A Study on Target Detection and Its Application and Development in the Identification of Unsafe Behaviour 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
The construction industry serves as the cornerstone of economic development, but the construction industry is also a very dangerous industry with workplace accidents occurring every year, so automatic identification and recognition of potentially unsafe behaviours and conditions is of great significance in safeguarding the safety of incoming recognised lives. In this paper, firstly, the three major classes of algorithms for target detection are elaborated in detail, the traditional target detection mainly relies on the method of machine learning, the two-phase target detection algorithm based on deep learning is mainly divided into two phases of candidate region production and target detection, while the one-phase target detection algorithm based on deep learning carries out end-to-end target detection without the need to produce a candidate region, and gives an assessment of the performance of the target detection indicators to evaluate the strengths and weaknesses, and summarises and analyses the current applications of target detection in the construction field and the new trends in the future.
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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 | 0.002 | 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