Search results

1 – 10 of 155
Article
Publication date: 20 January 2021

Xueqing Zhao, Min Zhang and Junjun Zhang

Classifying the types of fabric defects in the textile industry requires a way to effectively detect. The traditional textile fabric defects detection method is human eyes, which…

Abstract

Purpose

Classifying the types of fabric defects in the textile industry requires a way to effectively detect. The traditional textile fabric defects detection method is human eyes, which performs very low efficiency and high cost. Therefore, how to improve the classification accuracy of textile fabric defects by using current artificial intelligence and to better meet the needs in the textile industry, the purpose of this article is to develop a method to improve the accuracy of textile fabric defects classification.

Design/methodology/approach

To improve the accuracy of textile fabric defects classification, an ensemble learning-based convolutional neural network (CNN) method in terms of textile fabric defects classification (short for ECTFDC) on an enhanced TILDA database is used. ECTFDC first adopts ensemble learning-based model to classify five types of fabric defects from TILDA. Subsequently, ECTFDC extracts features of fabric defects via an ensemble multiple convolutional neural network model and obtains parameters by using transfer learning method.

Findings

The authors applied ECTFDC on an enhanced TILDA database to improve the robustness and generalization ability of the proposed networks. Experimental results show that ECTFDC outperforms the other networks, the precision and recall rates are 97.8%, 97.68%, respectively.

Originality/value

The ensemble convolutional neural network textile fabric defect classification method in this paper can quickly and effectively classify textile fabric defect categories; it can reduce the production cost of textiles and it can alleviate the visual fatigue of inspectors working for a long time.

Details

International Journal of Clothing Science and Technology, vol. 33 no. 4
Type: Research Article
ISSN: 0955-6222

Keywords

Article
Publication date: 11 October 2022

Shi Zhou, Jia Zhao, Yi Shan Shi, Yi Fan Wang and Shun Qi Mei

In the fabric manufacturing industry, various unfavorable factors, including machine fault and yarn breakage, can easily cause fabric defects and affect product quality, begetting…

Abstract

Purpose

In the fabric manufacturing industry, various unfavorable factors, including machine fault and yarn breakage, can easily cause fabric defects and affect product quality, begetting huge economic losses to enterprises. Thus, automatic fabric defect detection systems have become an important development direction. Herein, the most common defects in the fabric production process, like ribbon yarn, broken yarn, cotton ball, holes, yarn shedding and stains, are detected. Current fabric defect detection systems afford low detection accuracy and a high missed detection rate for small target fabric defects. Therefore, this study proposes deep learning technology for automatically detecting fabric defects by improving the YOLOv5s target detection algorithm. The improved algorithm is termed YOLOv5s-4SCK, which can effectively detect fabric defects. This study aims to discuss the aforementioned issues.

Design/methodology/approach

Specifically, based on the YOLOv5s algorithm, first, the structure of YOLOv5s is modified to add a small target detection layer, fully utilize deep and shallow features and reduce the missed detection rate of small target fabric defects. Second, the integration of CARAFE upsampling enables the effective retention of feature information and maintenance of a certain computational efficiency, thereby improving the detection accuracy. Finally, the K-Means++ clustering algorithm is used to analyze the position of the center point of the prior box to better obtain the anchor box and improve the average accuracy and evaluation index of detection.

Findings

The research results show that the YOLOv5s-4SCK algorithm increases the accuracy by 4.1% and the detection speed by 2 f.s-1 compared to the original YOLOv5s algorithm, and it effectively improves the original YOLOv5s problem of high missed detection rate of small targets.

Research limitations/implications

The YOLOv5s-4SCK proposed in this paper can effectively reduce the missed detection rate of fabric defects, improve the detection efficiency and has certain industrial value.

Practical implications

The proposed algorithm can quickly identify fabric defects, effectively improving the detection rate. In the future, the proposed algorithm will be applied in the actual industry.

Social implications

Automatic fabric defect detection reduces the manpower of inspectors, and the proposed YOLOv5s-4SCK algorithm is also suitable for other recognition fields.

Originality/value

The proposed YOLOv5s-4SCK algorithm has been tested using real cloth to ensure its accuracy, and its performance is better than the original YOLOv5s algorithm.

Details

International Journal of Clothing Science and Technology, vol. 35 no. 1
Type: Research Article
ISSN: 0955-6222

Keywords

Article
Publication date: 3 May 2019

Pandia Rajan Jeyaraj and Edward Rajan Samuel Nadar

The purpose of this paper is to focus on the design and development of computer-aided fabric defect detection and classification employing advanced learning algorithm.

1144

Abstract

Purpose

The purpose of this paper is to focus on the design and development of computer-aided fabric defect detection and classification employing advanced learning algorithm.

