Log in

Relevant bibliographies by topics / Fabric defect detection / Journal articles

To see the other types of publications on this topic, follow the link: Fabric defect detection.

Journal articles on the topic 'Fabric defect detection'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Fabric defect detection.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Abou-Taleb, Hemdan A., and Aya Tallah M. Sallam. "ON-LINE FABRIC DEFECT DETECTION AND FULL CONTROL IN A CIRCULAR KNITTING MACHINE." AUTEX Research Journal 8, no. 1 (2008): 21–29. http://dx.doi.org/10.1515/aut-2008-080105.

Full text

Abstract:

Abstract This study has shown that image analysis has great potential to provide reliable measurements for detecting defects in knitted fabrics. Using the principles of image analysis, an automatic fabric evaluation system, which enables automatic computerised defect detection -(analysis of knitted fabrics) was developed. On-line fabric defect detection was tested automatically by analysing fabric images captured by a digital camera. The results of the automatic fabric defect detection correspond well with the experimental values. Therefore, it is shown that the developed image capturing and a

APA, Harvard, Vancouver, ISO, and other styles

2

Xia, Rongfei, Yifei Chen, and Yangfeng Ji. "Detection of Microdefects in Fabric with Multifarious Patterns and Colors Using Deep Convolutional Neural Network." Advances in Polymer Technology 2024 (February 12, 2024): 1–10. http://dx.doi.org/10.1155/2024/5926658.

Full text

Abstract:

Automatic detection of fabric defects is important in textile quality control, particularly in detecting fabrics with multifarious patterns and colors. This study proposes a fabric defect detection system for fabrics with complex patterns and colors. The proposed system comprises five convolutional layers designed to extract features from the original images effectively. In addition, three fully connected layers are designed to classify the fabric defects into four categories. Using this system, the detection accuracy is improved, and the depth of the model is shortened simultaneously. Optimal

APA, Harvard, Vancouver, ISO, and other styles

3

HAO, Bao Ming, Hai Feng Xu, and Huan Yin Guo. "Fabric Defect Detection Based on Cross-Entropy." Advanced Materials Research 760-762 (September 2013): 1233–36. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1233.

Full text

Abstract:

The core of fabric defects detection is the collection and processing of fabrics image. A scheme for fabric defect detection based on cross-entropy is proposed in this paper.The crossentropy value illuminates the information difference between the template image and the realtime image on the average.So can take advantage of cross-entropy criteria to use for defect detection and identification. Results have confirmed the usefulness of this scheme for fabric defect detection.

APA, Harvard, Vancouver, ISO, and other styles

4

Peng, Peiran, Ying Wang, Can Hao, Zhizhong Zhu, Tong Liu, and Weihu Zhou. "Automatic Fabric Defect Detection Method Using PRAN-Net." Applied Sciences 10, no. 23 (2020): 8434. http://dx.doi.org/10.3390/app10238434.

Full text

Abstract:

Fabric defect detection is very important in the textile quality process. Current deep learning algorithms are not effective in detecting tiny and extreme aspect ratio fabric defects. In this paper, we proposed a strong detection method, Priori Anchor Convolutional Neural Network (PRAN-Net), for fabric defect detection to improve the detection and location accuracy of fabric defects and decrease the inspection time. First, we used Feature Pyramid Network (FPN) by selected multi-scale feature maps to reserve more detailed information of tiny defects. Secondly, we proposed a trick to generate sp

APA, Harvard, Vancouver, ISO, and other styles

5

Sun, Qin, Bernd Noche, Zongyi Xie, and Bingqiang Huang. "Research on Seamless Fabric Defect Detection Based on Improved YOLOv8n." Applied Sciences 15, no. 5 (2025): 2728. https://doi.org/10.3390/app15052728.

Full text

Abstract:

An improved YOLOv8n seamless fabric defect detection model is proposed to solve the current issues in seamless fabric defects in factories in this paper. The improvement in this paper first introduces the SPPF_LSKA module, which not only optimizes the extraction of multi-scale features but also enhances the adaptability of the model in detecting defects of different sizes by improving the feature fusion mechanism, enabling efficient recognition of both large-sized and small-sized defects. Secondly, the CARAFE upsampling method is used to adaptively learn the relationship between pixels, which

APA, Harvard, Vancouver, ISO, and other styles

6

Li, Long, Qi Li, Zhiyuan Liu, and Lin Xue. "Effective Fabric Defect Detection Model for High-Resolution Images." Applied Sciences 13, no. 18 (2023): 10500. http://dx.doi.org/10.3390/app131810500.

Full text

Abstract:

The generation of defects during fabric production impacts fabric quality, and fabric production factories can greatly benefit from the automatic detection of fabric defects. Although object detection algorithms based on convolutional neural networks can be quickly developed, fabric defect detection encounters several problems. Accordingly, a fabric defect detection model based on Cascade R-CNN was proposed in this study. We also proposed block recognition and detection box merging algorithms to achieve complete defect detection in high-resolution images. We implemented a Switchable Atrous Con

APA, Harvard, Vancouver, ISO, and other styles

7

Harshil, Sharma, and al et. "Enhanced YOLOv3 Model for Automated Fabric Defect Detection." International Journal of Human Computations and Intelligence 4, no. 3 (2025): 487–99. https://doi.org/10.5281/zenodo.15280482.

