Method for classifying linear polarization defects in transparent materials using stress birefringence

By acquiring and preprocessing polarization images, using optical phase conjugation technology to cancel noise, calculating the delay distribution map and performing topological data analysis, a dynamic knowledge medium-guided classification model is constructed. This solves the problems of noise interference and insufficient feature representation in the online polarization detection of transparent materials, and achieves high-precision defect classification.

CN121499397BActive Publication Date: 2026-06-09NAT INST OF MEASUREMENT & TESTING TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
NAT INST OF MEASUREMENT & TESTING TECH
Filing Date
2025-11-14
Publication Date
2026-06-09

AI Technical Summary

Technical Problem

In online polarization detection of transparent materials, measurement noise introduced by mechanical vibration and environmental disturbance affects the quality of polarization images. Existing methods fail to fully exploit the spatial topological information of stress distribution, resulting in low defect classification accuracy.

Method used

The original polarization images are acquired and preprocessed. Optical phase conjugation technology is used to cancel vibration noise, the delay distribution map is calculated and topological data analysis is performed. Macroscopic topological feature vectors are extracted, and a dynamic knowledge medium is constructed to guide the classification model decision-making, outputting the defect classification results.

Benefits of technology

It effectively cancels out vibration noise, improves the accuracy of defect classification, solves the problems of noise interference and insufficient feature representation in online detection, and achieves high-precision defect classification.

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Abstract

The application discloses a transparent material stress birefringence online polarization defect classification method and relates to the field of transparent material optical detection. The method comprises the following steps: collecting an original polarization image of a to-be-detected transparent material, pre-processing the original polarization image, and generating a calibrated polarization image; canceling vibration noise in the light path of the calibrated polarization image based on an optical phase conjugation technology to obtain a pure polarization light field signal; calculating a retardation distribution diagram of the transparent material according to the pure polarization light field signal, performing topological data analysis on the retardation distribution diagram, and extracting a macroscopic topological feature vector; and dividing the macroscopic topological feature vector into macroscopic layout features reflecting the overall topological structure of a stress field and microscopic morphological features reflecting the local geometric morphology of a defect according to feature sources and physical meanings. The application realizes high-precision defect classification and effectively solves the problem of low classification precision caused by large vibration interference and insufficient feature representation in online detection.
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