Texture defect detection by using polynomial interpolation and multilayer perceptron

Abid, S.

doi:10.1177/1558925018825272

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…At present, the fabric defect detection methods proposed by domestic and foreign researchers mainly include five categories based on structural analysis, 16 statistical analysis, 17 frequency domain analysis, 18 learning analysis, 19 and model analysis. 20 The traditional fabric defect detection method based on image processing only realizes the detection of the defect but does not clearly detect the category of the detected defect.…”

Section: Related Workmentioning

confidence: 99%

Defect detection algorithm for fabric based on deformable convolutional network

Luo

Cheng

et al. 2022

Textile Research Journal

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The detection of fabric defects is an important aspect of textile quality management. This paper proposes an algorithm based on YOLOv3 and deformable convolutional network to solve the problems of low accuracy, high rate of missed and false detection, and high labor cost existing in traditional manual detection methods. First, data enhancement is adopted to address the problem of imbalanced categories of defective samples in the dataset. Then, the Resnet101 model is used to extract the features, and the original convolution operation is replaced by deformable convolution to balance the accuracy and speed of the model. Finally, the focal loss function is introduced to solve the insensitivity problem of the model to difficult defect samples. Experimental results show that this method can quickly and accurately detect defects on the surface of fabrics. In real-time detection, the average accuracy of this method is 8.3% higher than that of YOLOv3. The average accuracy of multiple categories of detection reaches more than 90%, and the detection effect of small defect targets, such as three silk, is also relatively better.

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Section: Related Workmentioning

confidence: 99%

Defect detection algorithm for fabric based on deformable convolutional network

Luo

Cheng

et al. 2022

Textile Research Journal

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“…Several unsupervised representation learning algorithms retain many properties of artificial multi-layer neural networks, relying on the back-propagation algorithm to estimate stochastic gradients. Abid 24 adopted a polynomial interpolation and multilayer perceptron method to train a neural network to detect and locate regions of defects. Ren et al 25 presented a generic deeplearning method that used a pre-trained network and transferred features to build classifier, and then convolved the trained classifier over input image to make pixel-wise prediction.…”

Section: Introductionmentioning

confidence: 99%

Fabric defect detection based on deep-feature and low-rank decomposition

Liu

Wang

et al. 2020

Journal of Engineered Fibers and Fabrics

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Fabric defect detection plays an important role in controlling the quality of textile production. In this article, a novel fabric defect detection algorithm is proposed based on a multi-scale convolutional neural network and low-rank decomposition model. First, multi-scale convolutional neural network, which can extract the multi-scale deep feature of the image using multiple nonlinear transformations, is adopted to improve the characterization ability of fabric images with complex textures. The effective feature extraction makes the background lie in a low-rank subspace, and a sparse defect deviates from the low-rank subspace. Then, the low-rank decomposition model is constructed to decompose the feature matrix into the low-rank part (background) and the sparse part (salient defect). Finally, the saliency maps generated by the sparse matrix are segmented based on an improved optimal threshold to locate the fabric defect regions. Experimental results indicate that the feature extracted by the multi-scale convolutional neural network is more suitable for characterizing the fabric texture than the traditional hand-crafted feature extraction methods, such as histogram of oriented gradient, local binary pattern, and Gabor. The adopted low-rank decomposition model can effectively separate the defects from the background. Moreover, the proposed method is superior to state-of-the-art methods in terms of its adaptability and detection efficiency.

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