Real-Time Drowsiness Detection and Classification with Deep Learning Model
Bibliographic record
Abstract
Drowsy driving is a major concern for road safety, leading to accidents and fatalities.This paper presents a novel approach called Optimized Dual-Tree Deep Learning (ODT-DL) for real-time drowsiness detection in drivers.The model uses advanced techniques like image preprocessing, feature extraction, and feature selection.It uses Hidden Markov Models for sequence modelling and classification, enabling accurate drowsiness detection.The experimental evaluation of ODT-DL on two benchmark datasets, YAWDD and NTHU-DDD, shows outstanding performance, with accuracy, precision, recall, and F1-Score consistently exceeding 99%.The model's high discrimination capabilities and low false alarm rates ensure reliable detection.Comparative analysis against other machine learning models, such as AlexNet, ResNet, Support Vector Machine, and ensemble methods, highlights the superiority of ODT-DL.The findings suggest the model's practical implications for enhancing road safety by preventing accidents caused by driver drowsiness, with potential applications in vehicle safety systems.The proposed ODT-DL model holds promise for real-world implementation and opens avenues for future developments in road safety technology.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".