Optimized hybrid YOLOu‐Quasi‐ProtoPNet for insulators classification
Bibliographic record
Abstract
Abstract To ensure the electrical power supply, inspections are frequently performed in the power grid. Nowadays, several inspections are conducted considering the use of aerial images since the grids might be in places that are difficult to access. The classification of the insulators' conditions recorded in inspections through computer vision is challenging, as object identification methods can have low performance because they are typically pre‐trained for a generalized task. Here, a hybrid method called YOLOu‐Quasi‐ProtoPNet is proposed for the detection and classification of failed insulators. This model is trained from scratch, using a personalized ultra‐large version of YOLOv5 for insulator detection and the optimized Quasi‐ProtoPNet model for classification. For the optimization of the Quasi‐ProtoPNet structure, the backbones VGG‐16, VGG‐19, ResNet‐34, ResNet‐152, DenseNet‐121, and DenseNet‐161 are evaluated. The F1‐score of 0.95165 was achieved using the proposed approach (based on DenseNet‐161) which outperforms models of the same class such as the Semi‐ProtoPNet, Ps‐ProtoPNet, Gen‐ProtoPNet, NP‐ProtoPNet, and the standard ProtoPNet for the classification task.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".