Full text

Abstract:

The manufacture of high-quality fabrics depends on an accurate and efficient fault detection system that can analyze data in real-time. A sophisticated defect detection system based on an enhanced YOLOv3 architecture is presented in this work to improve detection accuracy and reduce false identifications. Using a hybrid technique that combines defect size analysis and k-means clustering, the proposed approach optimizes the number and size of anchor boxes for fabric imperfections, introducing two crucial breakthroughs. Second, a multi-scale feature improvement strategy is used, combining high-l

APA, Harvard, Vancouver, ISO, and other styles

8

Jing, Junfeng, Panpan Yang, and Pengfei Li. "Defect Detection on Patterned Fabrics Using Distance Matching Function and Regular Band." Journal of Engineered Fibers and Fabrics 10, no. 2 (2015): 155892501501000. http://dx.doi.org/10.1177/155892501501000210.

Full text

Abstract:

In this paper, regular band is presented to detect defects on patterned fabrics. Patterned fabrics are firstly disposed by fabric average to form object images mixed with positive and negative pixels in this proposed method. Distance matching function is computed to determine the periodic distance of patterned fabrics. The obtained periodic distance would be the length and width of regular band on row and column. Two features are calculated with regular band. The threshold of defect segmentation is extracted from the training step. Two features of regular band negotiating the threshold are con

APA, Harvard, Vancouver, ISO, and other styles

9

Nasim, Mariam, Rafia Mumtaz, Muneer Ahmad, and Arshad Ali. "Fabric Defect Detection in Real World Manufacturing Using Deep Learning." Information 15, no. 8 (2024): 476. http://dx.doi.org/10.3390/info15080476.

Full text

Abstract:

Defect detection is very important for guaranteeing the quality and pricing of fabric. A considerable amount of fabric is discarded as waste because of defects, leading to substantial annual losses. While manual inspection has traditionally been the norm for detection, adopting an automatic defect detection scheme based on a deep learning model offers a timely and efficient solution for assessing fabric quality. In real-time manufacturing scenarios, datasets lack high-quality, precisely positioned images. Moreover, both plain and printed fabrics are being manufactured in industries simultaneou

APA, Harvard, Vancouver, ISO, and other styles

10

Carrilho, Rui, Kailash A. Hambarde, and Hugo Proença. "A Novel Dataset for Fabric Defect Detection: Bridging Gaps in Anomaly Detection." Applied Sciences 14, no. 12 (2024): 5298. http://dx.doi.org/10.3390/app14125298.

Full text

Abstract:

Detecting anomalies in texture has become a significant concern across various industrial processes. One prevalent application of this is in inspecting patterned textures, especially in the domain of fabric defect detection, which is a commonly encountered scenario. This task entails dealing with a wide array of colours and textile varieties, spanning a broad spectrum of fabrics. Due to the extensive diversity in colours, textures, and defect characteristics, fabric defect detection presents a complex and formidable challenge within the realm of patterned texture inspection. While recent trend

APA, Harvard, Vancouver, ISO, and other styles

11

Wu, Ying, Ren Wang, Lin Lou, Lali Wang, and Jun Wang. "Local Characterisation and Detection of Woven Fabric Texture Based on a Sparse Dictionary." Fibres & Textiles in Eastern Europe 151, no. 3 (2022): 33–40. http://dx.doi.org/10.2478/ftee-2022-0020.

Full text

Abstract:

Abstract To achieve enhanced accuracy of fabric representation and defect detection, an innovative approach using a sparse dictionary with small patches was used for fabric texture characterisation. The effectiveness of the algorithm proposed was tested through comprehensive characterisation by studying eight weave patterns: plain, twill, weft satin, warp satin, basket, honeycomb, compound twill, and diamond twill and detecting fabric defects. Firstly, the main parameters such as dictionary size, patch size, and cardinality T were optimised, and then 40 defect-free fabric samples were characte

APA, Harvard, Vancouver, ISO, and other styles

12

Liu, Zhe. "Feature Model of Fabrics Irregular Defect." Key Engineering Materials 460-461 (January 2011): 566–68. http://dx.doi.org/10.4028/www.scientific.net/kem.460-461.566.

Full text

Abstract:

Aiming at current situation of absenting effective texture feature model for irregular defects of fabric image, this paper proposes a new feature model with “mean range” texture, which can express irregular defect of fabric better. In this paper, the definition of irregular defect is given, and rule model expressing fabric texture is established. At this basis, feature model of mean range texture is proposed, which can express irregular defects for all kinds of fabrics. After a large number of experiments, we conclude that feature with mean range is simple and effective, express fabric texture

APA, Harvard, Vancouver, ISO, and other styles

13

Bhandari, Shiva, Shashidhar Ram Joshi, and Sanjivan Satyal. "Optimized Gabor Filter Banks and Autoencoder Models for Enhanced Knitted Fabric Defect Detection." September 2024 6, no. 3 (2024): 241–62. http://dx.doi.org/10.36548/jaicn.2024.3.001.

