Yarn-dyed Fabric Defect Detection using U-shaped De-noising Convolutional Auto-Encoder

Zhang, Hongwei; Tan, Quan-lu; Lu, Shiping; Ge, Zhiqiang; D, Gu

doi:10.1109/ddcls49620.2020.9275154

Cited by 12 publications

(7 citation statements)

References 15 publications

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“…Zhang et al. 16 proposed a fabric defect detection method based on U-shaped denoising convolutional autoencoder (UDCAE). This method improves the fabric defect detection ability of the model by combining the autoencoder and U-Net.…”

Section: Related Workmentioning

confidence: 99%

“…The method detects the defects of yarn-dyed fabrics by calculating the residuals of the test image and the reconstructed image. Zhang et al 16 proposed a fabric defect detection method based on U-shaped denoising convolutional autoencoder (UDCAE). This method improves the fabric defect detection ability of the model by combining the autoencoder and U-Net.…”

Section: Unsupervised Fabric Defect Detection Based On Deep Learningmentioning

confidence: 99%

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Attention-based Feature Fusion Generative Adversarial Network for yarn-dyed fabric defect detection

Zhang

Qiao

et al. 2022

Textile Research Journal

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Defects on the surface of yarn-dyed fabrics are one of the important factors affecting the quality of fabrics. Defect detection is the core link of quality control. Due to the diversity of yarn-dyed fabric patterns and the scarcity of defect samples, reconstruction-based unsupervised deep learning algorithms have received extensive attention in the field of fabric defect detection. However, most existing deep learning algorithms cannot fully extract shallow, high-frequency and high-level information, which limits their ability to reconstruct yarn-dyed fabric images. In this article, we propose an Attention-based Feature Fusion Generative Adversarial Network framework for unsupervised defect detection of yarn-dyed fabrics. The framework utilizes a modified Feature Pyramid Network to fuse multi-level information and utilizes an attention mechanism to enhance the model's feature representation capabilities. The Attention-based Feature Fusion Generative Adversarial Network consists of an attention fusion generator and a patch-level discriminator. In the attention fusion generator, the Feature Pyramid Network with EfficientNetV2 as the backbone is used as the core building block, and different feature fusion methods are used to avoid the loss of information in the process of network deepening. The attention mechanism is used to enhance the channel and spatial-wise correlation of features, which helps the model to focus on more meaningful information by recalibrating the feature maps. In the discriminator, the patch-level discriminator is used to calculate the similarity between the reconstructed image and the original image from a local perspective, thereby improving the model's attention to texture details. Experimental results on public datasets demonstrate the effectiveness of the proposed method compared to other methods.

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Section: Related Workmentioning

confidence: 99%

Section: Unsupervised Fabric Defect Detection Based On Deep Learningmentioning

confidence: 99%

Attention-based Feature Fusion Generative Adversarial Network for yarn-dyed fabric defect detection

Zhang

Qiao

et al. 2022

Textile Research Journal

Self Cite

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“…A U-shaped denoising CAE is constructed in Ref. [17] by collecting a training model of defect-free yarn-dyed fabrics to identify defects. The paper in Ref.…”

Section: Insulator Defect Detection Based On Deep Learningmentioning

confidence: 99%

A novel insulator defect detection scheme based on Deep Convolutional Auto‐Encoder for small negative samples

et al. 2022

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This paper presents a novel insulator defect detection scheme based on Deep Convolutional Auto‐Encoder (DCAE) for small negative samples. The proposed DCAE scheme combines the advantages of supervised learning and unsupervised learning. In order to reduce the high cost of training Deep Neural Networks, this paper pre‐trained the Convolutional Neural Networks (CNN) through open labelled datasets. Through transferring learning, the encoder part of the traditional Convolutional Auto‐Encoder was replaced by the first three layers of the CNN, and a small number of defect samples were used to fine‐tune the parameters. A threshold discrimination scheme was designed to evaluate the model detection, realising the self‐explosion detection of insulator by judging the residual result and abnormal score. The experimental results show that compared with the existing insulator self‐explosion detection schemes, the proposed scheme can reduce the model training time by up to 40%, and the recognition accuracy can reach 97%. Moreover, this model does not need a large number of insulator labelled data and is especially suitable for small negative sample application.

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“…This model combines an image pyramid structure and a convolutional denoising autoencoder. Zhang et al 18 proposed a U‐shaped Denoising Convolutional Autoencoder (UDCAE) model based on the traditional autoencoder and combined with the classic U‐Net network. The GAN was first proposed by Goodfellow et al 19 In the image processing field, the GAN is a novel deep learning model for unsupervised learning with complex distributions.…”

Section: Related Workmentioning

confidence: 99%

A contrastive learning‐based attention generative adversarial network for defect detection in colour‐patterned fabric

Zhang

Chen

et al. 2022

Coloration Technology

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The pattern style of colour‐patterned fabrics is varied. Defective fabric samples are scarce in the production of small batches of colour‐patterned fabrics. Therefore, the unsupervised defect‐detection method for colour‐patterned fabric has attracted wide attention. Several unsupervised defect‐detection methods for colour‐patterned fabrics based on convolutional neural networks have been proposed. However, convolutional neural network methods cannot learn long‐range semantic information interaction well because of the intrinsic locality of convolution operations. Besides, as the number of layers in the convolutional neural network increases, the feature maps become more and more complex. Convolutional neural networks experience difficulties in coordinating numerous parameters and extracting key features from complex feature maps. Both these problems reduce the accuracy of the model for detecting defects in colour‐patterned fabrics. In this paper, we propose a Contrastive Learning‐based Attention Generative Adversarial Network (CLAGAN) for defect detection in colour‐patterned fabrics. The CLAGAN possesses two important parts: contrastive learning and a channel attention module. Contrastive learning captures long‐range dependencies by calculating the cosine similarity between different features. The channel attention module assigns different weights to each channel of the feature maps, and it enables the model to extract key features from those feature maps. The experimental results verified the effectiveness of the CLAGAN. It obtained values of 38.25% for intersection over union and of 51.67% for the F1‐measure on the YDFID‐2 public dataset.

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Yarn-dyed Fabric Defect Detection using U-shaped De-noising Convolutional Auto-Encoder

Cited by 12 publications

References 15 publications

Attention-based Feature Fusion Generative Adversarial Network for yarn-dyed fabric defect detection

Attention-based Feature Fusion Generative Adversarial Network for yarn-dyed fabric defect detection

A novel insulator defect detection scheme based on Deep Convolutional Auto‐Encoder for small negative samples

A contrastive learning‐based attention generative adversarial network for defect detection in colour‐patterned fabric

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