Bibliographic record
Abstract
A significant challenge for Photovoltaic (PV) power systems is the accumulation of dust on solar panels, particularly prevalent in desert areas.Dust accumulation on solar panels cause a high degradation in the output power and thus, solar panels should be monitored and cleaned continuously to keep their efficiency high.Automating the inspection of solar panels can serve as a viable alternative of human inspection due to the impact of labor expenses and human difficulties on decision-making on such an environment.In this work, we are proposing a computer vision approach that is capable of inspecting solar panels and determines its condition in terms of dust accumulation.The proposed approach aims to prove the capability of dust detection on distinct panels by means of visible light imaging and computer vision techniques.It deploys both gray level co-occurrence matrix (GLCM) textural features and local binary patterns (LBP) of solar panels' images in addition to support vector machine (SVM) to build a classification model for this purpose.The proposed approach has been tested on images of solar panels that suffer from moderate and heavy accumulation of desert sands and dusts.The experimental findings successfully illustrated the effectiveness of the proposed feature description and the overall dust detection approach of solar panels with an accuracy of 94.3%.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".