Performance Evaluation of Image Super-Resolution for Cavity Detection in Irradiated Materials
Bibliographic record
Abstract
Radiation-induced swelling in structural materials is a significant challenge for the safe operation of nuclear power plants. One of the promising solutions to accelerate the understanding of swelling is the development of machine learning tools for accelerated characterization of irradiated microstructures, focusing on cavities which contribute to a materials swelling response. In this paper, we examine an object detection model for cavities, YOLOv8, for cavity detection using the datasets from the Canadian Nuclear Laboratory (CNL) and Nuclear Oriented Materials & Examination (NOME). Specifically, we explored the use of Image Super-Resolution (ISR) techniques to enhance the detection performance either in the underfocused or overfocused condition for improved small cavity detection. The overall F1-score increase of 6.8% via ISR, highlights the effectiveness of ISR techniques in enhancing the model’s performance in detecting irradiation-induced cavities across all modalities. Furthermore, this study presents a comparative evaluation of YOLOv8 and Faster R-CNN, discussing their detection trade-offs and suitability for different imaging conditions. In addition to evaluating overall detection accuracy, this work assesses the impact of ISR on both models’ precision and recall for cavity detection.
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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".