Enhanced insulator fault detection using optimized ensemble of deep learning models based on weighted boxes fusion
Bibliographic record
Abstract
Fault identification in transmission line insulators is essential to keep the power system running. Using deep learning-based models combined with interpretative techniques can be an alternative to improve power grid inspections and increase their reliability. Based on that consideration, this paper proposes an optimized ensemble of deep learning models (OEDL) based on weighted boxes fusion (WBF), called OEDL-WBF, to enhance the fault detection of power grid insulators. The proposed model is hypertuned considering a tree-structured Parzen estimator (TPE), and interpretative results are provided using the eigenvector-based class activation map (Eigen-CAM) algorithm. The Eigen-CAM had better results than Grad-CAM, Activation-CAM, MaxActivation-CAM, and WeightedActivation-CAM. The multi-criteria optimization of the structure by TPE ensures that the appropriate hyperparameters of the you only look once (YOLO) model are used for object detection. With a mean average precision (mAP)@[0.5] of 0.9841 and mAP@[0.5:0.95] of 0.9722 the proposed OEDL-WBF outperforms other deep learning-based structures, such as YOLOv8, YOLOv9, YOLOv10, YOLOv11, and YOLOv12 in a benchmarking. The Eigen-CAM further helps to interpret the outcomes of the model.
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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 | 0.001 | 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) | 0.001 | 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 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".