U型去噪卷积自编码器色织衬衫裁片缺陷检测

doi:10.19665/j.issn1001-2400.2021.03.016

Abstract

Abstract:

Due to the scarcity of defective yarn-dyed fabric samples in the textile industry,the imbalance of defect types and the high cost to manually design defect features gained the poor generalization,and the supervised model solves the problem of yarn-dyed fabric defect detection with difficulty.Therefore,an unsupervised reconstruction model is proposed based on the denoising U-shaped convolutional auto-encoder,and a residual analysis method ispresented to inspect yarn-dyed shirt piece defects.First,normal samples are collected for a specific fabric in the training phase.Second,an unsupervised reconstruction model is trained based on the denoising U-shaped deep convolutional auto-encoder,which is employed to reconstruct new test samples.Finally,calculating the residual map between the original image and correspondingly reconstructed image is used to inspect and locate areas of fabric defects.Experimental results show that the proposed method can inspect and locate many types of yarn-dyed fabric defects without any defective fabric samples.

Key words: yarn-dyed shirt piece, defect detection, unsupervised learning, convolutional auto-encoder, U net

CLC Number:

TS107

ZHANG Hongwei,TAN Quanlu,LU Shuai,GE Zhiqiang,XU Jian. Yarn-dyed shirt piece defect detection based on an unsupervised reconstruction model of the U-shaped denoising convolutional auto-encoder[J].Journal of Xidian University, 2021, 48(3): 123-130.

Figures/Tables 8

References 18

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检测模型	数据集 Ⅰ	数据集 Ⅱ	数据集 Ⅲ	数据集 Ⅳ
MSCDAE	0.094 6	0.049 3	0.035 2	0.043 9
UDCAE	0.086 5	0.060 2	0.034 9	0.036 7

评价指标	数据集 Ⅰ		数据集 Ⅱ		数据集 Ⅲ		数据集 Ⅳ
评价指标	MSCDAE	UDCAE	MSCDAE	UDCAE	MSCDAE	UDCAE	MSCDAE	UDCAE
P	67.52	71.01	80.64	80.68	80.64	80.68	79.88	78.86
R	86.16	88.26	56.20	93.84	97.65	98.24	98.58	98.50
A_CC	98.15	98.17	97.65	98.24	56.20	93.84	85.26	89.68

Yarn-dyed shirt piece defect detection based on an unsupervised reconstruction model of the U-shaped denoising convolutional auto-encoder

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