A Deep Learning-Based System for Driver Fatigue Detection
Why this work is in the frame
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Bibliographic record
Abstract
Driver fatigue is still a principal cause of traffic accidents.While many ways allowing fatigue detection, a diversity of obstacles such as head position, luminosity, and facial expressions make it a very challenging problem.In this paper, we propose a hybrid approach using deep learning techniques to detect driver drowsiness by combining between structural and global classification methods.The structural method tracks eyes, eyebrows, and mouth movements to assess blink and yawning, for this purpose we calculate eye-opening and mouth-opening ratios relative to their width.Five parameters are extracted LEM (left eye movement), REM (right eye movement), LEB M (left eyebrow movement), REBM (right eyebrow movement), and MM (mouth movement), whereas the global method is based on Convolutional Neural Network (CNN) to describe the whole face.Eight-layer pre-trained Alexnet network is used to extract features and make classification of each frame.To do video classification, the five structural parameters, along with the global classification decision, are combined into a single vector to be input into Long-Short-Term Memory (LSTM) networks that is an improved version of Recurrent Networks.LSTM decision score is determined after running 150 steps, providing information about driver state Extensive Experiments are performed on a Driver Drowsiness Detection Dataset that contains subjects of different ethnicities.The experimental results show that the proposed method with the combined features improves drowsiness detection significantly as well as outperforms the state-of-the-art models in terms of drowsiness scores.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it