Training-Free Tunnel Defect Inspection and Engineering Interpretation via Visual Recalibration and Entity Reconstruction

arXiv cs.CV / 5/1/2026

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Key Points

The paper introduces TunnelMIND, a training-free framework for tunnel defect inspection that targets defect localization, measurement, severity grading, and engineering documentation needs.
Instead of using language-guided proposals as final outputs, TunnelMIND recalibrates their spatial support at inference time using dense visual consistency to handle tunnel-specific hard negatives.
The method reconstructs segmentation masks into structured defect entities, including category, location, geometry, severity, and contextual attributes suitable for downstream use.
TunnelMIND generates retrieval-grounded explanations and engineering-readable reports constrained by expert knowledge, improving usability beyond coarse open-vocabulary proposals.
Reported F1 scores are 0.68 (visible), 0.78 (GPR), and 0.72 (road defects), indicating effective performance across multiple defect inspection settings.

Abstract

Tunnel inspection requires outputs that can support defect localization, measurement, severity grading, and engineering documentation. Existing training-free foundation-model pipelines usually stop at coarse open-vocabulary proposals, which are difficult to use directly in interference-heavy tunnel scenes. We propose a training-free framework TunnelMIND. Specifically, language-guided defect proposals are not treated as final outputs; instead, their spatial support is recalibrated at inference time through dense visual consistency, so that coarse semantic anchors can be transformed into more reliable prompts under tunnel-specific hard negatives. The resulting masks are further reconstructed into structured defect entities with category, location, geometry, severity, and context attributes, which are then mapped to retrieval-grounded explanation and engineering-readable report generation under expert knowledge constraints. On visible, GPR, and road defect tasks, TunnelMIND achieves F1 scores of 0.68, 0.78, and 0.72, respectively. Overall, TunnelMIND shows that training-free tunnel inspection can move beyond coarse localization toward structured defect evidence for engineering assessment.