Blind weave detection for woven fabrics

Schneider, Dorian; Merhof, Dorit

doi:10.1007/s10044-014-0403-9

Cited by 33 publications

(15 citation statements)

References 20 publications

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“…All parameters can be retrieved in a straightforward manner from one single fabric reference image by using an image-processing tool that allows to display the coordinates of individual pixels. As a convenient alternative, we proposed a methodology that iteratively uses Fourier analysis and fuzzy C-mean clustering to calculate the grid and yarn vectors automatically within the context of blind weave detection in woven fabrics [15].…”

Section: A System Calibrationmentioning

confidence: 99%

“…Natural fabric images, especially satin and twill fabrics, do not meet these requirements. Corresponding sample fabrics can be found in the database published in [15]. Techniques for fabric density measurement based on the analysis of the fabric's Fourier spectrum are discussed by Xu [16], Tunák et al [17], Ravandi [18], and Sari-Sarraf and Goddard [19] who designed straightforward band-pass filters in the frequency domain and apply these to the signal to extenuate frequencies that do not relate the repetitive pattern of wefts and warps.…”

Section: Introductionmentioning

confidence: 99%

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Vision-Based On-Loom Measurement of Yarn Densities in Woven Fabrics

Schneider

Gloy

Merhof

2015

IEEE Trans. Instrum. Meas.

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A vision-based measurement system to quantify the yarn density of woven fabrics during production is presented. As an extension to an earlier developed fabric flaw detection system, the proposed framework consists of a combination of basic and custom-made image-processing techniques that allow to precisely track single wefts and warps within fabric images-in real-time. Several adaptations facilitate the measurement of density changes for plain, satin, and twill weaves. In this paper, the algorithmic framework has been evaluated in several comprehensive on-line experiments on a real-world air-jet loom and is additionally compared with three alternative methods for fabric density measurement. It proved to be precise, robust, and applicable for industrial use as it overcomes many of the existing shortcomings of current methods.Index Terms-Fabric quality control, machine vision algorithms, textile inspection, vision-based measurement (VBM), yarn density measurement.

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Section: A System Calibrationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Vision-Based On-Loom Measurement of Yarn Densities in Woven Fabrics

Schneider

Gloy

Merhof

2015

IEEE Trans. Instrum. Meas.

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“…More importantly, the quality index cannot be quantified by manual checking. Therefore, it is crucial to develop automatic detection method for the textile industry [3].…”

Section: Introductionmentioning

confidence: 99%

A Practical Method for Detecting Fluff Quality of Fabric Surface Using Optimal Sensing

Jin

Lin

Bie

et al. 2020

ELEKTRON ELEKTROTECH

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The raising process has been widely used in manufacturing fabric productions. After raising the surface of the fabric, productions are covered with a fluff layer. The quality of the fabric surface is often valuated by the fluffing type. In order to objectively assess the fluff quality of the fabric surface, an optimal sensing method is proposed in this paper. The fluff contour image was firstly collected by the light-cut imaging device. Then, the fluff region was segmented by the adaptive image segmentation method, the contour coordinates of the fabric were extracted using the freeman chain code and constructed in the form of the binary image. Lastly, a back-propagation neural network (BPNN) was used to learn the relationship between the contour coordinates and the fluff quality. On this basis, a practical fabric fluff detection platform was developed based on the optimal sensing technique. Experimental tests were conducted to evaluate the performance of the proposed method in detecting the fluff quality with four different colours and different fluffing processes. Furthermore, the actual fabric inspection was carried out. The detection correct rate can reach 94.17 %, which can meet the practical production requirement.

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“…However, Fourier transform was usually used with other approaches in the latest works (Schneider and Merh 2015 ; Hu et al 2015 ; Mohamed et al 2014 ; As et al 2013 ). Schneider presented an automatic method for plain and twill fabric detection by combining Fourier analysis, template matching and fuzzy clustering (Schneider and Merh 2015 ). The system proved to be robust and versatile as a 97 % detection accuracy could be achieved.…”

Section: Introductionmentioning

confidence: 99%

Automated vision system for fabric defect inspection using Gabor filters and PCNN

Zhang

2016

SpringerPlus

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In this study, an embedded machine vision system using Gabor filters and Pulse Coupled Neural Network (PCNN) is developed to identify defects of warp-knitted fabrics automatically. The system consists of smart cameras and a Human Machine Interface (HMI) controller. A hybrid detection algorithm combing Gabor filters and PCNN is running on the SOC processor of the smart camera. First, Gabor filters are employed to enhance the contrast of images captured by a CMOS sensor. Second, defect areas are segmented by PCNN with adaptive parameter setting. Third, smart cameras will notice the controller to stop the warp-knitting machine once defects are found out. Experimental results demonstrate that the hybrid method is superior to Gabor and wavelet methods on detection accuracy. Actual operations in a textile factory verify the effectiveness of the inspection system.

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Blind weave detection for woven fabrics

Cited by 33 publications

References 20 publications

Vision-Based On-Loom Measurement of Yarn Densities in Woven Fabrics

Vision-Based On-Loom Measurement of Yarn Densities in Woven Fabrics

A Practical Method for Detecting Fluff Quality of Fabric Surface Using Optimal Sensing

Automated vision system for fabric defect inspection using Gabor filters and PCNN

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