AN INTELLIGENT FUSION OBJECT-DETECTION ALGORITHM FOR SMART SUBSTATION SYSTEM, 1-7.
Bibliographic record
Abstract
Machine learning is playing an increasingly important role in smart substation systems.Object detection algorithms are commonly used in smart substations for procedures, such as helmet detection and personnel clothing inspection.However, object detection algorithms are inadequate for solving complex smart substation scenarios because of their poor generalisation ability.Thus, we introduce an intelligent fusion algorithm named YYSF-4 that has good generalisation ability.YYSF-4 comprises You Only Look Once (YOLO) V1, YOLO V3, a single-shot multi-box detector, and fastoriented text spotting, and is suitable for use in smart substations.We use real images from substations as a dataset to verify the effectiveness of the YYSF-4 in four scenarios: helmet detection and recognition, personnel clothing detection and identification, personnel detection and identification, and bill detection and recognition.The experimental results show that the mean average precision (mAP) of YYSF-4 in the above four scenarios is higher than the mAPs of other baseline algorithms.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".