Ground Penetrating Radar Image Analysis for Underground Barrier Detection by Combining YOLOv12 with Channel-wise Attention and Denoising Auto-Encoder
Bibliographic record
Abstract
Accurate detection of underground barriers such as pipelines is crucial for urban safety and infrastructure management. Ground Penetrating Radar (GPR) image provides a non-destructive means for subsurface exploration, but its B-scan images often contain strong noise and clutter that hinder reliable recognition. To address these challenges, we propose a YOLOv12-based detection framework enhanced with a denoising autoencoder (AE) and channel-wise attention (CBAM). The AE suppresses noise while preserving hyperbolic signatures, and CBAM adaptively highlights informative features, which improves robustness under complex soil conditions. Experiments on real GPR datasets of gas pipelines show that our method achieves higher precision, recall, and mAP@50 than baseline YOLOv12. Efficiency analysis further reveals that the CBAM-enhanced variant offers the best accuracy-training trade-off, while the combined AE+CBAM model provides the most balanced performance. These results demonstrate the effectiveness of integrating denoising and attention mechanisms into modern detectors for robust underground barrier detection.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".