Driver Drowsiness Detection Based on Joint Human Face and Facial Landmark Localization With Cheap Operations
Bibliographic record
Abstract
Real-time detection of driver drowsiness is critical to reduce the risk of road accidents and fatalities. Current facial landmark-based methods usually use a two-stage paradigm, where faces and facial landmarks are localized separately. Additionally, most methods can be hindered by challenging conditions, such as night driving or eyes closed. To address these challenges, we present a refined YOLO network named YOLOFaceMark that can simultaneously detect faces and their facial landmarks. Furthermore, we introduce a drowsiness detection model based on facial landmarks. This model utilizes extracted eye and mouth information to identify drowsy states. We optimize the original YOLO components through structural re-parameterization, channel shuffling, and the design of a dual-branch detection head with an implicit module. These enhancements are designed to improve the accuracy while maintaining computational efficiency. We validate the real-time performance and accuracy of YOLOFaceMark on public datasets, including 300W and COFW. Additionally, we conduct further validation to demonstrate our ability to achieve effective and robust drowsiness detection solely based on the facial landmarks detected by YOLOFaceMark.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".