CGTD-Net: Channel-Wise Global Transformer-Based Dual-Branch Network for Industrial Strip Steel Surface Defect Detection
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Bibliographic record
Abstract
Surface defects directly affect the mechanical properties of industrial strip steel products. To evaluate the integrity of the strip steel surface, a channel-wise global Transformer-based dual-branch network (CGTD-Net) for strip steel surface defect detection, dubbed CGTD-Net, is proposed in this study. First, the strip steel surface images are preprocessed using saturation adjustment and random flipping strategies to remove unnecessary background information and improve network generalization. Second, the Swin Transformer is employed at the end of the backbone network and the negative impacts of a single channel are then mitigated by using the multichannel feature pyramid networks via Transformer, which improves the extraction ability of the global semantic information for tiny or narrow defects. Third, an edge detection branch network is constructed with a spatial–channel global attention (SCGA) module to further enhance the feature extraction on both spatial and channel information. Finally, the CGTD-Net is compared with 11 state-of-the-art methods on the NEU-DET dataset, and ablation experiments are also implemented. The comparison results, conducted on a single 3090Ti GPU, reveal that the CGTD-Net achieves a mean intersection over union (mIoU) of 75.16% at 178 frames/s, outperforming other methods. The ablation experiment demonstrates that the CGTD-Net improves the mIoU by 7.83% and the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${F}$ </tex-math></inline-formula> -score by 6.3% compared to the baseline.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it