Toward Low-Cost RF-Based Bulk Fabric Classification for the Textile Industry

Fashion industry is currently low sustainable, where less than 1% of textile garments are recycled, generating 92 million tons of waste per year. Solutions to enable circular economy require efficient methods for automated bulk fabric identification for improving recycling/reconditioning in the text...

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Bibliographic Details
Published in:IEEE sensors journal 2022-08, Vol.22 (16), p.16586-16594
Main Authors: Lejarreta-Andres, Jon, Melia-Segui, Joan, Bhattacharyya, Rahul, Vilajosana, Xavier, Sarma, Sanjay E.
Format: Article
Language:English
Subjects:
Antenna design
Antennas
Backscattering
circular economy
Classification
Cotton
Dielectrics
Fabrics
Industries
Low cost
low-cost sensors
Moisture
Radio frequency identification
Reconditioning
Recycling
RFID tags
sensing applications
Sensors
smart environment
textile recognition
Textiles
Ultrahigh frequencies
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Summary:Fashion industry is currently low sustainable, where less than 1% of textile garments are recycled, generating 92 million tons of waste per year. Solutions to enable circular economy require efficient methods for automated bulk fabric identification for improving recycling/reconditioning in the textile industry. In this context, we have analyzed the UHF RFID backscatter response during fabrics drying process, with the goal of finding classifiable patterns. This paper describes the initial results regarding suitable UHF RFID tag antenna designs, and relevant features for identifying fabrics based on different RF profiles. Preliminary analysis show better performance for meander tag antennas, and an accuracy over 92% and F1 Scores of 95.7% and 82.7% when classifying between cotton and polyester fabrics respectively. The proposed method returns promising results towards low-cost unassisted bulk fabric identification, which may help to automate recycling processes and support better circular economy policies in the textile industries.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2022.3188936