Passenger Vehicle Driver Detection Based on YOLOv5+Stacking
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The intelligent detection of driver distracted driving and mask wearing can strengthen the safety management of passenger vehicle operators, the correct detection of drivers in monitoring images by regulatory authorities is the basis for the implementation of these two tasks.Although the traditional object detection model can detect the driver and the passenger accurately, there are still many wrong classification between the driver and the passenger.In order to increase the detection rate, this paper uses CBAM attention mechanism and the idea of non-maximum suppression in YOLO v5, and proposes a combination model of YOLOv5+Stacking integrated learning, which can effectively reduce false detection between drivers and passengers and ultimately increase the detection rate of YOLO v5 model.In this paper, this model is used to verify and detect the collected data set, and its evaluation indexes are not only better than the original YOLO v5 model but also better than other similar detection models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it