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Machine vision estimates the polyester content in recyclable waste textiles

Mäkelä, Mikko and Rissanen, Marja and Sixta, Herbert (2020). Machine vision estimates the polyester content in recyclable waste textiles. Resources, Conservation and Recycling. 161 , 105007
[Research article]

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Global textile production is mainly based on polyester and cotton fibers. A majority of textiles at the end of their lifecycle are currently landfilled or incinerated, but will be increasingly recycled in the future. Here, we discuss how the polyester content in blended textiles can be estimated based on hyperspectral near infrared imaging with the aim of developing machine vision for textile characterization and recycling. Differences in the textile samples were first visualized based on a principal component model and the polyester contents of individual image pixels were then predicted using image regression. The results showed average prediction errors of 2.24.5% within a range of 0-100% polyester and enabled visualizing the spatial changes in the polyester contents of the textiles. We foresee that digitalized tools similar to what we report here will be increasingly important in the future as more emphasis is placed on coordinated collection, sorting and reuse of waste textiles.

Authors/Creators:Mäkelä, Mikko and Rissanen, Marja and Sixta, Herbert
Title:Machine vision estimates the polyester content in recyclable waste textiles
Series Name/Journal:Resources, Conservation and Recycling
Year of publishing :2020
Article number:105007
Number of Pages:7
Publication Type:Research article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution-Noncommercial-No Derivative Works 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 2 Engineering and Technology > 205 Materials Engineering > Textile, Rubber and Polymeric Materials
(A) Swedish standard research categories 2011 > 1 Natural sciences > 102 Computer and Information Science > Computer Vision and Robotics (Autonomous Systems)
Keywords:Textiles, Cellulose, Polyester, Hyperspectral imaging, Near infrared, Regression
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Additional ID:
Type of IDID
Web of Science (WoS)000569610400085
ID Code:17801
Faculty:S - Faculty of Forest Sciences
Department:(S) > Department of Forest Biomaterials and Technology
Deposited By: SLUpub Connector
Deposited On:14 Oct 2020 11:19
Metadata Last Modified:15 Jan 2021 19:44

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