Smart Construction Safety and Alert System with Enhanced AI-Based Optimization
Bibliographic record
Abstract
Construction sites are inherently dangerous environments, often resulting in injuries and losses due to lapses in Personal Protective Equipment (PPE) compliance. This paper presents an AI-driven smart safety system designed to automate real-time monitoring of construction workers using computer vision. A comparative evaluation of multiple object detection models—including YOLOv7, YOLOv8, YOLOv9, YOLOv11 variants, and Faster R-CNN—was conducted to identify the best solution. YOLOv11s was selected for its lightweight architecture, excellent precision$(0.908)$, high$\text{mAP}(0.847)$, and efficient inference speed, making it highly suitable for real-time applications. The model was trained on a merged dataset of over 4,000 images across ten PPE categories, using mosaic augmentation and hyperparameter tuning to improve performance and reduce overfitting. The system incorporates a smart alert mechanism that automatically sends email notifications when PPE violations continue for more than 10 seconds, enabling timely intervention. A WebSocket-enabled backend ensures low-latency video streaming and seamless edge device deployment.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".