360° full-field vibration measurement of cylindrical shells based on 3D-DIC: FOV-constrained optimization and non-overlapping mode shape stitching

Phase-Consistency-Adaptive Multi-Path Total Focusing Ultrasonic Imaging for Delamination Quantification in L-Shaped CFRP Corner Parts

The delay-and-sum total focusing method (TFM) for ultrasonic full matrix capture (FMC) depends on accurate ray path and travel time computation. In L-shaped carbon fiber-reinforced polymer (CFRP) corner parts, elastic anisotropy, multilayer stacking, and curvature-induced ray path non-uniqueness generate strong stripe-like coherent clutter (deterministic structural echoes), degrading focusing and sizing. To address this, we search multiple physically plausible candidate ray paths and propose a phase-consistency-adaptive multi-path fusion TFM (PCA-MPF-TFM) that performs pixel-wise path selection and fusion. The method is validated using pulse-echo FMC data acquired with a water-immersion linear array from a 6.4 mm-thick L-shaped CFRP specimen containing three 3 mm-diameter polytetrafluoroethylene (PTFE) inserts; the two within the concave-side inspection region were quantitatively evaluated. Compared with conventional isotropic TFM, an edge-adjacent delamination previously masked by structural noise is consistently detected with a 9.2 dB signal-to-noise ratio (SNR) and a 0.2 mm length error. For the second delamination, the SNR improves by 25 dB and the length error decreases from 0.6 mm to 0.2 mm. Experimental results demonstrate improved defect detectability and noise robustness under curved, anisotropic, and multilayer propagation while maintaining sub-millimeter sizing accuracy.

Single-Shot and Rotational Stitching Methods for Long-Length FPD Imaging.

Research on binocular 3D measurement method for notch damage in aero-engine blades based on geometry-aware keypoint localization

Accurate quantification of notch damage from in-service aero-engine blade borescope images is vital for maintenance decision-making. Traditional manual point-selection and heatmap-regression-based keypoint localization methods often fail to deliver stable industrial-level measurement accuracy under complex imaging conditions including strong reflections, low contrast and cluttered backgrounds. To tackle this problem, this paper proposes a full-process intelligent measurement framework for complex industrial scenarios to realize non-contact three-dimensional (3D) measurement of blade notch length and depth. First, a lightweight scale-guided reflection analysis detection network is constructed to detect damaged region of interests in binocular images and suppress metal highlight interference. Second, a geometry-aware SimCC network with decoupled classification heads and geometric prior loss is presented for pixel-level notch keypoint localization. Finally, semantic matching and epipolar constraints are combined to build robust binocular correspondences. 3D keypoint coordinates are recovered via triangulation, and notch physical dimensions are computed using spatial geometric definitions. Experiments show average absolute errors of 0.685 mm (length) and 0.873 mm (depth), with an average keypoint localization error of 2.86 pixels. Maintaining a lightweight architecture and low computational cost, the proposed method achieves sub-millimeter-to-millimeter-level measurement precision, satisfying industrial borescope preliminary screening tolerances and offering a feasible solution for quantitative assessment of aero-engine blade notch damage.

Markerless 3D pose estimation system for highly reflective aquatic environments using multi-view polarization imaging and dual-physics constrained network (DP-CRN)

The three-dimensional pose measurement of water sports holds significant value for competitive training and sports injury prevention. However, the coupling interference induced by water surface specular reflection and human wet body surface highlights substantially degrades the accuracy of conventional unlabeled methods relying solely on RGB imagery. This paper presents an markerless 3D pose estimation system that integrates multi-view polarization imaging with a Dual-Physics Constrained Network (DP-CRN). The system constructs a multi-view synchronous acquisition platform comprising six DoFP polarization cameras and establishes a coupled water-body reflection imaging model. A polarization decoupling algorithm for dynamic water surfaces is designed based on the Fresnel-Mueller matrix, enabling adaptive highlight suppression through frame-by-frame tracking of the time-varying water surface normal direction. The DP-CRN incorporates dual constraints from optics and biomechanics is constructed, integrating the polarization Fresnel equation, bone length conservation, and joint kinematic range constraints into the loss function. Additionally, a cross-angle polarization Stokes consistency loss is introduced to drive end-to-end 3D pose optimization at the physical level. On an experimental dataset encompassing four categories of water sports and approximately 450,000 frames, the proposed method achieves an average per-joint position error of 64.7 mm and a PCK@100 accuracy of 75.8%, representing an improvement of approximately 14%-17% over existing state-of-the-art approaches, while inter-frame jitter is reduced by 32%. Real-time inference at 108 fps is achieved on a single GPU. Ablation experiments demonstrate that water surface polarization decoupling constitutes the primary contributor to system accuracy, and the dual constraints of optics and biomechanics yield complementary gains in spatial accuracy and temporal stability. Future work will extend the proposed framework to fully uncontrolled open-water environments, more diverse camera configurations, and broader athlete populations, while incorporating underwater refraction correction and time-series diffusion models to improve robustness under full submersion and severe splash occlusion. Ablation experiments demonstrate that water surface polarization decoupling constitutes the primary contributor to system accuracy, and the dual constraints of optics and biomechanics yield complementary gains in spatial accuracy and temporal stability.

MCCR: Multi-scale cross-modal cascade registration method for water surface scenes

Error-compensated multimodal 3D measurement with a high-speed polygon mirror

A multimodal 3D measurement framework based on a high-speed polygon mirror is proposed for integrating area structured-light projection and line laser triangulation scanning within a shared optical architecture. For off-axis polygon-mirror scanning, an optical projection deviation model is established, in which the overall system deviation is explicitly decomposed into Spatial Offset Error (SE) and Intrinsic Nonlinear Error (NE). Based on this model, an angular-domain intensity compensation method is developed for the structured-light mode, while an adaptive variable frequency triggering strategy and a corrected dynamic geometric model are introduced for the line-scanning mode. Full link numerical simulations and ablation studies show that the proposed framework effectively reduces phase distortion, improves sampling uniformity, and suppresses geometric error under the tested conditions. The results further indicate that joint compensation of SE and NE yields improved reconstruction performance in both measurement modes. The proposed method provides a compact and extensible basis for high-speed multimodal 3D measurement using a polygon mirror.