Towards Reliable Detection of Dielectric Hotspots in Thermal Images of the Underground Distribution Network
Bibliographic record
Abstract
This paper introduces a thermographic vision system to detect different types of hotspots on a variety of cable junctions commonly found in Hydro-Québec underground electrical distribution network. Cable junctions of underground distribution networks operate in harsh conditions, potentially leading to failure overtime. Faults can be prevented by the timely detection of local hotspot on these junctions. Hotspot detection is carried out by mean of image segmentation using a deep neural network. Special care is given to uncertainty estimation and validation. Uncertainty is used to assess the quality of a segmentation to avoid misdiagnosis or returning in the field to recapture images. It is also proposed as a tool to evaluate whether unannotated images should be included in the dataset. System performance has been evaluated on a test dataset as well as in the field by regular inspection teams. Promising results obtained so far led to the deployment of the vision system on a fleet of five inspection trucks performing inspection over the province over the last year Authorization was granted to scale the solution to 35 trucks starting this year.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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 | 0.001 |
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".