Critical Electrical Infrastructure Segmentation in Arctic Conditions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Most critical electrical infrastructure monitoring research takes place in temperate climates; however, significant infrastructure is deployed in remote and harsh environments, such as within arctic climates. Automatic segmentation of power poles from vehicle-based imaging using deep learning methods promises to reduce manual effort and enable more frequent inspections. Previous studies have demonstrated the successful application of deep learning techniques for pole segmentation using UAV and ground-based imagery. The HRNet-OCR model, originally trained for scene segmentation of ground-based imagery for multiple objects, including poles, has demonstrated the adaptability to learn semantic segmentation of poles in Google Street View (GSV) images captured during temperate weather conditions. This research study aims to evaluate the utility of the HRNet-OCR model architecture for semantic segmentation of utility power poles from ground-based images captured in arctic weather conditions, specifically in the Iqaluit region of Nunavut, Canada. Our work demonstrates promising performance of the HRNet-OCR model for power pole segmentation, and further finetuning suggests the model's ability to learn to segment other electrical equipment, such as pole-mounted transformers and crossarms. The proposed approach presents a promising solution for automating critical infrastructure maintenance in remote and harsh environments.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it