Driver Drowsiness Detection by Employing CNN and Dlib
Bibliographic record
Abstract
Every year thousands of people lose their life due to road accidents. One of the main reasons for these accidents is driver drowsiness. In driver drowsiness, the driver slept while driving, which causes the road accident, especially on the long routes. Driver fatigue and micro sleep while driving caused the fatal accident and death of human beings. To overcome this problem, we are implementing a technique in which we capture the image of the driver. After capturing the image of the driver, we process driver images to detect driver drowsiness. For the processing of the driver image, we are using two different techniques with each other. In the first technique, we are using the Dlib for image drowsiness detection by detecting that driver’s eyes are closed and the driver is yawning. In the second technique, we used CNN for the detection of yawning and the eyes of the driver are closed or not and predict driver drowsiness. After implementing the two techniques we combine the output of both techniques. After combining both techniques we test the system, and it gives us very good results.
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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 | 0.000 |
| 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 | 0.000 |
| 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".