High-Fidelity Reconstruction and Metrology Model for Defects Based on Physical Priors and Adaptive Morphology

Tool micro-defect quantification is often degraded by optical sampling limits, boundary aliasing, and non-Gaussian industrial noise. To improve segmentation fidelity and physical measurement reliability, this study proposes a physically guided reconstruction and metrology framework for milling tool defects. The framework first uses an improved ResNet18-BiFPN segmentation network supervised by a distance transform-based boundary-aware loss, which encourages mask boundaries to fit high-curvature crack and chipping regions. A tensor-guided elliptical adaptive structuring element is then introduced to restore local topology while preserving tangential connectivity and normal boundary fidelity. Finally, a stable edge region search and curvature-adaptive gray/Zernike moment fusion strategy are used for noise-robust sub-pixel localization and physical area quantification. Image-based evaluation and controlled simulation-based stress tests show that the proposed method achieves an mIoU of 92.5%, an F1-score of 97.1%, and an HD95 of 2.15 pixels. Under strong synthetic noise, the average distance error remains below 0.28 pixels. For micro-defect area measurement, the mean relative error is reduced to approximately 1.7%. These results indicate that the proposed framework can support more reliable defect metrology for tool condition monitoring under complex industrial imaging conditions.