A new method using the convolutional neural network with compressive sensing for fabric defect classification based on small sample sizes

Wei, Bing; Hao, Kuangrong; Tang, Xue-song

doi:10.1177/0040517518813656

Cited by 68 publications

(49 citation statements)

References 47 publications

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“…These spectral and structural methods produced enhanced accuracy compared to other methods. Wei et al, 2 Balci Kilic and Okur, 3 Li et al 4 and Cao et al 5 used the Support Vector Machine (SVM), a spectral method that has been used as a conventional method for defect detection and classification. The SVM has a highly precise mathematical model for fabric defect detection.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Wei et al, 2 Xin et al, 25 Xu et al, 26 Li et al, 27 Zheng et al, 28 and Zhou and Wang 29 proposed a method for the formation of discriminative patches for a batch of images. Each patch they considered as a cluster.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Each ERU with kernel value MðxÞ is used to normalize the pre-processed ERU and formation of the feature vector with Equations (1) and (2). Each iteration feeds forward weight and each ERU decomposition kernel is given by…”

Section: Deep Learning Frameworkmentioning

confidence: 99%

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Effective textile quality processing and an accurate inspection system using the advanced deep learning technique

Pitchaimani¹,

Rajan²

2019

Textile Research Journal

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This research paper focuses on the innovative detection of defects in fabric. This approach is based on the design and development of a computer-assisted system using the deep learning technique. The classification network is modeled using the ResNet512-based Convolutional Neural Network to learn the deep features in the presented fabric. Being an accurate method, this enables accurate localization of minute defects too. Our classification is based on three major steps; firstly, an image acquired by the NI Vision model and pre-processed for a standard pattern to Kullback Leibler Divergence calculation. Secondly, standard textile fabrics are presented to train the Convolutional Neural Network to classify the defective region and the defect-free region. Finally, the testing fabrics are examined by the trained deep Convolutional Neural Network algorithm. To verify the performance, multiple fabrics are presented and the classification accuracy is evaluated. For standard defects on defective fabrics, an average accuracy of 96.5% with 98.5% precision is obtained. Experimental results on the standard Textile Texture Database dataset confirmed that our method provides better results compared with similar recent classification methods, such as the Support Vector Machine and Bayesian classifier.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Effective textile quality processing and an accurate inspection system using the advanced deep learning technique

Pitchaimani¹,

Rajan²

2019

Textile Research Journal

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“…On the other hand, [16], [17] use the image blocking method to expand the size of training data and then to train neural networks to identify defect contained in these image blocks, thus locating the defective area. [18] gets the local image blocks of the original images (the size is 1280×1024) manually that only contain one fabric defect type. The size of the local images is 227×227.…”

Section: Related Workmentioning

confidence: 99%

Fabric Defect Detection Method Combing Image Pyramid and Direction Template

Xie

Zhang

2019

IEEE Access

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Focusing on the fabric defect detection with periodic-pattern and pure-color texture, an algorithm based on Direction Template and Image Pyramid is proposed. The detection process is divided into two stages: model training and defect localization. During the model training stage, we construct an Image Pyramid for each fabric image that does not contain any defects. Then, Stacked De-noising Convolutional Auto-Encoder (SDCAE) is used for image reconstruction, its training sets are created by randomly extracting image blocks from image pyramid, which makes the feature information of the image block more abundant and the reconstruction effect of the model more remarkable. During the defect localization stage, the image to be detected is divided into a number of blocks, and is reconstructed by using the trained SDCAE model. Then, the candidate defective image blocks are roughly located by using the Structural Similarity Index Measurement after the image reconstruction. Subsequently, direction template is introduced to solve the problem of fabric deformation caused by factors such as fabric production environment and photographic angle. We select the direction template of the images to be detected, filter the candidate defective blocks, and further reduce false detection rate of the proposed algorithm. Furthermore, there is no need to calculate size of periodic-pattern during detection for periodic textured fabric. The algorithm is also suitable for defect detection for pure-color fabrics. The experimental results show that the proposed algorithm can achieve better defect localization accuracy, and receive better results in detection of pure-color fabrics, compared with traditional methods. INDEX TERMS Fabric defect detection, direction template, image pyramid, stack de-noising convolutional auto-encoder, similarity measure.

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“…With the rapid development of artificial intelligent technology, deep learning method has achieved brilliant performance in image classification and detection. Numerous learning‐based methods [13–18] have been proposed to solve the problems of fabric defect detection because of its greater extraction ability of image features. Jing et al [19] proposed a learning‐based method with a deep convolutional neural network (CNN) to extract the features of fabric, and used the mean shift algorithm to segment the defects, realising the detection of Yarn‐dyed fabric.…”

Section: Introductionmentioning

confidence: 99%

Real‐time fabric defect detection based on multi‐scale convolutional neural network

Zhao

Zhang

et al. 2020

IET Collaborative Intelligent Manufacturing

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A new method using the convolutional neural network with compressive sensing for fabric defect classification based on small sample sizes

Cited by 68 publications

References 47 publications

Effective textile quality processing and an accurate inspection system using the advanced deep learning technique

Effective textile quality processing and an accurate inspection system using the advanced deep learning technique

Fabric Defect Detection Method Combing Image Pyramid and Direction Template

Real‐time fabric defect detection based on multi‐scale convolutional neural network

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