Full text

Abstract:

There is a high need for an automated system to detect fabric defects, as the current manual methods used in the garment industries in Nepal are unreliable and costly. Previous research has focused on specific fabric defects rather than overall fabric defects efficiently. This research employs two autoencoder models to identify different defects across different types of knitted fabrics, utilizing two datasets: the SFDG dataset and a custom dataset prepared from Butwal’s garment industries. The models benefit from a carefully designed Gabor filter bank to examine fabric compositions. This filt

APA, Harvard, Vancouver, ISO, and other styles

14

Durgaprasad, Mr P. "Fabric Defect Detection Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 7581–86. http://dx.doi.org/10.22214/ijraset.2023.53502.

Full text

Abstract:

Abstract: Fabric production companies place great emphasis on quality control, recognizing its crucial role in their operations. Failure to identify defects in fabrics exposes these companies to the risk of financial losses and damage to their reputation due to the sale of flawed products. Typically, traditional inspection systems exhibit an accuracy range of 60-75%. This paper proposes a solution to reduce costs by introducing a fast and automated defect detection system, complemented by human operator decision-making. The system employs a specialized Convolutional Neural Network (CNN) for de

APA, Harvard, Vancouver, ISO, and other styles

15

Rakhimjonov, Shahzodbek E., and Akbarjon A. Umarov. "Theoretical Study of Structural Defects in Textile Fabrics." International Journal of Advance Scientific Research 5, no. 6 (2025): 47–54. https://doi.org/10.37547/ijasr-05-06-07.

Full text

Abstract:

This article broadly covers modern research directions for identifying and evaluating structural defects in textile fabrics. These defects significantly affect the quality, aesthetic appearance, and functional properties of fabrics. Therefore, detecting, diagnosing, and effectively eliminating them is one of the most pressing issues in the textile industry. The study explores advanced techniques for identifying structural defects using image processing algorithms, deep learning technologies, and neural networks. It examines the potential of a deep learning approach based on the Fisher criterio

APA, Harvard, Vancouver, ISO, and other styles

16

Kang, Zhi Qiang, Xiu Hua Shi, Qi Li, and Bin Feng. "Grid-Based Method and Wavelet Transform Fusion of Rapid Detection of Fabric Defects." Applied Mechanics and Materials 65 (June 2011): 48–51. http://dx.doi.org/10.4028/www.scientific.net/amm.65.48.

Full text

Abstract:

For rapid detection of defects, this paper selected by the grid method in the whole image defect in the area, and then use the grid template to a defect in the image area can not be narrowed down to so far, and there is only a small area defect image processing. Detection of small defects in the fabric, through the Wavelet transform and other image enhancement comparison and the fabric defect detection experiment found that wavelet transform for image enhancement characteristics of a local image enhancement processing, can achieve better detection of weak targets the effect of small fabric def

APA, Harvard, Vancouver, ISO, and other styles

17

Si, Xiao Shu, Hong Zheng, and Xue Min Hu. "Fabric Defect Detection Based on SRG-PCNN." Advanced Materials Research 148-149 (October 2010): 1319–26. http://dx.doi.org/10.4028/www.scientific.net/amr.148-149.1319.

Full text

Abstract:

Defect segmentation has been a focal point in fabric inspection research, and it remains challenging because it detects delicate features of defects complicated by variations in weave textures and changes in environmental factors. According to the different features between the normal fabric image and defect image, this paper presents an adaptive image segmentation method based on a simplified region growing pulse coupled neural network (SRG-PCNN) for detecting fabric defects. The validation tests on the developed algorithms were performed with fabric images, and results showed that SRG-PCNN i

APA, Harvard, Vancouver, ISO, and other styles

18

Li, Peng Fei, Huan Huan Zhang, Jun Feng Jing, and Jing Wang. "Fabric Defect Detection Based on Local Entropy." Advanced Materials Research 562-564 (August 2012): 1998–2001. http://dx.doi.org/10.4028/www.scientific.net/amr.562-564.1998.

Full text

Abstract:

To solve the problem of automated defect detection for textile fabrics, this paper proposed a method for fabric defect detection which is based on local entropy. The method can transform the original gray image space for the entropy space and enhance the different organization structure which is conducive to extract the damage texture region. In the experiment, divided the fabric image to the same size local window, and chosen the smallest value of local entropy window region to segment the defects. The experimental result shown that this method can avoid the whole image complex operations and

APA, Harvard, Vancouver, ISO, and other styles

19

Jin, Rui, and Qiang Niu. "Automatic Fabric Defect Detection Based on an Improved YOLOv5." Mathematical Problems in Engineering 2021 (September 30, 2021): 1–13. http://dx.doi.org/10.1155/2021/7321394.

