Drowsiness Detection Through Yawning and Eye Blinking Models Using Convolutional Neural Networks and Transfer Learning
Bibliographic record
Abstract
Driver drowsiness is one of the main causes of road traffic accidents because it reduces cognitive capabilities and reflexes. Therefore, different artificial intelligence-based methods have been developed for monitoring the driver's state in real time. However, these methods still need improvement because identifying signs of fatigue, such as yawning and eye blinking, remains a significant challenge. The main objective of this paper is to create two models using pretrained Convolutional Neural Networks (CNNs) to detect yawning and eye blinking, which should help prevent road traffic accidents. Different datasets were used, including YawDD and MRL Eye, to train various pretrained models based on well-known architectures such as MobileNet, Xception, Inception-V3, and VGG-16, using transfer learning. These generated models demonstrated high efficiency, with MobileNet achieving the best overall performance with an accuracy ranging from 97.64% to 99.52%, depending on the dataset used for training and validation. The results indicate that pretrained CNN models fine-tuned with transfer learning are highly effective in detecting signs of drowsiness, which is promising for real-world applications in Advanced Driver Assistance Systems (ADAS) to ensure the driver's safety.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".