Adversarial network texture surface defect detection method based on abnormal feature editing

Abstract

The invention belongs to the technical field of image processing, and particularly discloses an adversarial network texture surface defect detection method based on abnormal feature editing. The adversarial network texture surface defect detection method comprises the following steps of: acquiring defect-free good product images and corresponding defect images to form an image data set jointly; constructing an adversarial network which comprises a generator and a discriminator, wherein the generator is used for extracting image features, detecting abnormal features and then editing the abnormal features by using normal features to obtain a reconstructed image, and the discriminator is used for discriminating the good product images and the reconstructed image; training the adversarial network through using the image data set according to a pre-constructed optimization target, so as to obtain a reconstructed image generation model; and inputting an image to be detected into the reconstructed image generation model to obtain a reconstructed image corresponding to the image to be detected, and further acquiring the texture surface defects according to the image to be detected and thecorresponding reconstructed image. The adversarial network texture surface defect detection method has high detection precision for defects of different shapes, sizes and contrast ratios on differenttexture surfaces.

Description

technical field [0001] The invention belongs to the technical field of image processing, and more particularly relates to a method for detecting surface defects of an adversarial network texture based on abnormal feature editing. Background technique [0002] In the field of industrial manufacturing, due to factors such as the quality of raw materials and complex manufacturing processes, texture defects will occur on the surface of products, such as mobile phone screens, wood, textiles, and ceramic tiles. Texture defects refer to localized areas of irregular brightness changes or disruption of textural structure. These texture defects will directly affect the user experience. In order to control product quality, all types of texture defects should be effectively controlled during the manufacturing process, so defect detection is the basis and key to improving the entire industrial manufacturing industry. [0003] The image recognition technology only needs to collect the te...

AI Technical Summary

Problems solved by technology

[0004] At present, there are still three difficulties in the detection of textured surface defects: various types of textures, that is, there are many kinds of regular and irregular textures, and there are many types of defects, that is, multiple defects may appear on the same textured surface, and there are few defect samples in the industry, so it is difficult to collect with annotations, therefore, defect detection on textured surfaces remains an extremely challenging problem

Some existing research results can only partially meet the accuracy and robustness requirements of texture surface defect detection

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