Full text

Abstract:

Fabric defect detection is particularly remarkable because of the large textile production demand in China. Traditional manual detection method is inefficient, time-consuming, laborious, and costly. A deep learning technique is proposed in this work to perform automatic fabric defect detection by improving a YOLOv5 object detection algorithm. A teacher-student architecture is used to handle the shortage of fabric defect images. Specifically, a deep teacher network could precisely recognize fabric defects. After information distillation, a shallow student network could do the same thing in real

APA, Harvard, Vancouver, ISO, and other styles

20

K, Sneha, Shenbaga Sowmiya V, Sivamani B, and Dhivya P. "Fabric Defect Detection Using Transfer Learning." International Journal of Research In Science & Engineering, no. 45 (September 28, 2024): 62–71. http://dx.doi.org/10.55529/ijrise.45.62.71.

Full text

Abstract:

Transfer learning in fabric defect detection involves utilizing pre-trained deep learning models on a large dataset, typically from a different domain, and fine-tuning them on a smaller dataset that is specific to fabric defects. By leveraging transfer learning, the limitations of limited annotated data for fabric defect detection can be overcome by utilizing the knowledge gained from a more extensive and diverse dataset. The pre-trained model's learned features are adjusted to recognize specific fabric defect patterns, resulting in more accurate and efficient defect detection. This approach r

APA, Harvard, Vancouver, ISO, and other styles

21

Kang, Xuejuan, and Erhu Zhang. "A universal defect detection approach for various types of fabrics based on the Elo-rating algorithm of the integral image." Textile Research Journal 89, no. 21-22 (2019): 4766–93. http://dx.doi.org/10.1177/0040517519840636.

Full text

Abstract:

In order to overcome the shortcoming that a fabric defect detection method can only fit into a certain type of fabric, this paper presents a novel method by integrating the idea of the integral image into the Elo-rating algorithm (IIER), which can detect the defects of various types of fabric speedily. Firstly, the golden sub-blocks are extracted from defect-free images. The whole images are divided into many sub-blocks, and the integral images of these sub-blocks are obtained. Next, the R sub-blocks are randomly selected from these integral sub-blocks, and each block is assigned an initial El

APA, Harvard, Vancouver, ISO, and other styles

22

Fathu Nisha, M., P. Vasuki, and S. Mohamed Mansoor Roomi. "Fabric Defect Detection Using the Sensitive Plant Segmentation Algorithm." Fibres and Textiles in Eastern Europe 28, no. 3(141) (2020): 84–87. http://dx.doi.org/10.5604/01.3001.0013.9025.

Full text

Abstract:

Fabric quality control and defect detection are playing a crucial role in the textile industry with the development of high customer demand in the fashion market. This work presents fabric defect detection using a sensitive plant segmentation algorithm (SPSA) which, is developed with the sensitive behaviour of the sensitive plant biologically named “mimosa pudica”. This method consists of two stages: The first stage enhances the contrast of the defective fabric image and the second stage segments the fabric defects with the aid of the SPSA. The SPSA proposed was developed for defective pixel i

APA, Harvard, Vancouver, ISO, and other styles

23

Xu, Guo Sheng. "Fabric Defect Automated Detection Technology." Applied Mechanics and Materials 325-326 (June 2013): 1431–34. http://dx.doi.org/10.4028/www.scientific.net/amm.325-326.1431.

Full text

Abstract:

To solve the problem of automated defect detection for textile fabrics, this paper proposed a method for fabric defect detection which is based on computer vision. After the operations in many aspects of basis image processing, such as gray-scale, denoising, contour detection and morphological, and it can transmit the fabric defect image information to host-computer with the USB interface in time. In order to acquire high processing speed, the captured images from each camera are sent into one dedicate computer for distributed and parallel image processing. The experimental result confirms tha

APA, Harvard, Vancouver, ISO, and other styles

24

Zhou, Lei, Bingya Ma, Yanyan Dong, Zhewen Yin, and Fan Lu. "DCFE-YOLO: A novel fabric defect detection method." PLOS ONE 20, no. 1 (2025): e0314525. https://doi.org/10.1371/journal.pone.0314525.

Full text

Abstract:

Accurate detection of fabric defects is crucial for quality control in the textile industry. However, the task of fabric defect detection remains highly challenging due to the complex textures and diverse defect patterns. To address the issues of inaccurate localization and false positives caused by complex textures and varying defect sizes, this paper proposes an improved YOLOv8-based fabric defect detection method. First, Dynamic Snake Convolution is introduced into the backbone network to enhance sensitivity to elongated and subtle defects, improving the extraction of edge and texture detai

APA, Harvard, Vancouver, ISO, and other styles

25

A, Sowmiya, and Karunamoorthy B. "Fabric Fault and Extra Thread Detection using Convolutional Neural Network." Journal of Soft Computing Paradigm 5, no. 2 (2023): 148–63. http://dx.doi.org/10.36548/jscp.2023.2.005.

