Defect detection method and device, electronic equipment and storage medium

Abstract

The invention relates to a defect detection method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a product image of a to-be-detected product and a template image matched with the product image; respectively carrying out feature extraction on the product image and the template image to obtain a first feature map of the product image and a second feature map of the template image; analyzing the correlation between the first feature map and the second feature map to obtain a correlation attention map between the first feature map and the second feature map; fusing the first feature map and the second feature map based on the correlation attention map to obtain a first fused feature map; and detecting the first fusion feature map to obtain a defect detection result of the product image. According to the embodiment of the invention, the defect detection accuracy can be improved.

Description

technical field [0001] The present disclosure relates to the technical field of computer vision, and in particular to a defect detection method and device, electronic equipment, and a storage medium. Background technique [0002] Surface defects of industrial products, such as scratches, foaming, dripping ink, etc., will not only affect the aesthetics of industrial products, but sometimes also affect the effect and safety of their use scenarios. Traditional defect detection methods usually rely on human eye detection, which requires a lot of manpower, and the effect of human eye detection is difficult to quantify and fluctuates from time to time, and the detection effect is poor. [0003] Traditional industrial defect detection usually uses spatial or time domain features to detect defects directly using Fourier transform, Gabor transform, etc., but these defect detection methods are sensitive to defect morphology and phenomenon conditions. Due to its diversity, sometimes t...

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Problems solved by technology

[0002] Surface defects of industrial products, such as scratches, foaming, dripping ink, etc. will not only affect the aesthetics of industrial products, but also sometimes affect the effect and safety of their use scenarios

Traditional defect detection methods usually rely on human eye detection, which requires a lot of manpower, and the effect of human eye detection is difficult to quantify and fluctuates from time to time, and the detection effect is poor

[0003] Traditional industrial defect detection usually uses spatial or time domain features to detect defects directly using Fourier transform, Gabor transform, etc., but these defect detection methods are sensitive to defect morphology and phenomenon conditions. It has diversity and sometimes there will be some missed detection and false detection

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