Bobbin yarn appearance defect classification method based on a deep convolutional neural network

Abstract

The invention discloses a bobbin yarn appearance defect classification method based on a deep convolutional neural network. The bobbin yarn appearance defect classification method comprises the following steps of S1, carrying out image cutting; S2, carrying out manual sorting; S3, cleaning the data; S4, reorganizing the data; S5, carrying out data augmentation; S6, carrying out a neural network structure; S7, carrying out performing model training; S8, adjusting parameters; S9, carrying out model testing; and S10, after obtaining the optimal data, starting from the step S3 until the loss valueof the verification set approaches 0 and cannot be reduced, and finally obtaining an optimal model. Compared with a traditional machine vision image processing method, the glass fiber bobbin yarn appearance detection method based on the convolutional neural network has the advantages that mathematical fitting is carried out on surface layer characteristic data extracted from a picture; and finally, a satisfactory result can be achieved by using a simple classifier, so that the reliability and the detection speed are improved, and the labor cost is reduced.

Description

technical field [0001] The invention relates to a method for classifying bobbin appearance defects based on a deep convolutional neural network. Background technique [0002] After the glass fiber bobbins pass through the twisting machine, they are formed into bobbins one by one. The bobbins need to go through peeling, Tex measurement, appearance inspection, weighing, packaging and other links before they can leave the factory. At present, the appearance inspection is done by a lot of manual work, which requires workers to have strong experience and observation ability to distinguish features, which has the problems of strong chance, low reliability, and inability to accurately detect the appearance defects of glass fiber bobbins. At present, the method based on deep learning has far surpassed the traditional machine vision detection and recognition algorithm. Convolutional neural network is one of the more mature methods of deep learning computer vision category. It is a fe...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a glass fiber tube based on deep convolutional neural network Yarn appearance detection and classification method to solve the problem that the existing glass fiber bobbin appearance defect detection requires workers to have strong experience and observation ability to distinguish features, which has strong chance, low reliability, and cannot accurately detect the appearance of glass fiber bobbin defects

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