Design/methodology/approach

To make a fast and effective classification of fabric defect, the authors have considered a characteristic of texture, namely its colour. A deep convolutional neural network is formed to learn from the training phase of various defect data sets. In the testing phase, the authors have utilised a learning feature for defect classification.

Findings

The improvement in the defect classification accuracy has been achieved by employing deep learning algorithm. The authors have tested the defect classification accuracy on six different fabric materials and have obtained an average accuracy of 96.55 per cent with 96.4 per cent sensitivity and 0.94 success rate.

Practical implications

The authors had evaluated the method by using 20 different data sets collected from different raw fabrics. Also, the authors have tested the algorithm in standard data set provided by Ministry of Textile. In the testing task, the authors have obtained an average accuracy of 94.85 per cent, with six defects being successfully recognised by the proposed algorithm.

Originality/value

The quantitative value of performance index shows the effectiveness of developed classification algorithm. Moreover, the computational time for different fabric processing was presented to verify the computational range of proposed algorithm with the conventional fabric processing techniques. Hence, this proposed computer vision-based fabric defects detection system is used for an accurate defect detection and computer-aided analysis system.

Details

International Journal of Clothing Science and Technology, vol. 31 no. 4
Type: Research Article
ISSN: 0955-6222

Keywords

Article
Publication date: 17 October 2022

Subhasis Das and Anindya Ghosh

In recent years, rough set theory has evolved as one of the most promising classification techniques. One of the cardinal uses of rough set theory is its application for rule…

Abstract

Purpose

In recent years, rough set theory has evolved as one of the most promising classification techniques. One of the cardinal uses of rough set theory is its application for rule generation. The purpose of this paper is to propose a real-time fabric inspection technique. This work deals with the multi-class classification of fabric defects using rough set theory.

Design/methodology/approach

This technique focuses on the classification of fabric defects using the effective decision rules envisaged by rough set theory. In the proposed work, the six features of 50 images have been used for multiclass classification of fabric defects.

Findings

In this work, 40 images were used for generation of decision rules and 10 unseen images were used for validation out of which nine images are accurately predicted by the proposed technique.

Originality/value

The proposed method accurately identified 9 out of 10 testing defects. The obtained decision rules provide an insight about the classification method which ensures that the prediction accuracy can be improved further by framing more robust decision rules with the help of a large training data set. Thus, with the support of modern computational systems this method is potent in getting recognition from the textile industry as a real-time classification technique.

Details

Research Journal of Textile and Apparel, vol. 27 no. 3
Type: Research Article
ISSN: 1560-6074

Keywords

Article
Publication date: 1 February 2015

S.N. Niles, S. Fernando and W.D.G. Lanerolle

Inspection of fabrics is a major consideration in fabric manufacture, as well as in manufacture of garments and other fabric-based goods. In this research, a computer-based system…

195

Abstract

Inspection of fabrics is a major consideration in fabric manufacture, as well as in manufacture of garments and other fabric-based goods. In this research, a computer-based system for objective assessment of fabric defects was designed with emphasis placed on fabric defects occurring in the Sri Lankan industry. Image processing techniques were used to analyse scanned images of the test fabric, compare it with an ideal sample, and identify defects according to pre-learnt rules. The information gathered was then used to grade the fabric, either by determining the frequency of defect occurrence or assigning points.

A new classification method for common defects was designed, thereby facilitating grading according to commonly used grading systems. A coding system for defects was also designed to help report defects to the user. The fabric defects were classified and stored according to the developed classification method and coding system.

Details

Research Journal of Textile and Apparel, vol. 19 no. 1
Type: Research Article
ISSN: 1560-6074

Keywords

Article
Publication date: 14 June 2011

A. Ghosh, T. Guha, R.B. Bhar and S. Das

The purpose of this paper is to address a solution to the problem of defect recognition from images using the support vector machines (SVM).

Abstract

Purpose

The purpose of this paper is to address a solution to the problem of defect recognition from images using the support vector machines (SVM).

Design/methodology/approach

A SVM‐based multi‐class pattern recognition system has been developed for inspecting commonly occurring fabric defects such as neps, broken ends, broken picks and oil stain. A one‐leave‐out cross validation technique is applied to assess the accuracy of the SVM classifier in classifying fabric defects.

Findings

The investigation indicates that the fabric defects can be classified with a reasonably high degree of accuracy by the proposed method.

Originality/value

The paper outlines the theory and application of SVM classifier with reference to pattern classification problem in textiles. The SVM classifier outperforms the other techniques of machine learning systems such as artificial neural network in terms of efficiency of calculation. Therefore, SVM classifier has great potential for automatic inspection of fabric defects in industry.