Full text

Abstract:

A planar substance made of textile fibers is called fabric. The main reason why defective fabrics are produced is loom malfunctions. A specialized computer vision system called a fabric inspection system is used to find fabric flaws in order to ensure product quality. In this paper we classify the defect by using Convolutional Neural Network. Utilizing a special type of class-based ensemble convolutional neural network architecture, the defect recognition system is built. The experiment is carried out using several textile fiber kinds. There is four layers in CNN to classify the defect that is

APA, Harvard, Vancouver, ISO, and other styles

26

Jeyaraj, Pandia Rajan, and Edward Rajan Samuel Nadar. "Computer vision for automatic detection and classification of fabric defect employing deep learning algorithm." International Journal of Clothing Science and Technology 31, no. 4 (2019): 510–21. http://dx.doi.org/10.1108/ijcst-11-2018-0135.

Full text

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 ac

APA, Harvard, Vancouver, ISO, and other styles

27

Rahimunnisa, K. "Textile Fabric Defect Detection." Journal of Innovative Image Processing 4, no. 3 (2022): 165–72. http://dx.doi.org/10.36548/jiip.2022.3.004.

Full text

Abstract:

Technology and digital imaging have a variety of uses in automated production processes and other applicable disciplines. A novel subject of inquiry in the present era is the detection of flaws in the textile industry utilizing digital image processing methods and other learning methods. The identification of flaws in the fabric must be ensured through a quality control method. The product quality is enhanced via a mechanism for detecting fabric defects. Detection of fabric flaw becoming more and more popular in the production of high-quality textile products. This system works by using image

APA, Harvard, Vancouver, ISO, and other styles

28

Gnanaprakash V, Et al. "Novel MobileNet based Multipath Convolutional Neural Network for defect detection in fabrics." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 2417–23. http://dx.doi.org/10.17762/ijritcc.v11i9.9308.

Full text

Abstract:

Automatic fabric defect detection and classification is the most important process in the textile industry to ensure the fabric quality. In the existing systems, a learning based method is used for detecting defects in plain weave fabrics. In this paper, a novel MobileNet based Multipath Convolutional Neural Network (MMPCNN) architecture is proposed for detection and classification of simple and complex patterned fabric defects. In the proposed MMPCNN architecture, MobileNet model is used in the first path. In this, Gabor filter bank is used instead of conventional filters in the first convolu

APA, Harvard, Vancouver, ISO, and other styles

29

Rui, Jin, and Niu Qiang. "Research on textile defects detection based on improved generative adversarial network." Journal of Engineered Fibers and Fabrics 17 (January 2022): 155892502211013. http://dx.doi.org/10.1177/15589250221101382.

Full text

Abstract:

Fabric defects seriously affect the textile industry in China. Given that traditional manual detection methods have low efficiency and poor accuracy, using automatic textile defect detection methods is urgently needed. A fabric defect detection method based on an improved generative adversarial network is thus developed to address the shortage of fabric defect samples. This method learns to reconstruct the fabric image in an unsupervised manner and locates the defect areas based on the differences between the original image and the reconstruction. Afterward, the defect-related features are ext

APA, Harvard, Vancouver, ISO, and other styles

30

Islam, Md Imranul, and A. K. M. Mobarok Hossain. "Exploring weft knit fabric defects based on their presence and quality impact: A case study." Communications in Development and Assembling of Textile Products 1, no. 1 (2020): 57–64. http://dx.doi.org/10.25367/cdatp.2020.1.p57-64.

Full text

Abstract:

    While addressing grey fabric quality in a renowned circular weft knitting mill of Bangladesh, the authors experienced some questionable approach practiced by knitters. The subjective nature of defect detection by knitters/inspectors often time causes wrong emphasizing on frequently occurring defect(s) instead of focusing on influential defect(s) and subsequently, employing wrong quality control approach to minimize the grey fabric defects. Knit fabric defects (e.g., hole, stain, press-off, gout, miss knit, barrè, tucking, etc.) should be assessed by type, fault cove

APA, Harvard, Vancouver, ISO, and other styles

31

Nam, Eun Su, Choong Kwon Lee, and Yun Sung Choi. "A Study on the Defect Detection of Fabrics using Deep Learning." Korean Institute of Smart Media 11, no. 11 (2022): 92–98. http://dx.doi.org/10.30693/smj.2022.11.11.92.

Full text

Abstract:

Identifying defects in textiles is a key procedure for quality control. This study attempted to create a model that detects defects by analyzing the images of the fabrics. The models used in the study were deep learning-based VGGNet and ResNet, and the defect detection performance of the two models was compared and evaluated. The accuracy of the VGGNet and the ResNet model was 0.859 and 0.893, respectively, which showed the higher accuracy of the ResNet. In addition, the region of attention of the model was derived by using the Grad-CAM algorithm, an eXplainable Artificial Intelligence (XAI) t

APA, Harvard, Vancouver, ISO, and other styles

32

Zhang, Yuming, Zhongyuan Gao, Chao Zhi, et al. "A novel defect generation model based on two-stage GAN." e-Polymers 22, no. 1 (2022): 793–802. http://dx.doi.org/10.1515/epoly-2022-0071.

