Method and system for detecting defects of flat plate shell based on Lie-Mean

Abstract

The invention discloses a method and a system for detecting defects of flat plate shell based on Lie-Mean. The method comprises the following steps: obtaining a flat plate shell image and carrying outimage preprocessing; manually marking the preprocessed image, marking the flat shell product with the defect, and giving the category with the defect to obtain a data sample set; mapping the obtaineddata sample set into a plum group manifold space, and calculating to obtain a mean value in the plum group of each product category; carrying out image preprocessing on the to-be-detected flat plateshell image, and mapping the to-be-detected flat plate shell image into the plum group manifold space to obtain a test sample; and calculating geodesic distances from the test samples to the obtainedmean values in the plum groups of each category, and judging the category to which the mean value in the plum group with the shortest distance belongs as the category of the test samples. The method has the advantages that (1) the method has good comprehensiveness; and (2) few calculation parameters, excellent calculation performance and small time delay are achieved, and the classification accuracy is improved while the good working efficiency is ensured.

Description

technical field [0001] The invention relates to a defect detection method for a flat shell, in particular to a Lie-Mean-based flat shell defect detection method and system. Background technique [0002] With the development of the economy, people are no longer satisfied with the use of desktop computers or notebook computers, so tablets have emerged as the times require. With the popularity of tablets and rapid replacement, there is a huge demand for tablet casing products. During the entire production process from the raw materials of the product to the final shell product, due to production level, process, logistics transportation and accidents, etc., the flat shell has defects such as scratches, breakage, and uneven color spraying, which will affect its appearance and reduce User experience, so such defective products should not enter the market. Although the current industrial production level has been greatly improved, most of the defect detection of industrial produc...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the problems of many parameters, complex network structure, large amount of calculation, and time extension in the existing defect detection methods, the present invention proposes a flat shell defect detection method and system based on Lie-Mean, using the Lie group machine learning method, By projecting the training samples into the Lie group manifold space, the Lie group mean of each training sample is calculated on the Lie group manifold space, and the unknown sample is considered to be the same as the unknown sample is closer to the Lie group mean of one of the categories than other categories. most likely to fall into this category

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