• Journal of Information Processing Systems
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• 제19권1호
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• pp.109-117
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• 2023

Defect detection is one of the key factors in fabric quality control. To improve the speed and accuracy of denim fabric defect detection, this paper proposes a defect detection algorithm based on cascading feature extraction architecture. Firstly, this paper extracts these weight parameters of the pre-trained VGG16 model on the large dataset ImageNet and uses its portability to train the defect detection classifier and the defect recognition classifier respectively. Secondly, retraining and adjusting partial weight parameters of the convolution layer were retrained and adjusted from of these two training models on the high-definition fabric defect dataset. The last step is merging these two models to get the defect detection algorithm based on cascading architecture. Then there are two comparative experiments between this improved defect detection algorithm and other feature extraction methods, such as VGG16, ResNet-50, and Xception. The results of experiments show that the defect detection accuracy of this defect detection algorithm can reach 94.3% and the speed is also increased by 1-3 percentage points.

• Journal of Information Processing Systems
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• 제16권5호
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• pp.1008-1014
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• 2020

Aiming at the defect detection quality of denim fabric, this paper designs an improved algorithm based on the optimized Gabor filter. Firstly, we propose an improved defect detection algorithm of jean fabric based on the maximum two-dimensional image entropy and the loss evaluation function. Secondly, 24 Gabor filter banks with 4 scales and 6 directions are created and the optimal filter is selected from the filter banks by the one-dimensional image entropy algorithm and the two-dimensional image entropy algorithm respectively. Thirdly, these two optimized Gabor filters are compared to realize the common defect detection of denim fabric, such as normal texture, miss of weft, hole and oil stain. The results show that the improved algorithm has better detection effect on common defects of denim fabrics and the average detection rate is more than 91.25%.

• 한국정보시스템학회지:정보시스템연구
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• 제32권4호
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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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권2호
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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.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제2권1호
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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.

• 스마트미디어저널
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• 제11권11호
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• pp.92-98
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• 2022

섬유산업에서 생산된 직물의 결함을 식별하는 것은 품질관리를 위한 핵심적인 절차이다. 본 연구는 직물의 이미지를 분석하여 결함을 검출하는 모델을 만들고자 하였다. 연구에 사용된 모델은 딥러닝 기반의 VGGNet 과 ResNet이었고, 두 모델의 결함 검출 성능을 비교하여 평가하였다. 정확도는 VGGNet 모델이 0.859, ResNet 모델이 0.893으로 ResNet 모델의 정확도가 더 높은 결과를 보여주었다. 추가적으로 딥러닝 모델이 직물의 이미지 내에서 결함으로 인식한 부분의 위치를 알아보기 위하여 XAI(eXplainable Artificial Intelligence)기법인 Grad-CAM 알고리즘을 사용하여 모델의 관심영역을 도출하였다. 그 결과 딥러닝 모델이 직물의 결함으로 인식한 부분이 육안으로도 실제 결함이 있는 것으로 확인되었다. 본 연구의 결과는 직물의 결함 검출에 있어서 딥러닝 기반의 인공지능을 활용함으로써 섬유의 생산과정에서 발생하는 시간과 비용을 줄일 수 있을 것으로 기대된다.

• 한국정밀공학회지
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• 제20권9호
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• pp.92-99
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• 2003

Quality inspection of textile products is an important problem for fabric manufacturers. This paper presents an automatic vision-based system for quality control of web textile fabrics. Typical web material is 1-3m wide and is driven with speeds ranging from 20m/min to 200m/min. At the present, the quality assessment procedures are performed manually by expert. But worker can not detect more than 60％ of the present defect and inspect the fabric if moving faster than 30m/min. To increase the overall quality and homogeneity of textile, an automated visual inspection system is needed fur the productivity. However, the existing inspection system are too expensive to purchase for small companies. In this paper, the proposed PC based real-time inspection algorithm gives low cost textile inspection system, high detection rate with good accuracy and low rate of false alarms. The method shows good results in the detection of several types of fabric defects.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1988년도 전기.전자공학 학술대회 논문집
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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.

• Journal of Information Processing Systems
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• 제8권2호
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• pp.359-374
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• 2012

In this paper, we propose a new machine vision algorithm for automatic defect detection on patterned textures with the help of texture-periodicity and the Jensen-Shannon Divergence, which is a symmetrized and smoothed version of the Kullback-Leibler Divergence. Input defective images are split into several blocks of the same size as the size of the periodic unit of the image. Based on histograms of the periodic blocks, Jensen-Shannon Divergence measures are calculated for each periodic block with respect to itself and all other periodic blocks and a dissimilarity matrix is obtained. This dissimilarity matrix is utilized to get a matrix of true-metrics, which is later subjected to Ward's hierarchical clustering to automatically identify defective and defect-free blocks. Results from experiments on real fabric images belonging to 3 major wallpaper groups, namely, pmm, p2, and p4m with defects, show that the proposed method is robust in finding fabric defects with a very high success rates without any human intervention.

• 한국항행학회논문지
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• 제26권2호
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• pp.113-118
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• 2022

사람은 독서나 필기 중 중요 문구를 형광펜으로 칠하는 것에서 착안하여, 본 논문에서는 복잡한 배경 질감을 가진 영상에서의 불량유형을 효과적으로 분류하기 위해 불량 픽셀 영역을 하이라이팅 하여 신경망을 훈련하는 방법을 제안한다. 제안 방법의 가능성을 검증하기 위하여 불량유형 구분이 매우 어려운 타이어 밴드 직물의 불량유형 분류에 제안 방법을 적용한다. 또한, 타이어 밴드 직물 영상에 특화된 백라이트 하이라이팅 방법을 제안한다. 백라이트 하이라이트 영상은 GradCAM 기법과 간단한 영상처리를 이용하여 획득할 수 있다. 실험에서 우리는 제안하는 하이라이팅 기법이 분류 정확도뿐만 아니라 훈련속도 면에서 기존 방법보다 우수함을 보였다. 인식률 면에서는 제안 방법이 기존 방법 대비 최대 13.4%의 향상을 달성하였다. 타이어 밴드 직물 영상에 특화된 백라이트 하이라이팅 기법이 윤곽 하이라이팅 기법보다 정확도 측면에서 우수함을 보였다.