Automated fabric defect detection—A review

Ngan, Henry Y. T.; Pang, G.K.H.; Yung, N.H.C.

doi:10.1016/j.imavis.2011.02.002

Cited by 431 publications

(216 citation statements)

References 118 publications

(298 reference statements)

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“…The importance of screening and early defect detection is recognised in a number of fields (for example, see Peng et al [39], Ngan et al [40], and Patel et al [41]). However, a detailed evaluation of these contributions is beyond the scope of this study.…”

Section: Screening and Detectionmentioning

confidence: 99%

A Mechanism for the Early Detection and Management of Physical Asset Management Strategy Execution Failure

Stimie

Vlok

2016

SAJIE

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The purpose of this paper is to present a mechanism that can assist physical asset management (PAM) practitioners and academics with the early detection and management of PAM strategy execution failure. In the pursuit of this objective, case studies were applied to develop the theory related to this topic the methodologies involved in the development of theory through case studies was were applied. The paper commences with a brief literature review of the contemporary literature on general business strategy, strategy execution, and, more specifically, PAM strategy execution failure (PAMSEF).The physical asset management strategy execution enforcement mechanism (PAMSEEM) is presented next. Validation of each one of the components of the PAMSEEM takes place within the context of an organisation that is highly dependent on physical assets (PA) dependent organization. The conclusion reached is that the PAMSEEM, can indeed assist organisations with the early detection and management of PAMSEF. OPSOMMINGIn die artikel word 'n meganisme voorgestel wat deur fisiese batebestuur praktisyns sowel as akademici gebruik kan word tydens die vroeë identifikasie en bestuur van fisiese batebestuur strategie uitvoering faling (FBBSUF). In die strewe om hierdie doelwit te bereik word die navorsingsmetodologie wat gebruik word in die skep van teorie deur middel van gevalle studies, toegepas. Die artikel begin met 'n oorsigtelike bespreking van intydse besigheidstrategie en strategie-uitvoerings literatuur en meer spesifiek fisiese batebestuur strategie faling.Die fisiese batebestuur strategie uitvoering afdwingingsmeganisme (FBBSUAM) word hierna voorgestel. Validasie van alle aspekte van die FBBSUAM vind plaas binne 'n organisasie met baie groot investering in fisiese bates. Die gevolgtrekking aan die einde van die studie is dat die FBBSUAM wel organisasies kan help met die vroeë identifikasie en bestuur van FBBSUF.

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Section: Screening and Detectionmentioning

confidence: 99%

A Mechanism for the Early Detection and Management of Physical Asset Management Strategy Execution Failure

Stimie

Vlok

2016

SAJIE

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“…Compared with detecting plain and twill defects, patterned fabric inspection is far more complex. A summary of methods of patterned fabric defect detection can be found in earlier work [1], [2]. Ng et al presented a novel method of decomposing the fabric image into a cartoon structure and repeated patterns according to the image decomposition method (ID), which is superior to other methods on benchmark images [2], [3].…”

Section: Introductionmentioning

confidence: 99%

Combining Fisher Criterion and Deep Learning for Patterned Fabric Defect Inspection

Zhang

Lin

2016

IEICE Trans. Inf. & Syst.

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SUMMARYIn this letter, we propose a novel discriminative representation for patterned fabric defect inspection when only limited negative samples are available. Fisher criterion is introduced into the loss function of deep learning, which can guide the learning direction of deep networks and make the extracted features more discriminating. A deep neural network constructed from the encoder part of trained autoencoders is utilized to classify each pixel in the images into defective or defectless categories, using as context a patch centered on the pixel. Sequentially the confidence map is processed by median filtering and binary thresholding, and then the defect areas are located. Experimental results demonstrate that our method achieves state-of-the-art performance on the benchmark fabric images.

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“…The main methods used for inspecting fabric defects with image analysis so far can be divided into two categories, those are, frequency-domain analysis methods and time-domain analysis methods [1,2]. Many methods have been proven effective for detecting the grey fabric defect, including grey level cooccurrence matrix, Fourier transform [3], Gabor transform [4,5] and wavelet transform [6], but they are not available for inspecting the yarn-dyed fabric defect.…”

Section: Introductionmentioning

confidence: 99%

Yarn-Dyed Fabric Defect Detection Based On Autocorrelation Function And GLCM

Zhu

Pan

Gao

et al. 2015

Autex Research Journal

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In this study, a new detection algorithm for yarn-dyed fabric defect based on autocorrelation function and grey level co-occurrence matrix (GLCM) is put forward. First, autocorrelation function is used to determine the pattern period of yarn-dyed fabric and according to this, the size of detection window can be obtained. Second, GLCMs are calculated with the specified parameters to characterise the original image. Third, Euclidean distances of GLCMs between being detected images and template image, which is selected from the defect-free fabric, are computed and then the threshold value is given to realise the defect detection. Experimental results show that the algorithm proposed in this study can achieve accurate detection of common defects of yarn-dyed fabric, such as the wrong weft, weft crackiness, stretched warp, oil stain and holes.

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Automated fabric defect detection—A review

Cited by 431 publications

References 118 publications

A Mechanism for the Early Detection and Management of Physical Asset Management Strategy Execution Failure

A Mechanism for the Early Detection and Management of Physical Asset Management Strategy Execution Failure

Combining Fisher Criterion and Deep Learning for Patterned Fabric Defect Inspection

Yarn-Dyed Fabric Defect Detection Based On Autocorrelation Function And GLCM

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