• Title/Summary/Keyword: fabric defects

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A Study on The Visual Inspection of Fabric Defects (시각 장치를 이용한 직물 결함 검사에 관한 연구)

  • Kyung, Kye-Hyun;Ko, Myoung-Sam;Lee, Sang-Uk;Lee, Bum-Hee
    • Proceedings of the KIEE Conference
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    • 1988.07a
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    • pp.959-962
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    • 1988
  • This paper describes an automatic visual inspection system for fabric defects based on pattern recognition techniques. The inspection for fabric defects can be separated into three sequences of operations which are the detection of fabric defects[1], the classification of figures of fabric defects, and the classification of fabric defects. Comparing projections of defect-detected images with the predefined complex, the classification accuracy of figures of fabric defects was found to be 95.3 percent. Employing the Bayes classifier using cluster shade in SGLDM and variance in decorrelation method as features, the classification accuracy of regional figure defects was found to be 82.4 percent. Finally, some experimental results for line and dispersed figures of fabric defects are included.

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Deep Learning Models for Fabric Image Defect Detection: Experiments with Transformer-based Image Segmentation Models (직물 이미지 결함 탐지를 위한 딥러닝 기술 연구: 트랜스포머 기반 이미지 세그멘테이션 모델 실험)

  • Lee, Hyun Sang;Ha, Sung Ho;Oh, Se Hwan
    • The Journal of Information Systems
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    • v.32 no.4
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    • pp.149-162
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    • 2023
  • Purpose In the textile industry, fabric defects significantly impact product quality and consumer satisfaction. This research seeks to enhance defect detection by developing a transformer-based deep learning image segmentation model for learning high-dimensional image features, overcoming the limitations of traditional image classification methods. Design/methodology/approach This study utilizes the ZJU-Leaper dataset to develop a model for detecting defects in fabrics. The ZJU-Leaper dataset includes defects such as presses, stains, warps, and scratches across various fabric patterns. The dataset was built using the defect labeling and image files from ZJU-Leaper, and experiments were conducted with deep learning image segmentation models including Deeplabv3, SegformerB0, SegformerB1, and Dinov2. Findings The experimental results of this study indicate that the SegformerB1 model achieved the highest performance with an mIOU of 83.61% and a Pixel F1 Score of 81.84%. The SegformerB1 model excelled in sensitivity for detecting fabric defect areas compared to other models. Detailed analysis of its inferences showed accurate predictions of diverse defects, such as stains and fine scratches, within intricated fabric designs.

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

  • Jiang, Jiein;Jin, Zilong;Wang, Boheng;Ma, Li;Cui, Yan
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.14 no.2
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    • pp.687-701
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    • 2020
  • 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.

Wavelet Analysis to Real-Time Fabric Defects Detection in Weaving processes

  • Kim, Sung-Shin;Bae, Hyeon;Jung, Jae-Ryong;Vachtsevanos, George J.
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.2 no.1
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    • pp.89-93
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    • 2002
  • This paper introduces a vision-based on-line fabric inspection methodology of woven textile fabrics. Current procedure for determination of fabric defects in the textile industry is performed by human in the off-line stage. The advantage of the on-line inspection system is not only defect detection and identification, but also 벼ality improvement by a feedback control loop to adjust set-points. The proposed inspection system consists of hardware and software components. The hardware components consist of CCD array cameras, a frame grabber and appropriate illumination. The software routines capitalize upon vertical and horizontal scanning algorithms characteristic of a particular deflect. The signal to noise ratio (SNR) calculation based on the results of the wavelet transform is performed to measure any deflects. The defect declaration is carried out employing SNR and scanning methods. Test results from different types of defect and different style of fabric demonstrate the effectiveness of the proposed inspection system.

A Study on The Visual Inspection of Fabric Defects (시각 장치를 이용한 직불 결합 인식에 관한 연구)

  • Kyung, Kye-Hyun;Ko, Myoung-Sam;Lee, Sang-Uk;Lee, Bum-Hee
    • Proceedings of the KIEE Conference
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    • 1987.11a
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    • pp.311-315
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    • 1987
  • This paper describes the automatic visual inspect ion system of fabric defects based on pattern recognition techniques. To extract features for detection of fabric defects, four different techniques such as SGLDM. GCM, decorrelation method, and Laws' texture measure were investigated. From results of computer simulation, it has been found that GCM and decorrelation techniques provide good features. By employing a simple statistical pattern recognition technique, theaccuracy of classification of defect and nondefect was more than 90%. Some experimental results arm also presented.

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A Wavelet-based Yarn Quality Assessment for Fabric Visual Qualities (직물외관을 위한 웨이블릿 기반의 방적사 평가시스템)

  • Kim, Jooyong
    • Proceedings of the Korean Society for Emotion and Sensibility Conference
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    • 2002.05a
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    • pp.16-19
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    • 2002
  • Random and/or periodic defects occur in all spun yarns. These irregularities can often lead to defects in finished fabric. Yarn evenness tests are used to obtain statistical data about yarn properties, such as CV%, which is useful in comparing several sets of similar data that differ in mean value but may have some commonality in relative variation. Although this statistical data is helpful in determining relative yarn Quality, accurate predictions of how the yarn will appear in fabric form are still difficult to obtain. As an promising alterative, wavelet analysis has been employed to localize yam defect so as to predict the visual qualifies of the fabrics.

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A Wavelet-based Yarn Quality Assessment for Fabric Visual Qualities

  • Kim, Joo-Yong
    • Science of Emotion and Sensibility
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    • v.5 no.3
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    • pp.35-38
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    • 2002
  • Random and/or periodic defects occur in all spun yarns. These irregularities can often lead to defects in finished fabric. Yarn evenness tests are used to obtain statistical data about yarn properties, such as CV%, which is useful in comparing several sets of similar data that differ in mean value but may have some commonality in relative variation. Although this statistical data is helpful in determining relative yam quality, accurate predictions of how the yarn will appear in fabric form are still difficult to obtain. As an promising alterative, wavelet analysis has been employed to localize yarn defect so as to predict the visual qualities of the fabrics.

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Experimental Remarks on Manually Attentive Fabric Defect Regions (직물 결함영역을 표시한 영상에 대한 실험적 고찰)

  • Shohruh, Rakhmatov;Choi, Hyeon-yeong;Ko, Jaepil
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2019.05a
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    • pp.442-444
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    • 2019
  • Fabric defect classification is an important issue in fabric quality control. However, automated classification is difficult because it is hard to identify various types of defects in images. classification of fabric defects mostly rely on human ability. In this paper, to solve this problem we apply Convolutional Neural Networks (CNN) for fabric defect classification. To make training CNN easier, we propose a method that is manually attentive defect regions in images. we compare the proposed method with the original image and confirm that the proposed method is effective for learning.

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A Study on the Defect Detection of Fabrics using Deep Learning (딥러닝을 이용한 직물의 결함 검출에 관한 연구)

  • Eun Su Nam;Yoon Sung Choi;Choong Kwon Lee
    • Smart Media Journal
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    • v.11 no.11
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    • pp.92-98
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    • 2022
  • 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) technique, to find out the location of the region that the deep learning model recognized as a defect in the fabric image. As a result, it was confirmed that the region recognized by the deep learning model as a defect in the fabric was actually defective even with the naked eyes. The results of this study are expected to reduce the time and cost incurred in the fabric production process by utilizing deep learning-based artificial intelligence in the defect detection of the textile industry.