Multi-perspective measurement of yarn hairiness using mirrored images

Abstract

Most photoelectric and imaging methods for yarn hairiness measurements often provide underestimated data of hairy fibers measured from light projection, which ignores the spatial orientations and shapes of protruding fibers. In this project, a three-dimensional (3D) system was developed to detect hairy fibers from multiple perspectives and to reconstruct a 3D model for the yarn that permits fibers to be traced spatially. The system utilized two angled planar mirrors to view a yarn from five different perspectives simultaneously, and a digital camera to capture the multiple images in one panoramic picture. The image-processing techniques were used to dissect the panoramic picture into five sub-images containing separate views of the yarn, and to segment the sub-images to obtain yarn silhouettes showing the edges of the yarn and hairy fibers. A 3D model of the yarn could be built by merging the five silhouettes with the angles defined by the scene geometry of the dual mirrors. From the 3D model, hairy fibers protruding from the yarn core could be traced in the space for accurate length measurements. The system represents a simple and practical solution for the 3D measurement of yarn hairiness.

The Comparison of the Edge Detection Methods in the Determination of Yarn Hairiness through Image Processing

“…Therefore, lengths of hairs on opposite sides of the camera and the back of the yarn determine incorrectly. To solve this problem, Wang et al (2018) used two angled planar mirrors to view a yarn from five different perspectives simultaneously, and a digital camera to capture the multiple images in one panoramic picture [25]. acquired yarn images from different viewing angles by rotating yarns as 15 degree by the CMOS camera.…”

Yarn apparent evenness detection based on L0 norm smoothing and the expectation maximization method

“…Li et al 10 used the fuzzy C-means (FCM) clustering algorithm to extract the yarn evenness, and then calculate the coefficient of variation (CV) of yarn evenness. Wang and colleagues 11,12 developed a three-dimensional (3D) system to detect yarn. Ozkaya et al 13 focused on the influence of illumination conditions on the yarn apparent evenness diameter detection; the detected CV values using their method were similar to those measured by the Uster instrument.…”

“…Visual inspection, 2 biological microscope detection, 3 photoelectric detection, 4–7 capacitance evenness tester 8 and image analysis 9–21 are the widely used methods for yarn diameter evenness measurement.…”

“…10 used the fuzzy C-means (FCM) clustering algorithm to extract the yarn evenness, and then calculate the coefficient of variation (CV) of yarn evenness. Wang and colleagues 11,12 developed a three-dimensional (3D) system to detect yarn. Ozkaya et al.…”

A relative hairiness index for evaluating the securities of fiber ends in staple yarns and its application

“…Hairiness is a crucial parameter evaluating the status of protruding fiber ends, loops and wild fiber out of yarn bodies, and is one of the important yarn surface characteristics. 1,2 Since the 1950s, several methods [3][4][5][6][7][8] and instruments [9][10][11][12][13][14][15][16][17][18] have been developed to characterize yarn hairiness, notably the Uster Tester, Uster Zweigle hairiness tester, SDL hairiness tester, Lawson Hemphill tester, Keisokki Laserspot tester, amongst others; in addition, the most novel solutions for yarn hairiness measurement were mainly based on digital image processing and signal processing. 19,20 Notwithstanding this, only two methods were universally accepted and commercially utilized in textile industries and institutions: (a) an array of sensors measures the length of fiber ends protruding out of the yarn core; and (b) the amount of light scattered by the protruding fibers is used to calculate a hairiness index value for the yarn.…”