PDFormer: Efficient Vision Transformer for Photovoltaic Defect Detection
Bibliographic record
Abstract
In industrial production, the quality of photovoltaic determines the power generation efficiency and service life. Therefore, only by improving the quality inspection automation capability of photovoltaic products can we ensure the quality of mass production. Recently, Vision Transformers (ViTs) have shown excellent performance in various visual tasks. However, the ViTs generally suffer severe performance degradation when small-scale datasets are used for training since ViTs overfit quickly. To alleviate this, we propose PDFormer, a simple but effective Transformer framework towards efficient learning for photovoltaic defects detection. Our proposed PDFormer boosts the performance of the ViTs by compound improvements, which generally consists in three levels: data level, structure level, and supervision level. Specifically, an image mixing augmentation method called QuadMix augmentation is first proposed to randomly mix the positive and negative samples for the binary classification task. Besides, we develop a novel attention-based module to reweight the deep features by intermediate classification scores. Finally, we adopt both the ViT and CNN networks as the compound teacher networks to perform compositional multi-teacher knowledge distillation for the transformer student. Benefitted from the overall efficient designs, PDFormer significantly improves the detection performance of the transformer baseline on the dataset. Experimental results demonstrate that PDFormer achieves a top-1 accuracy of 98.08%, surpassing other competitive methods on the photovoltaic dataset.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".