Distracted Driving Behavior and Driver’s Emotion Detection Based on Improved YOLOv8 With Attention Mechanism
Bibliographic record
Abstract
An improved YOLOv8 detection method is proposed for detecting distracted driving behavior and driver’s emotion. Unlike the commonly used YOLOv8 method, an attention mechanism named MHSA and a CNN module are synthesized to ensure improved performance in terms of accuracy and convergence, where MHSA is used to detect distracted driving behavior and CNN is used to detect driver’s emotion. The FER2013 dataset and collected dataset are used to train the improved YOLOv8. The training results show that the proposed YOLOv8 demonstrates improved performance compared with the commonly used YOLO based methods. Finally, the validity of the proposed YOLOv8 method is illustrated through implementations in Jetson Nano platform, where the TensorRT and DeepStream methods in the Jetson Nano device are used to optimize the volume and operational speed of the proposed YOLOv8 method, respectively. Test results show that the proposed YOLOv8 method can yield better real-time and accuracy properties.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".