Combing Deep and Handcrafted Features for NTV-NRPCA Based Fabric Defect Detection

Wang, Junpu; Li, Chunlei; Liu, Zhoufeng; Dong, Yan; Huang, Yun

doi:10.1007/978-3-030-31726-3_41

Cited by 6 publications

(2 citation statements)

References 19 publications

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“…It is worth noting that deep features and handcrafted low-level features are complementary. Wang et al 30 found that deep features can be enhanced by adding handcrafted features. By combining the respective advantages of handcrafted and deep feature, the representation ability of fabric image can be enhanced.…”

Section: The Proposed Methodsmentioning

confidence: 99%

Fabric defect detection based on deep-handcrafted feature and weighted low-rank matrix representation

Jiang

Liu

et al. 2021

Journal of Engineered Fibers and Fabrics

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In the process of textile production, automatic defect detection plays a key role in controlling product quality. Due to the complex texture features of fabric image, the traditional detection methods have poor adaptability, and low detection accuracy. The low rank representation model can divide the image into the low rank background and sparse object, and has proven suitable for fabric defect detection. However, how to further effectively characterize the fabric texture is still problematic in this kind of method. Moreover, most of them adopt nuclear norm optimization algorithm to solve the low rank model, which treat every singular value in the matrix equally. However, in the task of fabric defect detection, different singular values of feature matrix represent different information. In this paper, we proposed a novel fabric defect detection method based on the deep-handcrafted feature and weighted low-rank matrix representation. The feature characterization ability is effectively improved by fusing the global deep feature extracted by VGG network and the handcrafted low-level feature. Moreover, a weighted low-rank representation model is constructed to treat the matrix singular values differently by different weights, thus the most distinguishing feature of fabric texture can be preserved, which can efficiently outstand the defect and suppress the background. Qualitative and quantitative experiments on two public datasets show that our proposed method outperforms the state-of-the-art methods.

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Section: The Proposed Methodsmentioning

confidence: 99%

Fabric defect detection based on deep-handcrafted feature and weighted low-rank matrix representation

Jiang

Liu

et al. 2021

Journal of Engineered Fibers and Fabrics

Self Cite

View full text Add to dashboard Cite

show abstract

“…Some studies combine the traditional and deep learning methods. Wang et al [103] extracted global deep features using CNN in combination with handcrafted low-level features, and nonconvex robust PCA regularized by nonconvex total variation are employed to data processing and noise reduction. en a segmentation algorithm is used to segment the saliency map to obtain the defect area.…”

Section: Deep Learning Algorithmsmentioning

confidence: 99%

Fabric Defect Detection in Textile Manufacturing: A Survey of the State of the Art

et al. 2021

Security and Communication Networks

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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 algorithms and learning-based algorithms, and traditional algorithms are further categorized into statistical, structural, spectral, and model-based algorithms. The learning-based algorithms are further divided into conventional machine learning algorithms and deep learning algorithms which are very popular recently. A systematic literature review on these methods is present. Thirdly, the deployments of fabric defect detection algorithms are discussed in this study. This paper provides a reference for researchers and engineers on fabric defect detection in textile manufacturing.

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Multi-scale defect detection for plaid fabrics using scale sequence feature fusion and triple encoding

Zhao,

Ma,

Shi

et al. 2024

Vis Comput

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Combing Deep and Handcrafted Features for NTV-NRPCA Based Fabric Defect Detection

Cited by 6 publications

References 19 publications

Fabric defect detection based on deep-handcrafted feature and weighted low-rank matrix representation

Fabric defect detection based on deep-handcrafted feature and weighted low-rank matrix representation

Fabric Defect Detection in Textile Manufacturing: A Survey of the State of the Art

Multi-scale defect detection for plaid fabrics using scale sequence feature fusion and triple encoding

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