Power defect detection method, device, equipment, medium and program product
By integrating feature extraction and target classification models for multi-scale feature processing, the problem of inaccurate feature extraction in power defect detection using visual language models is solved, achieving higher detection accuracy and robustness.
CN121997146APending Publication Date: 2026-05-08GUANGDONG POWER GRID CO LTD DONGGUAN POWER SUPPLY BUREAU
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Patent Information
- Application Number
- CN202610188246.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-02-09
- Publication Date
- 2026-05-08
AI Technical Summary
Technical Problem
In existing technologies, when using visual language models for power defect detection, feature extraction is inaccurate, leading to inaccurate defect detection.
Method used
By employing a fusion feature extraction model and a target classification model, and through multi-scale feature extraction, feature fusion, discriminative feature extraction, and classification, the probability of occurrence of each type of power defect is obtained.
Benefits of technology
It improves the accuracy and generalization of power defect detection, can handle images with poor lighting and shooting angles, and enhances the matching degree and robustness between features and power defects.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention provides a power defect detection method, device and equipment, a medium and a program product. According to the method, an obtained to-be-detected electric power image and a preset defect detection prompt text are input into a fusion feature extraction model to obtain a fusion feature matrix; performing multi-scale feature extraction processing on the fused feature matrix through a target classification model to obtain a plurality of first feature matrixes; performing fusion processing on all the first feature matrixes to obtain a second feature matrix; performing discriminant feature extraction processing on the second feature matrix to obtain a third feature matrix; and carrying out classification processing on the third feature matrix to obtain the occurrence probability of each power defect. According to the scheme, the fusion features obtained by the fusion feature extraction model are processed through the target classification model, the matching degree of the features and the electric power defects is improved, and therefore the accuracy of the occurrence probability of the electric power defects is improved.
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