Industrial-grade intelligent surface defect detection method

Abstract

The invention relates to an industrial-grade intelligent surface defect detection method, which comprises the following steps: constructing and training to generate a twin generative adversarial network GAN, repairing an input image into a normal sample through an improved GAN network, and comparing the output with a manually labeled positive sample through a twin CNN network to obtain a difference, which is a defect. The twin generative adversarial network designed by the invention does not need a large number of samples and does not need to perform data amplification, can solve the problem of small sample size of common industrial products, and reduces the occurrence of an overfitting phenomenon caused by the small number of samples and zero samples in deep learning, so that the defect detection during the development of products and new products with small defect sample size becomes possible. A cross alignment loss function CA and a distribution alignment loss function DA are used to enhance the relationship between two network outputs, and a good classification and identification effect is obtained. The model training speed is improved through an Attention mechanism and a hardware GPU, so that industrial rapid deployment becomes possible.

Description

technical field [0001] The invention relates to an industrial-grade intelligent surface defect detection method, which belongs to the technical field of industrial application of deep learning technology. Background technique [0002] With the continuous development of machine vision based on deep learning in image processing-related applications, deep learning has become the leader in academia, and the demand for it in the industrial field is becoming more and more urgent. However, due to the particularity of image processing applications in the industrial field: [0003] 1) Small sample size: In industrial production, many products are produced according to demand, resulting in a small number of products in a batch, and fewer products with defects that can be used as samples. In addition, some products are expensive and difficult to provide A large number of samples can be used for training. [0004] 2) High resolution. In industrial production, there are high requiremen...

AI Technical Summary

Problems solved by technology

Obviously, it is difficult to directly transfer the methods in the academic world to the industrial field, and it is difficult to meet the needs of the industrial field

[0006] In addition, the difficulty of interpretation and uncontrollability of deep neural networks determines that the current machine vision is still difficult to meet the requirements of many industrial-level applications, which affects practical applications. For the small sample size, high resolution, and variety in the industrial field, propose the invention

Images