Dictionary learning framework for fabric defect detection

Zhou, Jian; Semenovich, Dimitri; Sowmya, Arcot; Wang, Jun

doi:10.1080/00405000.2013.836784

Cited by 42 publications

(26 citation statements)

References 23 publications

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“…Model-based approaches, which be applied to the fabric image with random changes, can generate the texture to match the observed texture. It mainly include Markov Random Field model [21] and Autoregressive model [22][23]. Markov Random Field model uses the dependence on pixel points in fabric images.…”

Section: Related Workmentioning

confidence: 99%

“…Recent research on defect detection mainly includes dictionary learning based methods [23][24], sparse plus low rank strategy based methods [25][26] and convolutional neural network (CNN) based methods [27]. Dictionary learning based methods learned a dictionary from many image patches, where the image patches are acquired by defect-free images.…”

Section: Related Workmentioning

confidence: 99%

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A Sobel Operator Combined with Patch Statistics Algorithm for Fabric Defect Detection

Jiang¹,

Jin²,

Wang³

et al. 2020

KSII TIIS

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In the production of industrial fabric, it needs automatic real-time system to detect defects on the fabric for assuring the defect-free products flow to the market. At present, many visual-based methods are designed for detecting the fabric defects, but they usually lead to high false alarm. Base on this reason, we propose a Sobel operator combined with patch statistics (SOPS) algorithm for defects detection. First, we describe the defect detection model. mean filter is applied to preprocess the acquired image. Then, Sobel operator (SO) is applied to deal with the defect image, and we can get a coarse binary image. Finally, the binary image can be divided into many patches. For a given patch, a threshold is used to decide whether the patch is defect-free or not. Finally, a new image will be reconstructed, and we did a loop for the reconstructed image to suppress defects noise. Experiments show that the proposed SOPS algorithm is effective.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Sobel Operator Combined with Patch Statistics Algorithm for Fabric Defect Detection

Jiang¹,

Jin²,

Wang³

et al. 2020

KSII TIIS

View full text Add to dashboard Cite

show abstract

“…Meanwhile, rising labour costs place a burden on the textiles industry. To reduce costs, improve efficiency and ensure high quality products, an automated inspection system has been deployed in the research industry for a considerable time 2 . But products like towels with complicated patterns and variable textures present a challenge for automated inspection systems.…”

Section: Introductionmentioning

confidence: 99%

“…A support vector machine was used to classify the fabric defects. Zhou et al 2 presented a dictionary‐learning framework to implement fabric defect detection. Li et al 8 recommended that saliency map and histogram features should be employed to effectively discriminate between defective and zero‐defect fabric images.…”

Section: Introductionmentioning

confidence: 99%

A novel feature‐based network with sequential information for textile defect detection

Liu

2020

Coloration Technology

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In this paper, a novel feature‐based attention network with sequential information is proposed for fabric defect detection. As an important part of the textiles industry, fabric quality inspection needs to be automated in the latest wave of intelligent transformation. To this end, fabric defect detection algorithms have been widely studied. In this paper, an efficient defect detection model for fabrics was built. As the appearances of defects change with the type of fabric, manual features which denote the overall situation of the fabric are used as prior knowledge. The feature‐based attention module discussed in this paper can generate attention maps from these manual features to rectify the responses of the feature maps according to the whole situation of the input image. Multidirectional long short‐term memory networks are implemented to extract context information from continuous defects. When making a judgement, taking sequential information into consideration may reduce the number of unexpected misjudged decisions compared with the number of those made independently pixel by pixel. Both of those two modules can be integrated into any existing convolutional neural network model and trained in an end‐to‐end manner. A fabric defect dataset is built to train and test the models. In this paper, several models with different architectures are implemented to verify our ideas, and are supported by results confirming the efficiency of the proposed methods.

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“…12 The research includes image denoising and compression, face recognition, pattern classification, defect detection, and so on. 12 The research includes image denoising and compression, face recognition, pattern classification, defect detection, and so on.…”

Section: Introductionmentioning

confidence: 99%

Patterned fabric defect detection via convolutional matching pursuit dual-dictionary

Jing

Fan

2016

Opt. Eng

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Patterned fabric defect detection via convolutional matching pursuit dual-dictionary," Opt. Eng. 55(5), 053109 (2016), Abstract. Automatic patterned fabric defect detection is a promising technique for textile manufacturing due to its low cost and high efficiency. The applicability of most existing algorithms, however, is limited by their intensive computation. To overcome or alleviate the problem, this paper presents a convolutional matching pursuit (CMP) dual-dictionary algorithm for patterned fabric defect detection. A preprocessing with mean sampling is performed to eliminate the influence of background texture of fabric defects. Subsequently, a set of defect-free image blocks are selected as a sample set by sliding window. Dual-dictionary and sparse coefficiencies of the defect-free sample set are obtained via CMP and the K-singular value decomposition (K-SVD) based on a Gabor filter. Then we employ the defect-free and defective fabric image's projections onto the dual-dictionary as features for defect detection. Finally, the test results are determined by comparing the distance between the features to be measured. Experimental results reveal that the proposed algorithm is effective for patterned fabric defect detection and an acceptable average detection rate reaches by 94.2%. Downloaded From: http://opticalengineering.spiedigitallibrary.org/ on 06/11/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx Optical Engineering 053109-2 May 2016 • Vol. 55(5) Jing, Fan, and Li: Patterned fabric defect detection via convolutional matching pursuit. . . Downloaded From: http://opticalengineering.spiedigitallibrary.org/ on 06/11/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx

show abstract

Dictionary learning framework for fabric defect detection

Cited by 42 publications

References 23 publications

A Sobel Operator Combined with Patch Statistics Algorithm for Fabric Defect Detection

A Sobel Operator Combined with Patch Statistics Algorithm for Fabric Defect Detection

A novel feature‐based network with sequential information for textile defect detection

Patterned fabric defect detection via convolutional matching pursuit dual-dictionary

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