Hardhat-Wearing Detection for Enhancing On-Site Safety of Construction Workers
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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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".