.2 million images, covering diverse driving conditions. Despite achieving a 69% accuracy, suggesting real-world potential, computational limitations restricted training to only a quarter of the data. This necessitates further research with larger datasets and increased resources to enhance accuracy and robustness. 2024 IEEE.
- Source
- 2024 3rd International Conference on Artificial Intelligence for Internet of Things, AIIoT 2024
- Date
- 2024-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- CNN; computer vision; deep learning; Driver drowsiness; facial expressions; head pose; image processing; mouth movement; ResNet101
- Coverage
- Pandey S., CHRIST(Deemed To Be) University Lavasa, School of Science, Maharashtra, Pune, India; Lekha J., CHRIST(Deemed To Be) University Lavasa, School of Science, Maharashtra, Pune, India; Kadyan S., CHRIST(Deemed To Be) University Lavasa, School of Science, Maharashtra, Pune, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835037212-0
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Pandey S.; Lekha J.; Kadyan S., “Advancing Road Safety through Driver Drowsiness Detection Using Deep Learning Model,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 23, 2025, https://archives.christuniversity.in/items/show/19347.