PixTransNet: A Sensor-Aware CNN–Transformer Model for Magnetic Flux Leakage Defect Segmentation
Bibliographic record
Abstract
Magnetic Flux Leakage (MFL) is a widely used non-destructive evaluation (NDE) technique for pipeline inspection. However, its signals are highly sensitive to noise and geometric distortions, causing small defects with limited spatial coverage and subtle defects with low-contrast patterns to be embedded in noise, resulting in indistinct boundaries and irregular shapes that complicate segmentation. To address these challenges, we propose PixTransNet, a hybrid CNN–Transformer model built on a UNet encoder–decoder architecture with a ResNet18 backbone, designed to improve the segmentation and boundary localization of small and subtle defects in MFL signals. We embed pixel-aware transformer blocks into the deeper encoder stages to capture long-range dependencies and enhance the modeling of subtle and fragmented defect patterns. To further enhance the interpretation of MFL signals, we introduce a cross-attention module that selectively emphasizes signal regions with strong structural relevance, leading to more continuous and accurate defect boundaries, particularly for small defects. Extensive experiments on a large-scale dataset of 33,000 MFL images demonstrate that PixTransNet achieves notable improvements in segmentation quality, particularly in detecting small, weak, and low-contrast defects compared to existing baselines. PixTransNet achieves 48.30% IoU, representing a 1.97% improvement, and 70.73% Recall, representing a 13.06% improvement over the best-performing baseline
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".