Multi-modal defect automatic classification method and device, equipment and storage medium

By employing a multimodal defect automatic classification method, and combining microscopic images and X-ray images for feature fusion and reliability assessment, the problem of lacking multimodal data collaborative processing in existing technologies is solved, achieving high-precision and high-efficiency defect detection.

CN121788515APending Publication Date: 2026-04-03WUHAN ZHONGDAO OPTOELECTRONIC EQUIP CO LTD
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Patent Information

Application Number
CN202512029769.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-30
Publication Date
2026-04-03

AI Technical Summary

Technical Problem

Existing automatic defect classification systems lack multimodal data collaborative processing mechanisms, resulting in insufficient reliability of classification results. In particular, they rely excessively on manual review in boundary case scenarios, making it difficult to meet the requirements for high-precision and high-efficiency detection.

Method used

A multimodal automatic defect classification method is adopted. By acquiring microscope images, X-ray images and defect feature information data, the first classification process is performed based on microscope images. When the reliability conditions are not met, feature fusion is performed and a second classification process is executed. Finally, a final judgment is made based on the defect feature information data. Deep learning and machine learning models are used for feature extraction and classification.

Benefits of technology

It improved the overall accuracy of defect classification, reduced the false positive rate, enhanced the system's adaptability to various defect characteristics, optimized the allocation efficiency of computing resources, enhanced the system's robustness and fault tolerance, and improved detection speed and efficiency.

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Abstract

The invention discloses a multi-mode defect automatic classification method, device and equipment and a storage medium. The method comprises the following steps: acquiring a microscope image, an X-ray image and defect feature information data of a to-be-detected product; first classification processing is preferentially carried out based on the microscope image, when the first classification processing result meets a first preset reliability condition, the first classification processing result is taken as a final classification result, and when the first classification processing result does not meet the first preset reliability condition, the final classification result is taken as a final classification result. Performing feature fusion based on the X-ray image and the defect feature information data, and executing second classification processing; when the second classification processing result meets a second preset reliability condition, the second classification processing result serves as a final classification result, and when the second classification processing result does not meet the second preset reliability condition, defect type judgment is conducted based on key parameters in the defect feature information data, and the final classification result is output; the classification accuracy and efficiency can be improved, and the error recognition rate is reduced.
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