Fabric defect detection based on GLCM and Gabor filter: A comparison

Raheja, Jagdish Lal; Kumar, Sunil; Chaudhary, Ankit

doi:10.1016/j.ijleo.2013.05.004

Cited by 125 publications

(60 citation statements)

References 14 publications

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“…Grey level co-occurrence matrix, one of the best promising methods for texture analysis, estimates image properties related to second-order statistics [14]. And it is the basis of analysing local pattern and the pixel arrangement rules of images [15].…”

Section: Grey Level Co-occurrence Matrixmentioning

confidence: 99%

Yarn-Dyed Fabric Defect Detection Based On Autocorrelation Function And GLCM

Zhu

Pan

Gao

et al. 2015

Autex Research Journal

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In this study, a new detection algorithm for yarn-dyed fabric defect based on autocorrelation function and grey level co-occurrence matrix (GLCM) is put forward. First, autocorrelation function is used to determine the pattern period of yarn-dyed fabric and according to this, the size of detection window can be obtained. Second, GLCMs are calculated with the specified parameters to characterise the original image. Third, Euclidean distances of GLCMs between being detected images and template image, which is selected from the defect-free fabric, are computed and then the threshold value is given to realise the defect detection. Experimental results show that the algorithm proposed in this study can achieve accurate detection of common defects of yarn-dyed fabric, such as the wrong weft, weft crackiness, stretched warp, oil stain and holes.

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Section: Grey Level Co-occurrence Matrixmentioning

confidence: 99%

Yarn-Dyed Fabric Defect Detection Based On Autocorrelation Function And GLCM

Zhu

Pan

Gao

et al. 2015

Autex Research Journal

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“…6,7,[19][20][21] An important property of Gabor filters is that they achieve optimal joint localization (or resolution) in both the spatial and frequency domains. 22) In the spatial domain, the Gabor function is a complex exponential modulated by a Gaussian function. The general form of the two-dimensional (2D) Gabor function is given by:…”

Section: Filtering For Pattern Extractionmentioning

confidence: 99%

Detection of Periodic Defects Using Dual-Light Switching Lighting Method on the Surface of Thick Plates

et al. 2015

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Currently, automated inspection algorithms are widely used to ensure high-quality products and achieve high productivity in the steel making industry. In this paper, we propose a vision-based method to detect periodic defects in the surface of thick plates. To minimize the influence of a non-uniform surface property and improve the accuracy of the detection rate, a detection method based on dual-light switching lighting (DLSL) proposed. In general, single lighting (SL) methods cannot well represent the steel surface because the surface features are not uniform and strongly vary according to lighting conditions. In the DLSL method, defective regions are represented by a black and white pattern, regardless of shape, size, or orientation. Therefore, defects can be found by the black and white patterns in the corresponding images. Gabor filtering was used to find defective regions and reduce the false positive rates. To find the periodic candidates of defects, we process the period searching using manufacturing information. To identify periodic defects from among the defect candidates, we use "similarity of shapes" features with a support vector machine (SVM) classifier. The experimental results show that the proposed algorithm is effective at detecting periodic defects on the surface of thick plates.

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“…Image segmentation is an indispensable step for most popular defect detection methods, including statistical methods, 2-9 model-based methods, [10][11][12] and frequency spectral methods. [13][14][15][16][17][18][19][20][21] In all of these methods, the determination and location of defects depends on image segmentation. That is, almost all surface defect detection methods must segment each image to be inspected before determining whether the product is defective or not.…”

Section: Introductionmentioning

confidence: 99%

Rapid surface defect detection based on singular value decomposition using steel strips as an example

2018

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On the radiated EMI current extraction of dc transmission line based on corona current statistical measurements AIP Advances 8, 055001 (2018) For most surface defect detection methods based on image processing, image segmentation is a prerequisite for determining and locating the defect. In our previous work, a method based on singular value decomposition (SVD) was used to determine and approximately locate surface defects on steel strips without image segmentation. For the SVD-based method, the image to be inspected was projected onto its first left and right singular vectors respectively. If there were defects in the image, there would be sharp changes in the projections. Then the defects may be determined and located according sharp changes in the projections of each image to be inspected. This method was simple and practical but the SVD should be performed for each image to be inspected. Owing to the high time complexity of SVD itself, it did not have a significant advantage in terms of time consumption over image segmentation-based methods. Here, we present an improved SVD-based method. In the improved method, a defect-free image is considered as the reference image which is acquired under the same environment as the image to be inspected. The singular vectors of each image to be inspected are replaced by the singular vectors of the reference image, and SVD is performed only once for the reference image off-line before detecting of the defects, thus greatly reducing the time required. The improved method is more conducive to real-time defect detection.

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Fabric defect detection based on GLCM and Gabor filter: A comparison

Cited by 125 publications

References 14 publications

Yarn-Dyed Fabric Defect Detection Based On Autocorrelation Function And GLCM

Yarn-Dyed Fabric Defect Detection Based On Autocorrelation Function And GLCM

Detection of Periodic Defects Using Dual-Light Switching Lighting Method on the Surface of Thick Plates

Rapid surface defect detection based on singular value decomposition using steel strips as an example

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