Full text

Abstract:

Abstract The fabric defect models based on deep learning often demand numerous training samples to achieve high accuracy. However, obtaining a complete dataset containing all possible fabric textures and defects is a big challenge due to the sophisticated and various fabric textures and defect forms. This study created a two-stage deep pix2pixGAN network called Dual Deep pix2pixGAN Network (DPGAN) to address the above problem. The defect generation model was trained based on the DPGAN network to automatically “transfer” defects from defected fabric images to clean, defect-free fabric images, t

APA, Harvard, Vancouver, ISO, and other styles

33

Sandhya, NC, Nihal Mathew Sashikumar, M. Priyanka, Sebastian Maria Wenisch, and Kunaraj Kumarasamy. "Automated Fabric Defect Detection and Classification: A Deep Learning Approach." Textile & Leather Review 4 (December 14, 2021): 315–35. http://dx.doi.org/10.31881/tlr.2021.24.

Full text

Abstract:

A computer-based intelligent visual inspection system plays a major role in evaluating the quality of textile fabrics and its demand is continuously increasing in the textile industry, especially when the quality of textile is to be considered. In this paper, we propose an AI-based automated fabric defect detection algorithm which utilizes pre-trained deep neural network models for classifying possible fabric defects. The fabric images are enhanced by pre-processing at various levels using conventional image processing techniques and they are used to train the networks. The Deep Convolutional

APA, Harvard, Vancouver, ISO, and other styles

34

Aafaf, Beljadid, Tannouche Adil, and Balouki Abdessamad. "Automatic fabric defect detection employing deep learning." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 4 (2022): 4129–36. https://doi.org/10.11591/ijece.v12i4.pp4129-4136.

Full text

Abstract:

A major issue for fabric quality inspection is in the detection of defaults, it has become an extremely challenging goal for the textile industry to minimize costs in both production and quality inspection. The quality inspection is currently done manually by professionals; hence the need for the implementation of a fast, powerful, robust, and intelligent machine vision system in order to achieve high global quality, uniformity, and consistency of fabrics and to increase productivity. Consequently, the automatic inspection control process can improve productivity and enhance product quality. T

APA, Harvard, Vancouver, ISO, and other styles

35

Wei, Bing, Lei Gao, Xue-song Tang, and Kuangrong Hao. "Multi-Class Object Learning with Application to Fabric Defects Detection." AATCC Journal of Research 8, no. 1_suppl (2021): 165–72. http://dx.doi.org/10.14504/ajr.8.s1.20.

Full text

Abstract:

Deep convolutional neural networks (CNNs) have shown great success in single-class fabric image detection. However, real-world fabric defect images generally contain several types of defects in one image. Accurately recognizing and classifying multi-class fabric defect images is still an unsolved issue due to the complexity of intersected defects, as well as difficulty in distinguishing small-size defects. To address these challenges, this study develops a methodology based on the deep learning feature pyramid networks (FPN) approach to detect multi-class fabric defects. To evaluate the propos

APA, Harvard, Vancouver, ISO, and other styles

36

Guo, Yongbin, Xinjian Kang, Junfeng Li, and Yuanxun Yang. "Automatic Fabric Defect Detection Method Using AC-YOLOv5." Electronics 12, no. 13 (2023): 2950. http://dx.doi.org/10.3390/electronics12132950.

Full text

Abstract:

In the face of detection problems posed by complex textile texture backgrounds, different sizes, and different types of defects, commonly used object detection networks have limitations in handling target sizes. Furthermore, their stability and anti-jamming capabilities are relatively weak. Therefore, when the target types are more diverse, false detections or missed detections are likely to occur. In order to meet the stringent requirements of textile defect detection, we propose a novel AC-YOLOv5-based textile defect detection method. This method fully considers the optical properties, textu

APA, Harvard, Vancouver, ISO, and other styles

37

Li, Wen Yu, Long Di Cheng, and Wen Liang Xue. "Automatic Defect Detection of Yarn-Dyed Fabrics Based on Energy Fusion and Local Binary Patterns." Advanced Materials Research 472-475 (February 2012): 3039–42. http://dx.doi.org/10.4028/www.scientific.net/amr.472-475.3039.

Full text

Abstract:

For the purpose of realizing fast and effective detection of defects of yarn-dyed fabric, and in consideration of the inherent characteristics of texture, i.e., color and structure, an approach for automatic defect detection is proposed in this paper. The image of yarn-dyed fabric to be enhanced is first converted from RGB true color space to L*a*b* color space. Then Log-gabor filters filter chromatic and brightness channels, and energy feature images are acquired after energy is fused between chromatic and brightness. Finally defects of yarn-dyed fabrics can be detected on the energy feature

APA, Harvard, Vancouver, ISO, and other styles

38

Cao, Junjie, Nannan Wang, Jie Zhang, Zhijie Wen, Bo Li, and Xiuping Liu. "Detection of varied defects in diverse fabric images via modified RPCA with noise term and defect prior." International Journal of Clothing Science and Technology 28, no. 4 (2016): 516–29. http://dx.doi.org/10.1108/ijcst-10-2015-0117.

