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Feasibility Study of Visual Computing and Machine Learning Application for Textile Material Sorting

Feasibility Study of Visual Computing and Machine Learning Application for Textile Material Sorting

Siu Cheung Ho, Jiannong Cao
Copyright: © 2021 |Pages: 25
ISBN13: 9781799849155|ISBN10: 1799849155|EISBN13: 9781799849162
DOI: 10.4018/978-1-7998-4915-5.ch013
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MLA

Ho, Siu Cheung, and Jiannong Cao. "Feasibility Study of Visual Computing and Machine Learning Application for Textile Material Sorting." Eco-Friendly Energy Processes and Technologies for Achieving Sustainable Development, edited by Mir Sayed Shah Danish and Tomonobu Shah Senjyu, IGI Global, 2021, pp. 243-267. https://doi.org/10.4018/978-1-7998-4915-5.ch013

APA

Ho, S. C. & Cao, J. (2021). Feasibility Study of Visual Computing and Machine Learning Application for Textile Material Sorting. In M. Danish & T. Senjyu (Eds.), Eco-Friendly Energy Processes and Technologies for Achieving Sustainable Development (pp. 243-267). IGI Global. https://doi.org/10.4018/978-1-7998-4915-5.ch013

Chicago

Ho, Siu Cheung, and Jiannong Cao. "Feasibility Study of Visual Computing and Machine Learning Application for Textile Material Sorting." In Eco-Friendly Energy Processes and Technologies for Achieving Sustainable Development, edited by Mir Sayed Shah Danish and Tomonobu Shah Senjyu, 243-267. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-4915-5.ch013

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Abstract

This project aims to study the feasibility of visual computing (VC) and machine learning (ML) method applied in the textile recycle industry for efficiently manages the post-consumer textile waste. It includes an image-based VC technology for supporting textile waste reuse and resale, and a material identification system for sorting textile materials by using near infrared (NIR)/hyperspectral spectroscopy technology to support efficiently recycling to reuse the textile fibre will be evaluated. The process involved collecting and validating reference samples and applying ML technique to auto recognize the garment type and features applying visual technology; afterward, the sorted garments would be measured and pre-treated by NIR/hyperspectral spectrum and building up the parameters for spectral patterns calculation for recycling process recover the fibre. The main part of the study is to proof of the concept for using VC and ML method for identifying the textile fibre in the recycling process.

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