Surface defect detection method based on cascaded convolutional neural network

Abstract

The invention relates to a surface defect detection method based on a cascaded convolutional neural network, and the method comprises the steps: building a defect detection network based on Cascade R-CNN, and carrying out the detection of an image of a to-be-detected product collected by an industrial camera in real time through the trained and optimized defect detection network. According to theinvention, the Cascade R-CNN is improved into a defect detection network, defect regions and defect features do not need to be manually extracted, and classification is carried out while defects are positioned; Cascade R-CNN is used as a basic detection network architecture, and the excellent detection performance of the Cascade R-CNN makes the positioning and classification precision of the defects more advantageous, and the feature extraction capability is enhanced through ResNeXt; the FPN is used for detecting small defects, deformable convolution is adopted, anchor frames are added to adapt to various shapes and sizes of the defects, the defects have large enough receptive fields, the detection effect of various extreme defects is improved, the threshold value of non-maximum suppression is adjusted, and the detection accuracy is further improved.

Description

technical field [0001] The invention belongs to the application of deep learning technology in the field of machine vision detection, and in particular relates to a surface defect detection method based on a cascaded convolutional neural network. Background technique [0002] The surface defects produced in the production process of industrial products seriously affect the quality of the product itself. How to detect surface defects to control the quality of products has always been a major problem faced by manufacturers. As a large manufacturing country, China is favored by foreign consumers because of its low labor costs and the relatively low price of industrial products produced. However, as the quality of my country's labor force improves, the demographic dividend is gradually disappearing. In the future, many companies will face tremendous pressure from high-quality standards and high labor costs. The defect detection of products is a link that consumes a lot of labor...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the problems that the existing methods have low accuracy in identifying product defects, many parameters, poor robustness, and inability to take into account both classification and positioning, the present invention provides a surface defect detection method based on a cascaded convolutional neural network, which specifically includes the following Three parts: Cascade R-CNN-based defect detection network construction, network training, defect detection

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