Driver Drowsiness Detection and Alert System Development Using Object Detection
Bibliographic record
Abstract
Fatigue driving is an invisible killer in car accidents and one of the main causes of traffic accidents. In order to reduce traffic accidents caused by driving fatigue, this research has developed a safety assist device to prevent such traffic accidents. In this research, a non-contact driver drowsiness detection and alert system is established in the vehicle cabin. The real-time facial image of the driver is obtained through the camera installed in front of the driver, and then the image is input to the NVIDIA Jetson TX2 embedded module. YOLO (You Only Look Once) object detection algorithm is used to detect the opening and closing of the driver's eyes, and by processing the eye area, to determine whether the driver is currently awake or fatigued while driving. The driver drowsiness detection and alert system established in this research can be applied to the vehicle interior environment to monitor the driving status. When the driver is fatigued, the system will simultaneously emit sound and light signals to promptly warn such dangerous driving behaviors. It can prevent the driver from continuing to drive when fatigued, and ensure driving 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".