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arXiv Computer Vision

Integrated Laser Scanning and Image-Based Topology Optimization Techniques for Detection and Quantification of Visible and Subsurface Structural Defects

The paper introduces two non‑contact, vision‑based methods for detecting and quantifying structural defects in steel components. The first method uses high‑resolution laser scanning to create 3D point clouds, comparing measured and reference data to locate surface damage, measure geometric loss, and map defect geometry into finite element models. The second method combines full‑field surface deformation data from 3D digital image correlation with finite element model updating and topology optimization to infer subsurface defects from their effect on structural response. Experimental validation on steel‑beam specimens with controlled defects shows both approaches can accurately identify and quantify defect geometry, offering complementary insights for visible and subsurface damage assessment.

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Accurate Reconstruction of Gas Turbine Blade Geometry Using 3D/2D Rigid Registration and CT View Optimization

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Robust Online Aero-Engine Blade Defect Detection via Dual-Alignment Test-Time Adaptation

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Lowering the Barrier to AI-Driven Inspection: A No-Code Workflow for Automated Structural Defect Detection

The paper introduces YOLOEZ, a no-code, GUI-based tool that streamlines the entire YOLO model workflow—data labeling, training, and inference—for automated structural defect detection. It demonstrates that YOLOEZ outperforms traditional image‑processing methods across most detection metrics while simplifying deployment for users without programming expertise. The tool aims to lower technical barriers in structural health monitoring, enabling broader adoption of AI-driven inspection for predictive maintenance and intelligent structural systems.

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