Fabric defect detection based on adaptive LBP and SVM

Fu, Rong; Shi, Meihong

doi:10.3724/sp.j.1087.2010.01597

Cited by 3 publications

(3 citation statements)

References 6 publications

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“…The selection of reference images has a significant influence on the detection accuracy. In Fu and Shi (2010) and (Liu et al (2010), the techniques of LBP and Support Vector Machine (SVM) are proposed for defect detection. The proposed methods use the main pattern set of normal fabric images without defects and the set of fabric images with defects to train a SVM classifier, which was reported of having achieved a high detection accuracy.…”

Section: Ijcst 275mentioning

confidence: 99%

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A fabric defect detection algorithm via context-based local texture saliency analysis

Liu

Zhao

et al. 2015

International Journal of Clothing Science and Technology

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Purpose -Fabric defect detection plays an important role in textile quality control. The purpose of this paper is to propose a fabric defect detection algorithm via context-based local texture saliency analysis. Design/methodology/approach -In the proposed algorithm, a target image is first divided into blocks, then the Local Binary Pattern (LBP) technique is used to extract the texture features of blocks. Second, for a given image block, several other blocks are randomly chosen for calculating the LBP contrast between a given block and the randomly chosen blocks. Based on the obtained contrast information, a saliency map is produced. Finally, saliency map is segmented by using an optimal threshold, which is obtained by an iterative approach. Findings -The experimental results show that the proposed algorithm, integrating local texture features and global image texture information, can detect texture defects effectively. Originality/value -In this paper, a novel fabric defect detection algorithm via context-based local texture saliency analysis is proposed.

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Section: Ijcst 275mentioning

confidence: 99%

“…IJCST 27,5 Recently, LBP has also been applied for fabric defect detection (Tajeripour et al, 2008;Fu and Shi, 2010;Liu et al, 2010). For example, Tajeripour et al (2008) employed the property difference between the LBP features of a given fabric image and a reference image for defect detection.…”

mentioning

confidence: 99%

A fabric defect detection algorithm via context-based local texture saliency analysis

Liu

Zhao

et al. 2015

International Journal of Clothing Science and Technology

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“…In the textile industry, fabric defect detection is a key part of quality control, but realising automatic detection is difficult . Research on fabric defect detection can contribute to successful automatic detection.…”

Section: Introductionmentioning

confidence: 99%

The design of optimal real Gabor filters and their applications in fabric defect detection

Feng

2015

Coloration Technology

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Fabric defect detection has been recognised as one of the key challenges for automatic production, and Gabor filters are one of the most useful tools in detecting fabric defects. The half‐peak tangent method is applied in real Gabor filter design so that the filters can cover the frequency of defects as much as possible. Meanwhile, the half‐peak‐magnitude contours of the neighbouring filters are tangential. On this basis, two optimal orientations are selected by applying direction masks, and the optimal scale at each optimal orientation is determined according to the signal‐to‐noise ratio. In this way, two optimal real Gabor filters are obtained. A new algorithm based on the two optimal filters is proposed for fabric defect detection. A series of experiments are carried out for 46 fabric defect images combined with 46 corresponding reference fabric images, in order to verify the effectiveness of the new algorithm. The experimental results obtained show that the new algorithm can accurately detect defects in grey fabric defect images as well as in colour images. For the 46 fabric defect images, the detection rate is 95.66%, indicating that the new algorithm performs well. In addition, comparison of the new algorithm with other algorithms in the literature demonstrates that the new algorithm is more effective in the detection of several fabric defect images.

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Fabric defect detection algorithm based on MFS and SVM

Zhao¹,

Ma²

2018

2018 International Conference on Image and Video Processing, and Artificial Intelligence

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Fabric defect detection based on adaptive LBP and SVM

Cited by 3 publications

References 6 publications

A fabric defect detection algorithm via context-based local texture saliency analysis

A fabric defect detection algorithm via context-based local texture saliency analysis

The design of optimal real Gabor filters and their applications in fabric defect detection

Fabric defect detection algorithm based on MFS and SVM

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