Full text

Abstract:

Purpose – The purpose of this paper is to present a novel method for fabric defect detection. Design/methodology/approach – The method based on joint low-rank and spare matrix recovery, since patterned fabric is manufactured by a set of predefined symmetry rules, and it can be seen as the superposition of sparse defective regions and low-rank defect-free regions. A robust principal component analysis model with a noise term is designed to handle fabric images with diverse patterns robustly. The authors also estimate a defect prior and use it to guide the matrix recovery process for accurate ex

APA, Harvard, Vancouver, ISO, and other styles

39

Beljadid, Aafaf, Adil Tannouche, and Abdessamad Balouki. "Automatic fabric defect detection employing deep learning." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 4 (2022): 4129. http://dx.doi.org/10.11591/ijece.v12i4.pp4129-4136.

Full text

Abstract:

A major issue for fabric quality inspection is in the detection of defaults, it has become an extremely challenging goal for the textile industry to minimize costs in both production and quality inspection. The quality inspection is currently done manually by professionals; hence the need for the implementation of a fast, powerful, robust, and intelligent machine vision system in order to achieve high global quality, uniformity, and consistency of fabrics and to increase productivity. Consequently, the automatic inspection control process can improve productivity and enhance product quality. T

APA, Harvard, Vancouver, ISO, and other styles

40

Fan, Junjun, Wai Keung Wong, Jiajun Wen, Can Gao, Dongmei Mo, and Zhihui Lai. "Fabric Defect Detection Using Deep Convolution Neural Network." AATCC Journal of Research 8, no. 1_suppl (2021): 143–50. http://dx.doi.org/10.14504/ajr.8.s1.18.

Full text

Abstract:

Fabric defect detection plays an increasingly important role in the industrial automation application for fabric production, but how to detect defects rapidly and accurately is still challenging. In this study, we propose a powerful fabric defect detection method using a hybrid of convolutional neural network (CNN) and variational autoencoder (VAE). The convolutional layers are used for extracting fabric image pattern features and the variational autoencoder is used for modeling the latent characteristics and inferring a reconstruction. The defect positions can be detected by the differences b

APA, Harvard, Vancouver, ISO, and other styles

41

Choi, Youngwoon, Hyunseok Lee, and Sang Won Lee. "Defect Data Augmentation Method for Robust Image-based Product Inspection." PHM Society European Conference 8, no. 1 (2024): 8. http://dx.doi.org/10.36001/phme.2024.v8i1.4068.

Full text

Abstract:

In this paper, we develop a model for detecting defects in fabric products based on an object segmentation algorithm, including a novel image data augmentation method to enhance the robustness. First, a vision-based inspection system is established to collect image data of the fabric products. The three types of fabric defects, such as a hole, a stain, and a dyeing defect, are considered. To enhance defect detection accuracy and robustness, a novel image data augmentation method, referred to as the defect-area cut-mix, is proposed. In this method, the shapes that are the same as each defect ar

APA, Harvard, Vancouver, ISO, and other styles

42

Kulkarni, Sneha, Kajal Jojare, Vaishnavi Bhosale, and Priyanka Arude. "Textile Fabric Defect Detection." IJARCCE 5, no. 12 (2016): 476–78. http://dx.doi.org/10.17148/ijarcce.2016.512108.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

NISHA, M. FATHU, L. MALLIGA, S. MANTHANDI PERIANNASAMY, J. JOHN BENNET, and S. AMALORPAVA MARY RAJEE. "Smart fabric inspection using Mimosa pudica plant." Industria Textila 74, no. 02 (2023): 163–68. http://dx.doi.org/10.35530/it.074.02.1719.

Full text

Abstract:

Fabric quality governing and defect detection are playing a crucial role in the textile industry with the development of high customer demand in the fashion market. This work presents fabric defect detection using the sensitive plant segmentation algorithm (SPSA) which, is developed with the sensitive behaviour of the plant biologically named “Mimosa pudica”i. This method consists of two stages. The first stage enhances the contrast of the defective fabric image and the second stage segments the fabric defects with aid of SPSA. The proposed work SPSA is developed for defective pixels identific

APA, Harvard, Vancouver, ISO, and other styles

44

Jun, Xiang, Jingan Wang, Jian Zhou, Shuo Meng, Ruru Pan, and Weidong Gao. "Fabric defect detection based on a deep convolutional neural network using a two-stage strategy." Textile Research Journal 91, no. 1-2 (2020): 130–42. http://dx.doi.org/10.1177/0040517520935984.

