Online detecting method and system for surface defects of cold-rolled strip steel

Abstract

The invention relates to the technical field of surface defect detecting of cold-rolled strip steel, and provides an online detecting method for surface defects of the cold-rolled strip steel. The method is characterized by comprising the following steps of: S1, irradiating the cold-rolled strip steel by using light and receiving an image reflected by the cold-rolled strip steel; S2, preprocessingthe image; S3, constructing a deep learning network for the surface defect detecting of the cold-rolled strip steel, extracting defect features of the defect image by utilizing the deep learning network for the surface defect detecting of the cold-rolled strip steel and identifying defect types of the defect features; and S4, determining the quality grade of the cold-rolled strip steel accordingto the defect types, the severity degree, the quantity of the defect features and the distribution situation of the detects. The online detecting method for the surface defects of the cold-rolled strip steel, which is provided by the invention, is capable of quickly identifying the essential defect types of the surface defects of the cold-rolled strip steel and is good in adaptability.

Description

technical field [0001] The invention relates to the technical field of detection of surface defects of cold-rolled strip steel, in particular to an online detection method and detection system for surface defects of cold-rolled strip steel. Background technique [0002] The judgment of the surface quality of cold-rolled strip is mainly based on the type, severity, quantity and distribution of defects on the surface of cold-rolled strip. It is extremely necessary to improve the identification effect of the existing online detection system for surface defects of cold-rolled strip. [0003] At present, most of the on-line detection systems for cold-rolled strip surface defects are realized by computer vision technology, including several steps such as target segmentation, artificial defect feature extraction, artificial feature description calculation, statistical methods or shallow network identification. Due to the variety of image types and low contrast of cold-rolled strip ...

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to overcome the defects of the prior art, and provides an online detection method for surface defects of cold-rolled strip steel, which uses a deep learning network to establish a machine learning model with a rich hidden layer structure by simulating the hierarchical structure of the visual perception system , through a large number of sample data training, useful essential features can be obtained, and the accuracy of defect classification, identification and detection can be improved

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