Soft computing applied to the build of textile defects inspection system

Darwish, Saad M.

doi:10.1049/iet-cvi.2012.0125

Cited by 16 publications

(6 citation statements)

References 22 publications

(50 reference statements)

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“…Siddiqui and Sun 14 and Ng et al [15][16][17] proposed a defect learning method based on the convolution neural network model for yarn fabric to process defects occurring during the production process. In this method they used a golden subtraction method for internal decomposed fabric.…”

Section: Literature Reviewmentioning

confidence: 99%

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%

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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“…Several studies depicted the employment of soft computing and particularly of fuzzy logic in various fields of textile machines [50]- [53]. However, the use of fuzzy logic in the condition-based maintenance of textile machines is by far less investigated.…”

Section: Maintenance Decision-making Support For Textile Machines: a Fuzzy Logic Approachmentioning

confidence: 99%

Maintenance Decision-Making Support for Textile Machines: A Knowledge-Based Approach Using Fuzzy Logic and Vibration Monitoring

2019

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A condition-based maintenance approach may be used for planning the maintenance activities of textile machines with a satisfactory performance by developing maintenance decision-making support based on fuzzy logic and vibration monitoring. Since textile machines are systems with moving parts operating at relatively high-speed, vibration monitoring was used to indicate their failure development. At the same time, the characterization of the degradation phenomenon of textile machines involves some degree of uncertainty and vagueness. Within this context, a knowledge-based approach that employed fuzzy logic and vibration monitoring was developed. Deterioration symptoms do announce future failures of industrial machines, therefore building a maintenance decision-making support for scheduling maintenance actions of textile machines based on the estimation of their condition becomes a resourceful way to prevent their further deterioration.

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“…To this end metaheuristics are integrated with artificial neural networks or fuzzy inference systems. Darwis [13] proposed a novel interval type-2 fuzzy system for resolving defects recognition problem of textile industries. In the proposed system, swarm optimization algorithm is integrated with interval type-2 fuzzy reasoning in order to enhance the defects classification capabilities.…”

Section: Quality Controlmentioning

confidence: 99%

A Literature Survey on Metaheuristics in Production Systems

Onar

Öztayşi

Kahraman

et al. 2016

Operations Research/Computer Science Interfaces Series

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Typical problems in production systems are suboptimal production scheduling, long manufacturing lead times, inefficient inventory control, low work center utilization, etc. The solutions of these problems may need complex techniques while the classical techniques are insufficient to solve them. In this chapter we first classified the problems which may be faced in production systems and then the solution techniques called metaheuristics.

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Soft computing applied to the build of textile defects inspection system

Cited by 16 publications

References 22 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

Maintenance Decision-Making Support for Textile Machines: A Knowledge-Based Approach Using Fuzzy Logic and Vibration Monitoring

A Literature Survey on Metaheuristics in Production Systems

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