Details

International Journal of Clothing Science and Technology, vol. 23 no. 2/3
Type: Research Article
ISSN: 0955-6222

Keywords

Article
Publication date: 27 May 2014

Zhijie Wen, Junjie Cao, Xiuping Liu and Shihui Ying

Fabric defects detection is vital in the automation of textile industry. The purpose of this paper is to develop and implement a new fabric defects detection method based on…

Abstract

Purpose

Fabric defects detection is vital in the automation of textile industry. The purpose of this paper is to develop and implement a new fabric defects detection method based on adaptive wavelet.

Design/methodology/approach

Fabric defects can be regarded as the abrupt features of textile images with uniform background textures. Wavelets have compact support and can represent these textures. When there is an abrupt feature existed, the response is totally different with the response of the background textures, so wavelets can detect these abrupt features. This method designs the appropriate wavelet bases for different fabric images adaptively. The defects can be detected accurately.

Findings

The proposed method achieves accurate detection of fabric defects. The experimental results suggest that the approach is effective.

Originality/value

This paper develops an appropriate method to design wavelet filter coefficients for detecting fabric defects, which is called adaptive wavelet. And it is helpful to realize the automation of textile industry.

Details

International Journal of Clothing Science and Technology, vol. 26 no. 3
Type: Research Article
ISSN: 0955-6222

Keywords

Article
Publication date: 13 May 2020

Zhijie Wen, Qikun Zhao and Lining Tong

The purpose of this paper is to present a novel method for minor fabric defects detection.

Abstract

Purpose

The purpose of this paper is to present a novel method for minor fabric defects detection.

Design/methodology/approach

This paper proposes a PETM-CNN algorithm. PETM-CNN is designed based on self-similar estimation algorithm and Convolutional Neural Network. The PE (Patches Extractor) algorithm extracts patches that are possible to be defective patches to preprocess the fabric image. Then a TM-CNN (Triplet Metric CNN) method is designed to predict labels of the patches and the final label of the image. The TM-CNN can perform better than normal CNN.

Findings

This algorithm is superior to other algorithms on the data set of fabric images with minor defects. The proposed method achieves accurate classification of fabric images whether it has minor defects or not. The experimental results show that the approach is effective.

Originality/value

Traditional fabric defects detection is not effective as minor defects detection, so this paper develops a method of minor fabric images classification based on self-similar estimation and CNN. This paper offers the first investigation of minor fabric defects.

Details

International Journal of Clothing Science and Technology, vol. 33 no. 1
Type: Research Article
ISSN: 0955-6222

Keywords

Article
Publication date: 1 December 2004

George K. Stylios

Examines the tenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects…

3517

Abstract

Examines the tenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

Details

International Journal of Clothing Science and Technology, vol. 16 no. 6
Type: Research Article
ISSN: 0955-6222

Keywords

Article
Publication date: 6 March 2019

Xueqing Zhao, Xin Shi, Kaixuan Liu and Yongmei Deng

The quality of produced textile fibers plays a very important role in the textile industry, and detection and assessment schemes are the key problems. Therefore, the purpose of…

Abstract

Purpose

The quality of produced textile fibers plays a very important role in the textile industry, and detection and assessment schemes are the key problems. Therefore, the purpose of this paper is to propose a relatively simple and effective technique to detect and assess the quality of produced textile fibers.

Design/methodology/approach

In order to achieve automatic visual inspection of fabric defects, first, images of the textile fabric are pre-processed by using Block-Matching and 3-D (BM3D) filtering. And then, features of textile fibers image are respectively extracted, including color, texture and frequency spectrum features. The color features are extracted by using hue–saturation–intensity model, which is more consistent with the human vision perception model; texture features are extracted by using scale-invariant feature transform scheme, which is a quite good method to detect and describe the local image features, and the obtained features are robust to local geometric distortion; frequency spectrum features of textiles are less sensitive to noise and intensity variations than spatial features. Finally, for evaluating the quality of the fabric in real time, two quantitatively metric parameters, peak signal-to-noise ratio and structural similarity, are used to objectively assess the quality of textile fabric image.

Findings

Compared to the quality between production and pre-processing of textile fiber images, the BM3D filtering method is a very efficient technology to improve the quality of textile fiber images. Compared to the different features of textile fibers, like color, texture and frequency spectrum, the proposed detection and assessment method based on textile fabric image feature can easily detect and assess the quality of textiles. Moreover, the objective metrics can further improve the intelligence and performance of detection and assessment schemes, and it is very simple to detect and assess the quality of textiles in the textile industry.

Originality/value

An intelligent detection and assessment method based on textile fabric image feature is proposed, which can efficiently detect and assess the quality of textiles, thereby improving the efficiency of textile production lines.

Details

International Journal of Clothing Science and Technology, vol. 31 no. 3
Type: Research Article
ISSN: 0955-6222

Keywords

1 – 10 of 155