A Safety Helmet Detection Method Using Adjusted YOLOv8
Why this work is in the frame
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Bibliographic record
Abstract
Safety production is of paramount importance in protecting the safety, health of workers and assets. Safety helmets play a crucial role across various industries, directly impacting the wearer's life safety. In response to the prevalent issue of many workers not wearing safety helmets, coupled with high cost and risks associated with manual safety helmet detection, current automated methods are difficult to detect safety helmet usage at a large scale, complex on-site environments. This paper proposes a safety helmet detection method based on adjusting YOLOv8. Adjustments to the backbone network of YOLOv8 were replaced by DenseNet121 and appropriate data augmentation methods were designed. This method achieved an accuracy of 96.81% in the Safety Helmet Wearing Dataset. Compared to the original YOLO v8 algorithm, it achieved a 0.74% performance improvement. Our method enhances the accuracy of safety helmet detection, provided important technical support to ensure production safety.
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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 | 0.001 | 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) | 0.000 | 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 it