Intelligent sorting method and system based on multi-modal defect feature fusion

Through the multimodal defect feature fusion method, combined with surface images, infrared thermal imaging and voiceprint vibration data, the limitations of traditional single mode detection are solved, and efficient, accurate detection and sorting of object defects are achieved.

CN120561867AInactive Publication Date: 2025-08-29SHENZHEN HUAKAI INFORMATION TECH CO LTD
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Patent Information

Application Number
CN202511045135.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-07-29
Publication Date
2025-08-29
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

The traditional single-modal detection method has sensitive environmental interference, high missed detection rate, high error detection rate in object defect detection, and lacks the ability to fusion analysis of multimodal data, making it difficult to meet the requirements of industrial intelligence for comprehensiveness, accuracy and real-timeness.

Method used

The multimodal defect feature fusion method is adopted to construct a defect detection reference set through data preprocessing, feature fusion and cross-modal correlation analysis of surface images, infrared thermal imaging and vocalprint vibration data, and optimize sorting decisions based on dynamic checksum historical data.

Benefits of technology

It significantly improves the comprehensiveness and accuracy of object defect detection, reduces the error detection rate and missed detection rate, realizes flexible sorting action triggering, and adapts to the diversified needs of different production scenarios.

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Abstract

The invention relates to the technical field of intelligent sorting, and discloses a multi-mode defect feature fusion intelligent sorting method and system, and the method comprises the steps: obtaining and preprocessing a surface image, infrared thermal imaging and voiceprint vibration data of an object; analyzing and generating multi-modal defect feature parameters, fusing to form a fused feature vector set, and constructing a defect detection reference set; performing dynamic matching verification on the multi-modal data based on the reference set, and analyzing the mismatching state of the surface texture and the thermal distribution by using a space alignment technology; and generating a defect form deviation degree index according to a verification result, and judging whether a sorting action is triggered or not. The system comprises a multi-modal acquisition module, a feature fusion modeling module, a form association verification module and a sorting judgment module which are used for respectively realizing data acquisition preprocessing, feature fusion modeling, cross-modal association analysis and sorting decision. Through multi-modal data fusion and cross-modal quantitative analysis, the comprehensiveness of defect detection and the sorting accuracy are improved.
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