An improved YOLOv11 framework for robust helmet detection with focus on small-scale target detection
Bibliographic record
Abstract
Wearing helmets is essential for safety in construction and transport, as the absence of head protection is closely associated with severe injury risks. Due to the limitations of manual supervision, automated helmet detection has become increasingly necessary. However, helmets often appear as small objects in surveillance imagery, making detection difficult due to limited pixel representation, weak semantic cues, and the loss of fine-grained features during early-stage processing. To address these challenges, this work proposes a real-time detection framework centered around the Multi-Scale Pooling with Coordinate Attention (MSPCA) module. MSPCA integrates multi-scale semantic enhancement and directional spatial attention into a unified structure. It employs a staged attention mechanism—consisting of soft attention to guide early feature focus and hard attention to suppress background noise—while preserving low-level details via residual connections. This design improves the network’s ability to localize and classify small targets. Experiments on the SHWD dataset demonstrate that the proposed framework significantly outperforms the YOLOv11n baseline, achieving higher recall, mAP@0.5, and mAP@0.5:0.95. These results confirm the model’s effectiveness in detecting small helmet instances.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".