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Nondestructive Testing Method for Local Concealment Features in Aviation Glass Based on Multi-Dimensional Feature Fusion
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DOI：10.1007/s10921-026-01414-x.png)
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Abstract
En 中文
A feature extraction method combines anisotropic guided filtering with multimodal large-component adaptive segmentation. It addresses the local concealment features of aviation glass and enables accurate detection of small-target defects. The local concealment features of aviation glass are analyzed. Canny edge detection provides the edge foundation for the guided filtering. Local window size, regularization parameter, and anisotropic factor are dynamically adjusted to remove noise while preserving details. The adaptive threshold is dynamically determined according to the local gray-level characteristics of the image for binarization. An area threshold is set to filter and fuse potential target features, achieving accurate segmentation of local concealment feature images. The filtered image achieves a peak signal-to-noise ratio of 37.61, a structural similarity index of 0.91, and a feature extraction accuracy of 0.9962. The method effectively overcomes the interference of local concealment features in the precise segmentation and extraction of aviation glass feature images.
Keywords:
Aviation glass
Local concealment feature
Anisotropic guided filtering
Adaptive segmentation
Feature extraction
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