Full text

Abstract:

With the rise of labor costs and the advancement of automation in the textile industry, fabric defect detection has become a hot research field in recent years. We proposed a learning-based framework for automatic detection of fabric defects. Firstly, we use a fixed-size square slider to crop the original image to a certain step and regularity. Then an improved histogram equalization is used to enhance each cropped image. Furthermore, the Inception-V1 model is employed to predict the existence of defects in the local area. Finally, we apply the LeNet-5 model, which plays the role of a voting m

APA, Harvard, Vancouver, ISO, and other styles

45

Patel, Jagrti, Meghna Jain, and Papiya Dutta. "Detection of Faults Using Digital Image Processing Technique." Asian Journal of Engineering and Applied Technology 2, no. 1 (2013): 36–39. http://dx.doi.org/10.51983/ajeat-2013.2.1.644.

Full text

Abstract:

This paper presents an approach to automatic detection of fabric defects using digital image processing. In Textile industry automatic fabric inspection is important to maintain the quality of fabric. Fabric defect detection is carried out manually with human visual inspection for a long time. This paper proposes an approach to recognize fabric defects in textile industry for minimizing production cost and time. Fabric analysis is performed on the basis of digital images of the fabric. The recognizer acquires digital fabric images by image acquisition device and converts that image into binary

APA, Harvard, Vancouver, ISO, and other styles

46

Ho, Chao-Ching, Wei-Chi Chou, and Eugene Su. "Deep Convolutional Neural Network Optimization for Defect Detection in Fabric Inspection." Sensors 21, no. 21 (2021): 7074. http://dx.doi.org/10.3390/s21217074.

Full text

Abstract:

This research is aimed to detect defects on the surface of the fabric and deep learning model optimization. Since defect detection cannot effectively solve the fabric with complex background by image processing, this research uses deep learning to identify defects. However, the current network architecture mainly focuses on natural images rather than the defect detection. As a result, the network architecture used for defect detection has more redundant neurons, which reduces the inference speed. In order to solve the above problems, we propose network pruning with the Bayesian optimization al

APA, Harvard, Vancouver, ISO, and other styles

47

Li, Chao, Jun Li, Yafei Li, Lingmin He, Xiaokang Fu, and Jingjing Chen. "Fabric Defect Detection in Textile Manufacturing: A Survey of the State of the Art." Security and Communication Networks 2021 (May 10, 2021): 1–13. http://dx.doi.org/10.1155/2021/9948808.

Full text

Abstract:

Defects in the textile manufacturing process lead to a great waste of resources and further affect the quality of textile products. Automated quality guarantee of textile fabric materials is one of the most important and demanding computer vision tasks in textile smart manufacturing. This survey presents a thorough overview of algorithms for fabric defect detection. First, this review briefly introduces the importance and inevitability of fabric defect detection towards the era of manufacturing of artificial intelligence. Second, defect detection methods are categorized into traditional algori

APA, Harvard, Vancouver, ISO, and other styles

48

Hu, Feng, Jie Gong, Han Fu, and Wenliang Liu. "Fabric Defect Detection Method Using SA-Pix2pix Network and Transfer Learning." Applied Sciences 14, no. 1 (2023): 41. http://dx.doi.org/10.3390/app14010041.

Full text

Abstract:

This paper proposes a fabric defect detection algorithm based on the SA-Pix2pix network and transfer learning to address the issue of insufficient accuracy in detecting complex pattern fabric defects in scenarios with limited sample data. Its primary contribution lies in treating defects as disruptions to the fabric’s texture. It leverages a generative adversarial network to reconstruct defective images, restoring them to images of normal fabric texture. Subsequently, the reconstituted images are subjected to dissimilarity calculations against defective images, leading to image segmentation fo

APA, Harvard, Vancouver, ISO, and other styles

49

Zhao, Hanqing, and Tuanshan Zhang. "Fabric Surface Defect Detection Using SE-SSDNet." Symmetry 14, no. 11 (2022): 2373. http://dx.doi.org/10.3390/sym14112373.

Full text

Abstract:

For fabric defect detection, the crucial issue is that large defects can be detected but not small ones, and vice versa, and this symmetric contradiction cannot be solved by a single method, especially for colored fabrics. In this paper, we propose a method based on a combination of two networks, SE and SSD, namely the SE-SSD Net method. The model is based on the SSD network and adds the SE module for squeezing and the Excitation module after its convolution operation, which is used to increase the weight of the model for the feature channels containing defect information while re-preserving t

APA, Harvard, Vancouver, ISO, and other styles

50

Xie, Huosheng, Yafeng Zhang, and Zesen Wu. "An Improved Fabric Defect Detection Method Based on SSD." AATCC Journal of Research 8, no. 1_suppl (2021): 181–90. http://dx.doi.org/10.14504/ajr.8.s1.22.

Full text

Abstract:

The fabric defect detection algorithm based on object detection has become a research hotspot. The method based on the Single Shot MultiBox Detector (SSD) model has a fast detection speed, but the detection accuracy is insufficient. To balance the detection speed and accuracy of the model and meet the actual needs of the industry, an improved fabric defect detection algorithm based on SSD is proposed in this study. The Fully Convolutional Squeeze-and-Excitation (FCSE) block is added into the traditional SSD to improve the detection accuracy of the model. The number of default boxes was adjuste

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!