Hardhat-Wearing Detection for Enhancing On-Site Safety of Construction Workers
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
Construction is one of the most dangerous job sectors, which annually reports tens of thousands of time-loss injuries and deaths. These injuries and deaths do not only bring suffering to the workers and their families, but also incur delays and costs to the projects. Therefore, safety is an important issue that a general contractor must monitor and control. One of the fundamental safety regulations is wearing a hardhat, which should not be violated anytime on the sites. In this paper, a novel vision-based method is proposed to automate the monitoring of whether people are wearing hardhats on the construction sites. Under the method, human bodies and hardhats are first detected in the video frames captured by on-site construction cameras. Then, the matching between the detected human bodies and hardhats is performed using their geometric and spatial relationship. This way, the people who are not wearing hardhats could be automatically identified and safety alerts could be issued correspondingly. The method has been tested with real site videos. The high safety alert precision and recall of the method demonstrate its potential to facilitate the site safety monitoring work.
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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