Surface defect detection method based on positive case training

Abstract

The invention relates to a surface defect detection method based on positive case training. The method comprises two steps of image reconstruction and defect detection, image reconstruction is to reconstruct an inputted original image into an image without defects, reconstruction steps are as follows, artificial defects and noise are added to the positive case image during training, a self-encoderis utilized for reconstruction, the L1 distance between the reconstruction result and the noise-free original image is calculated, the distance is minimized as a reconstruction target, in cooperationwith the generative adversarial network, the reconstruction image effect is optimized; defect detection is performed after image reconstruction, LBP features of the reconstructed image and the original image are calculated, after difference between the two feature images is made, the two images are binarized based on the fixed threshold, so the defects are found. The method is advantaged in thatthe depth learning method is utilized, the method can be sufficiently robust to be less susceptible to environmental changes when positive samples are enough, moreover, based on regular training, themethod does not rely on a large number of negative samples and manual annotation, the method is suitable for being used in real-world scenarios, and the surface defects can be better detected.

Description

technical field [0001] The invention belongs to the technical field of machine vision, relates to product surface defect detection, and is a surface defect detection method based on positive example training. Background technique [0002] In industrial production, defect detection is a very important part, which has a significant impact on the quality of the final product and the reputation of the product in the market. In traditional industrial production, human eyes are often used to detect defects, which will cause the following disadvantages: the subjective influence is large, and human eyes are prone to missed and wrong detections in a large number of repetitive tasks, and human resources Costs are expensive. Therefore, machine vision instead of human eye defect detection can not only reduce the cost but also improve the accuracy. [0003] Machine vision algorithms will encounter many problems and challenges in the actual industrial defect detection environment. The a...

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Problems solved by technology

[0011] The problem to be solved by the present invention is: in the actual survival process of surface defect detection, it will encounter the problem that the production environment changes complex, and the large number of defect samples and manual labels required by the supervised deep learning method are not easy to obtain.

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