Deep Learning Based Solar Panel Structural and Electrical Damage Detection
Bibliographic record
Abstract
Guaranteeing the quality of solar panels is crucial, with numerous inspection techniques and technologies suggested. Traditional techniques for visually inspecting solar panels have several limitations and often fail to ensure perfect results. Automated inspection of solar panels utilizing deep learning has become a powerful and efficient method for tackling quality control problems in the industrial sector. Thus, this study concentrates on identifying defects in solar panels through a tailored and lightweight model built on MobileNetv2 for classification and feature extraction, along with the application of the Grad-CAM algorithm to pinpoint the damaged region in the panel. This research primarily targets improving the performance of real-time solar panel defect detection by utilizing a dataset with six different categories of solar panel defects. Additionally, we conducted a comparative analysis between our proposed model and the other models discussed in the literature review section. Our classifier attained an accuracy of 96 %, and Grad-CAM demonstrated effective results for locating defects.
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How this classification was reachedexpand
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 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".