Defect Detection for Patterned Fabric Images Based on GHOG and Low-Rank Decomposition

Li, Chunlei; Gao, Guangshuai; Liu, Zhoufeng; Huang, Di; Xi, Jiangtao

doi:10.1109/access.2019.2925196

Cited by 48 publications

(32 citation statements)

References 48 publications

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“…Meanwhile, Laplacian regularization is integrated in LRR to further enlarge the gaps between defective regions and the background. In [15], a spatial pooling strategy is utilized to improve the discrimination ability of an efficient second-order orientation-aware descriptor GHOG. Then the nuclear norm in RPCA is surrogated by a non-convex log det, which can improve the efficiency.…”

Section: Rpca Based Fabric Defect Detectionmentioning

confidence: 99%

“…Therefore, the low-rank decomposition model can naturally be used for fabric defect detection. A few methods have been proposed based on this idea and achieved good results [12]- [15]. However, further improvement is still required, as fabric images may be contaminated by various noises and interferences, which are also sparse in nature and hence may be falsely detected as defects by low-rank decomposition model.…”

Section: Introductionmentioning

confidence: 99%

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Fusing Multilevel Deep Features for Fabric Defect Detection Based NTV-RPCA

Dong

Wang

et al. 2020

IEEE Access

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Fabric defect detection plays an important role in automated inspection and quality control in textile manufacturing. As the fabric images have complex and diverse textures and defects, traditional detection methods show a poor adaptability and low detection accuracy. Robust principal component analysis (RPCA) model that can be used to separate the image into object and background have proven applicable in fabric defect detection. However, how to represent texture feature of the fabric image more effectively is still problematic in this kind of method. In addition, the use of the traditional RPCA may result in low accuracy and more noises in sparse part. In this paper, a novel fabric defect detection method based on multilevel deep features fusion and non-convex total variation regularized RPCA (NTV-RPCA) is proposed. Firstly, the image representation ability is well enhanced through multilevel deep features extracted by a convolutional neural network. Then, the non-convex total variation regularized RPCA is proposed in which total variation constraint significantly reduces the noises in sparse part and non-convex solution is more approximate to the authentic one. Next, multilevel saliency maps generated by the sparse matrixes are fused via RPCA to produce a more reliable detection result. Finally, the defect region is located by segmenting the fused saliency map via a threshold segmentation algorithm. Qualitative and quantitative experiments conducted on two public fabric image databases demonstrate that the proposed method improves the adaptability and detection accuracy comparing to the state-of-the-arts.

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Section: Rpca Based Fabric Defect Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fusing Multilevel Deep Features for Fabric Defect Detection Based NTV-RPCA

Dong

Wang

et al. 2020

IEEE Access

Self Cite

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“…The background in the acquired image, including the eyeglass support and the imaging plate, needs to be removed. This is achieved by using (1)- (5), and the results are exhibited in Fig. 11.…”

Section: Experimental Studymentioning

confidence: 99%

Automatic Detection of Dispersed Defects in Resin Eyeglass Based on Machine Vision Technology

2020

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This paper is concerned with detection of dispersed defects in the resin eyeglass. At present, manual detection is always used in industry, which inevitably causes impairing eyes and a high rate of false-negative detection. Statistics show that its average accuracy and the average detection time are about 85% and 10s, respectively. We for the first time propose an automatic approach to detection of dispersed defects in resin eyeglass, based on the machine vision technology. It is observed that the refractivity of the normal and the defective regions of an eyeglass are different, and thus the reflection image is also collected in our system in addition to the normal transmission image. Such an optical imaging system is modelled and its analysis shows the gray-scale gradient difference between the normal and defective regions in the acquired image is dramatically enhanced under the illumination of a point light source with our designed system. An image processing algorithm is then developed to reveal the above difference and detect dispersed defects in resin eyeglasses. Our simulation study verifies the proposed approach. Further, its experimental evaluation was carried out and the result was consistent with the simulation one, showing that its detection accuracy and the average detection time were 97.50% and 0.636s, respectively, which meet the requirements for online detection of dispersed defects. INDEX TERMS Automatic detection, dispersed defects, imaging analysis, machine vision, resin eyeglass.

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“…Patterned fabric could be defined as a fabric that has repetitive pattern or multicolored units in its design. Because of this, it is difficult for the defect detection of patterned fabric images as the detection results are susceptible to the color or pattern interference of these repeated units [14,15]. The wavelet golden image subtraction method proposed by Ngan [16] is one of the widely used algorithms.…”

Section: Introductionmentioning

confidence: 99%

Sequential Detection of Image Defects for Patterned Fabrics

2020

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Automatic fabric defect detection has been successfully applied to establish the quality quick response system for the automation of textile production. However, the image complexity and diversity of patterned fabrics have effects on the fabric defect detection, which makes it difficult for automatic quality inspection of textiles. In order to solve this problem, a novel method that sequential detection of image defects for patterned fabrics was proposed in this paper. Firstly, the fabric image was segmented adaptively based on the periodic distance of the pattern, which can be used for the determination of repetitive pattern block element in fabrics, so that the defect image blocks could be identified according to the minimum principle of structure similarity; Then, the texture features of defect image blocks were extracted to match the constructed feature dictionary of defect-free image block, the defect position could be located by distance measurement and threshold segmentation. Our experiments show that the proposed method has the advantages of low computational complexity, high detection accuracy and strong applicability, by the comparison with existing approaches for defect detection of patterned fabric. Moreover, the new proposed automatic defect location method can solve the problem of deficiency caused by manual defect marking using existing image inpainting algorithms, it can be used for the automatic image inpainting of patterned fabrics without manual marking to improve the robustness of computer vision-based fabric re-engineering. INDEX TERMS patterned fabric, quality inspection, defect detection, defect position, image inpainting.

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Defect Detection for Patterned Fabric Images Based on GHOG and Low-Rank Decomposition

Cited by 48 publications

References 48 publications

Fusing Multilevel Deep Features for Fabric Defect Detection Based NTV-RPCA

Fusing Multilevel Deep Features for Fabric Defect Detection Based NTV-RPCA

Automatic Detection of Dispersed Defects in Resin Eyeglass Based on Machine Vision Technology

Sequential Detection of Image Defects for Patterned Fabrics

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