SFC-YOLOv8: Enhanced Strip Steel Surface Defect Detection Using Spatial-Frequency Domain-Optimized YOLOv8
Bibliographic record
Abstract
Steel strips, renowned for their exceptional strength, durability, and impact resistance, are ubiquitous in various manufacturing sectors, notably aerospace, shipbuilding, and automotive industries. However, surface defects on these strips are inevitable due to various factors, including processing and environmental conditions. As a result, the efficient detection of these defects is paramount. This study introduces SFC-YOLOv8, a novel method for detecting surface defects on steel strips that leverages an improved YOLOv8 framework in the spatial-frequency domain. Initially, by exploiting the distinct high-frequency features of defect images, we extract mixed spatial-frequency domain features before applying YOLOv8, enhancing its sensitivity to low-contrast defects. Furthermore, we incorporate a global-local information-enhanced attention module into YOLOv8’s neck, which integrates high-frequency, low-frequency, and local perceptual information to capture defect features more effectively, boosting the model’s capability to detect minute and subtle defects. Additionally, we propose a frequency domain feature adaptive module that adaptively adjusts the soft threshold based on image frequency domain information, filtering out background noise while preserving the underlying semantic information of the image, thereby enhancing defect detection precision under varying lighting conditions. Comparative evaluations with conventional detection methods reveal that SFC-YOLOv8 achieves a mean average precision of 85.6%, a detection speed of 203 frames per second, and a compact model parameter of 4.36 MB, showcasing its superior overall performance. Ablation studies further confirm that SFC-YOLOv8 outperforms traditional YOLOv8 by enhancing detection precision by 6.6%.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".