AI-Driven Automated Helmet Detection in Underground Coal Mines using Attention-Enhanced Vision Transformer
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
Ensuring safety compliance in underground coal mines is essential for preventing accidents and safeguarding miners. Traditional methods for monitoring helmet usage are often ineffective due to poor visibility, dust, and equipment occlusion. This study proposes an attention-enhanced Vision Transformer (ViT) model, specifically adapted for helmet detection in challenging underground environments. The model processes images as sequences of patches, leveraging multi-head self-attention mechanisms to capture global dependencies and improve feature extraction. A custom dataset was developed from underground coal mine footage, and the model was trained using supervised learning with a cross-entropy loss function. The customized ViT achieved an accuracy of 98%, outperforming other State-Of-The-Art (SOTA) models, such as YOLOv8 with attention mechanisms, Mask R-CNN, and Detectron2. The results demonstrate the effectiveness of the attention-enhanced ViT in accurately detecting helmets, even in low-light and cluttered environments. This research contributes to developing real-time, automated safety monitoring systems, which reduce human error and enhance worker safety in hazardous mining operations.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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