An improved gray line profile method to inspect the warp–weft density of fabrics

Aldemir, Erdoğan; Özdemi̇r, Hakan; Sarı, Zekeriya

doi:10.1080/00405000.2018.1467743

Cited by 13 publications

(10 citation statements)

References 17 publications

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“…Only using RMSE cannot demonstrate the performance of our model for fabric density measurement. So, by following the convention of existing works [6], [12], we calculate the mean-absolute-percentage error (MAPE) between automatic and manual fabric density. The manual fabric density is measured according to [2].…”

Section: Evaluation Detailsmentioning

confidence: 99%

“…In general, the main workflow of automatic fabric density measurement is fabric image acquisition and image processing. The fabric images are mainly acquired through industrial cameras [6] or scanners [7], [8]. Due to the influence of fabric pattern and acquisition environment, the accuracy and application of these methods are limited.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, these methods are effective for the analysis of solid color fabrics. Although some methods can deal with yarn-dyed fabrics [18], complex pattern fabrics [6], and high-tightness woven fabrics (HTWF) [19], they all focus on a specific type of fabric. The parameters of their methods need to be adjusted according to different fabric types which has limitations in generalization and varieties adaptability.…”

Section: Introductionmentioning

confidence: 99%

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Woven Fabric Density Measurement by Using Multi-Scale Convolutional Neural Networks

et al. 2019

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Fabric density measurement plays a key role in the analysis of fabric structural parameters. Existing automatic measurement methods lack varieties of adaptability and present poor performance in practical application. In order to solve these problems, we use convolutional neural networks (CNNs) to locate warps and wefts for woven fabric density measurement. First, we use a portable wireless device to capture high-resolution fabric images and set up a new dataset with labeled yarns location. Based on this dataset, we propose an effective multi-scale convolutional neural network (MSnet) architecture to locate warps and wefts. Then, by using Hough transform and image projection of predicted yarns location, the fabric density is measured accurately. The experimental results emphasize that the proposed method has reached high accuracy under various kinds of patterns and densities of the fabrics and is superior to the state-of-theart methods in terms of its accuracy and robustness. Promisingly, the proposed method can provide novel ideas for more fabric structural parameter analyses. INDEX TERMS Warps and wefts locating, density measurement, fabric structural parameters, multi-scale convolutional neural networks.

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Section: Evaluation Detailsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Woven Fabric Density Measurement by Using Multi-Scale Convolutional Neural Networks

et al. 2019

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“…Following Zheng’s work, Aldemir et al. 19 proposed using a Gabor filter to extract textures and generate projection curves by gray line profiles of textures.…”

Section: Related Workmentioning

confidence: 99%

“…(b) Spatial domain methods. Gray projection 6,[16][17][18][19][20] is the main spatial domain method used to detect yarn locations. After projecting pixels in a fabric image along the weft or warp directions, yarns and interstices are located on the local maximum and local minimum of the gray projection.…”

mentioning

confidence: 99%

Nonparametric histogram segmentation-based automatic detection of yarns

Han

Luo

Xie

et al. 2019

Textile Research Journal

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Detection of yarns in fabric images is a basic task in real-time monitoring in fabric production processes since it relates to yarn density and fabric structure estimation. In this paper, a new detection method is proposed that can automatically and efficiently estimate the locations as well as the numbers of both weft and warp yarn in fabric images. The method has three sequential phases. First, the modulus of discrete partial derivatives at each pixel is projected onto the weft and warp directions to generate the accumulated histograms. Second, for each histogram, a monotone hypothesis of a nonparametric statistical approach is applied to segment the histogram. Third, according to the segmentation result, the locations of each weft and warp yarn are adaptively determined, while the fabric structure is also obtained. Numerical results demonstrate that, compared with classical yarn detection methods, which are based on image smoothing, the proposed method can estimate yarn locations and fabric structures with more accuracy, but also reduce the influence of yarn hairiness.

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Automatic Inspection of Yarn Locations by Utilizing Histogram Segmentation and Monotone Hypothesis

Han

Luo

2019

Lecture Notes in Computer Science

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An improved gray line profile method to inspect the warp–weft density of fabrics

Cited by 13 publications

References 17 publications

Woven Fabric Density Measurement by Using Multi-Scale Convolutional Neural Networks

Woven Fabric Density Measurement by Using Multi-Scale Convolutional Neural Networks

Nonparametric histogram segmentation-based automatic detection of yarns

Automatic Inspection of Yarn Locations by Utilizing Histogram Segmentation and Monotone Hypothesis

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