Industrial product surface defect detection method based on sample enhancement

Abstract

The invention discloses an industrial product surface defect detection method based on sample enhancement. The method comprises the following steps: 1) carrying out size standardization, normalization, cutting and classification on an industrial product surface image; 2) carrying out data enhancement of random flipping on the picture with the defect; 3) randomly splicing and enhancing the defective picture and the normal picture; 4) carrying out iterative training by using a Cascade-RCNN algorithm; 5) acquiring a Cascade-RCNN detection model; 6) carrying out sliding window detection on the industrial product surface picture to be detected and the texture template picture determining no defect through the Cascade-RCNN detection model, splicing results detected by a sliding window, and comparing the results obtained by the sliding window detection and the texture template picture to finally obtain the defect category and region annotation of the picture to be detected. According to the method, the influence of conditions such as illumination, exposure and displacement on defect detection can be effectively reduced, the detection stability is improved, the resolution capability of thetwo-stage target detector on patterns and backgrounds is improved, and the false detection rate is reduced.

Description

technical field [0001] The invention relates to the technical field of detection of surface defects of industrial products, in particular to a method for detecting surface defects of industrial products based on sample enhancement. Background technique [0002] Defect detection is an important part of the production process, which guarantees the reliability of industrial products. The surface defect detection of industrial products needs to accurately locate the defect position on the surface and classify the located defects, which is a typical target detection problem. Prior to this, the surface defect detection technology of industrial products usually used traditional machine vision technology to perform operations such as image grayscale binarization, edge contour extraction, and template matching. The disadvantage of this type is that it is very sensitive to changes in image illumination and displacement. , with poor robustness. In addition, the previous research on s...

AI Technical Summary

Problems solved by technology

[0004] The existing two-stage deep learning object detector has high accuracy and good versatility, but it is still difficult to distinguish between defects and background textures in textured surface defect detection, and normal pictures without defects cannot participate in model training and industrial The picture of the product has a large demand for video memory, etc.

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