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

Abstract

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.

Reduction of the Weaving Process Set-up Time through Multi-Objective Self- Optimization

“…Measurement technology for fabric quality: At Institut für Textiltechnik der RWTH Aachen University (ITA), Aachen, Germany a measuring system for online error detection during fabric production was developed [6]. A camera takes pictures of the fabric.…”

“…The digital image processing software is calibrated using a flawless piece of fabric. The digital image processing classifies deviations from the calibrated condition as a defect [6]. Depending on the share of incorrect pixels in the pictures, the fabric is assigned to a quality category.…”

“…d tot is calculated by using the geometric mean of the individual desirabilities: camera direction of production area where the fabric is created 1 m Figure 3: Vision-based defect detection of woven fabrics according to [6].…”

“…Vision-based defect detection of woven fabrics according to [6]. The total desirability reveals how close the individual desirabilities are to the optimal range.…”

Training a New Instrument to Measure Cotton Fiber Maturity Using Transfer Learning

“…In many cases, the new instrument may provide the same measurement in a completely different manner. For example, in [1], a visionbased method is presented to measure yarn density in woven fabrics, which replaces an older method using X-ray sensors. In [2], a new sensor is designed to measure fabric texture in a manner that mimics human touch.…”