High-voltage electrical equipment surface defect identification method based on image processing

By employing multi-source image acquisition and feature fusion technology, the problem of poor robustness in identifying surface defects of high-voltage electrical equipment under complex lighting and background interference was solved, enabling accurate identification of minute defects.

CN120953292AActive Publication Date: 2025-11-14CHINA THREE GORGES PROJECTS DEV CO LTD +1
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Patent Information

Application Number
CN202511492983.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-10-20
Publication Date
2025-11-14
Estimated Expiration
2045-10-20

AI Technical Summary

Technical Problem

Traditional image processing methods are not robust enough for identifying surface defects in high-voltage electrical equipment and are difficult to accurately identify minute defects under complex lighting and background interference.

Method used

Using multi-source image acquisition technology, combined with gradient analysis, structural tensor, local texture intensity and anisotropic coefficients, a defect saliency map is constructed by Laplacian filtering and Gaussian kernel filtering, and the feature map is fused for defect identification.

Benefits of technology

It improves the accuracy of surface defect identification in high-voltage electrical equipment and can effectively identify minute defects in complex environments.

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Abstract

The invention relates to the technical field of image processing, in particular to a high-voltage electrical equipment surface defect identification method based on image processing. The method comprises the following steps: analyzing the gradient of pixel points in a to-be-analyzed image of a to-be-detected area on the surface of the high-voltage electrical equipment to obtain the weight of each pixel point; weighting the gray value of each pixel point in the to-be-analyzed image subjected to Laplacian filtering by using the weight of each pixel point of the to-be-analyzed image to obtain a first feature map; calculating a local standard deviation of each pixel point in the to-be-analyzed image so as to obtain a first parameter and a second parameter; constructing two Gaussian kernels based on the first parameter and the second parameter to filter the to-be-analyzed image to obtain a second feature map; fusing the first feature map and the second feature map of the to-be-analyzed image to obtain a defect saliency map of the to-be-analyzed image; and recognizing a surface defect area of the high-voltage electrical equipment based on the defect saliency map of each to-be-analyzed image. According to the invention, the accuracy of high-voltage electrical equipment surface defect identification can be improved.
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