Adaptive detection of weft-knitted fabric defects based on machine vision system

Abstract

This paper describes a machine vision system for the detection of weft-knitted fabric defects based on an adaptive pulse-coupled neural network (PCNN) and Ridgelet transform. In order to classify defects according to their different texture features, two methods are implemented: an improved PCNN method to segment the defects such as hole and dropped stitch from background image and a Ridgelet transform method based on wavelet analysis to identify the defect such as course mark. In implementing the PCNN model, necessary parameters of PCNN model such as linking coefficient, connection weight, and iteration number are automatically calculated in accordance with the spatial distance of neurons, mean, and variance value of whole image, and the cross-entropy criterion. The function of Ridgelet transform is to identify the straight line marks and fit the regression equation for simulating the course mark in the image. The Ridgelet transform model can be simplified as the combination of Radon and wavelet transforms. The parameters of detected line are acquired by wavelet analysis in Fourier semicircle region. The experiment materials were several plain and interlocked weft-knitted fabrics with hole, dropped stitch, and course mark defects. The fabric images were captured by an area-scan camera with a resolution of 600 × 800 pixels, and signal processing was controlled by a digital signal processing multiprocessor on the inspection machine. The validation tests proved that the system performed well.

Online Fabric Defect Inspection Using Smart Visual Sensors

“…The Gabor wavelet was used in the detection algorithm. Sun [5] proposed an adaptive inspection system based on a PCNN neural network, which had area scan cameras with resolution of 800 × 600 and a computer. Experiments showed the effectiveness of his method for plain and interlocked weft-knitted fabrics with holes, dropped stitches, and course mark defects.…”

Detection of chemical fabric defects on the basis of morphological processing

“…Ebraheem, Yasser, Mohamed, Safinaz, andChristopher (2006) proposed a new method for the detection and classification of defects in knitted fabrics by applying image analysis and neural networks. Sun and Long (2011) approached weft-knitted fabric defects based on an adaptive pulse-couple neural network and Ridgelet transform. A neural network and morphological processing were also combined by Chandra, Banerjee, and Datta (2010) for detecting fabric defects.…”

A Novel Industrial Application of CNN Approach: Real Time Fabric Inspection and Defect Classification on Circular Knitting Machine

“…The fabric structure can be composed with different methods such as knitting, weaving and the nonwoven texture property. Considering the knitting fabric production, common defects are seen such as hole/cracks, loops/drop stitches, lycra missing, knots [3][4][5][6][7]. These defect types are defined on the knitted fabric roll after production prosess.…”