Defect detection method and device, electronic equipment and storage medium

By combining ConvNeXt networks and Fourier convolutions, a defect detection method was developed that addresses the problem of insufficient accuracy in identifying complex defects in aluminum-magnesium alloy consumer electronics frames or shells, achieving high-precision automated detection and real-time linkage.

CN121921249APending Publication Date: 2026-04-24LUXCASE PRECISION TECH (YANCHENG) CO LTD
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Patent Information

Application Number
CN202511806167.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-03
Publication Date
2026-04-24

AI Technical Summary

Technical Problem

Existing defect detection methods for aluminum-magnesium alloy consumer electronics frames or shells are insufficient in identifying complex defects, especially when the boundaries are blurred and the textures are similar. Furthermore, they cannot be linked with the production line in real time and lack automated rejection and traceability functions.

Method used

A defect detection method combining ConvNeXt network and Fourier convolution is adopted. The first model with the first loss function is configured to detect defects in metal anodized surface images, ensuring the consistency between foreground and background features and improving the recognition accuracy of complex textures and edge regions.

Benefits of technology

It enables automated detection of metal oxide surfaces, improves defect identification accuracy, supports real-time linkage and automated rejection, and has traceability function.

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Abstract

The invention discloses a defect detection method and device, electronic equipment and a storage medium. The method comprises the following steps: determining a first image; performing defect detection on the first image through a first model to obtain a first result; a first loss function is configured in the first model; the first loss function can instruct the first model to distinguish foreground feature information and background feature information of the first image. According to the method, the defect detection is performed on the first image through the first model, and the first loss function is configured in the first model, so that the foreground feature and the background feature of the first image can be aligned, and the feature consistency can be ensured, and therefore, the use of the first model can realize the automatic detection of the metal oxidation surface, and the detection efficiency is improved. And the defect identification precision of complex textures and edge areas on the metal oxidation surface can also be improved.
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