Aluminum plate surface defect classification method based on BP neural network and support vector machine

Abstract

The invention discloses an aluminum plate surface defect classification method based on a BP neural network and a support vector machine. The method comprises the steps that feature values of aluminum plate surface defects are extracted as the input quantity of a BP neural network classification model, and oil spots and the first class of defects are adopted as the output quantity to construct the BP neural network classification model; a plurality of support vector machine classification models are constructed through the first class of defects in a one-to-one classification method; learning samples are obtained, and the BP neural network classification model and the support vector machine classification models are trained; the oil spots and the first class of defects are classified through the BP neural network classification model, the BP neural network classification model is regarded as a testing sample of the oil spots to be removed, and the rest of the first class of defects are classified again through the support vector machine classification models; a classification result is obtained finally through statistics. According to the method, the recognition and classification rate of the oil spots on the surface of a cold rolling aluminum plate is improved, meanwhile, the overall recognition rate of the cold rolling aluminum plate surface defects is improved, and the method can be used for recognizing and classifying other metal surface defects and is simple and easy to implement.

Description

technical field [0001] The invention relates to the technical field of pattern recognition, in particular to a method for classifying surface defects of an aluminum plate based on a BP neural network and a support vector machine. It is suitable for metal surface defect detection system to identify and classify various types of metal surface defects including oil spots. Background technique [0002] The surface defect detection and classification of cold-rolled aluminum sheets has experienced three development stages: manual visual classification, traditional non-destructive testing classification and machine vision-based detection and classification. Poor sex. Traditional nondestructive testing classification methods include eddy current testing classification, infrared testing classification, magnetic flux leakage testing classification, laser testing classification, etc. These methods detect few types of defects, and the real-time detection and classification are not stro...

AI Technical Summary

Problems solved by technology

This scheme can improve the recognition and classification rate of individual defects on the surface of cold-rolled aluminum sheets and the overall recognition and classification rate to a certain extent, but it increases the complexity of the hardware and increases the cost, which is not conducive to the small and medium-sized cold-rolled aluminum sheet surface defect detection system. Aluminum plate production enterprises to promote the use

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