An industrial-grade intelligent surface defect detection method

Abstract

The invention relates to an industrial-grade intelligent surface defect detection method. By constructing and training a twin generation adversarial network GAN, the input image is repaired into a "normal sample" through an improved GAN network, and the output is compared with a manually labeled positive sample through the twin The CNN network is compared, and the difference is a defect. The twin generation adversarial network designed by the present invention does not require a large number of samples or data amplification, which can solve the problem of small sample sizes of common industrial products and reduce the occurrence of over-fitting phenomena caused by deep learning with few samples and zero samples. , making it possible to detect defects in products with a small number of defective samples and in new product development. The cross alignment loss function CA and the distribution alignment loss function DA are used to strengthen the relationship between the outputs of the two networks and achieve better classification and recognition results. The model training speed is improved through the Attention mechanism and hardware GPU, making rapid industrial deployment 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

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