JSAI2024

Presentation information

General Session

General Session » GS-3 Knowledge utilization and sharing

[4F3-GS-3] Knowledge utilization and sharing:

Fri. May 31, 2024 2:00 PM - 3:40 PM Room F (Temporary room 4)

座長:川崎 敦史(株式会社東芝)

3:00 PM - 3:20 PM

[4F3-GS-3-04] A Construction of the Data Set for Classification of Used Clothes

〇Chie Mizunuma1, Tomoko Ikeya1, Hiromi Kurosaki1, Norika Note1, Noriyuki Okumura1, Yasushi Kami2, Miwako Hanada1 (1. Kobe Shoin Women's University, 2. Hiroshima Institute of Technology)

[[Online]]

Keywords:data set, used clothes, recycle

The fashion industry poses a significant environmental burden from sourcing raw materials to disposal, making it an international challenge to address. According to a survey by the Ministry of the Environment, 95% of clothing disposed of as household waste is either incinerated or landfilled. This research aims to construct a system using AI to automatically determine whether unwanted clothing items can be reused or recycled, thereby reducing the amount of clothing sent to waste.In this report, we discuss the method for creating a dataset of clothing images necessary for machine learning, as there is no open dataset specifically targeting clothing items that fall within the borderlines of reuse or recycling. Images were obtained from clothing collected mainly from thrift stores dealing with low-cost items. These items were photographed from multiple directions using several cameras with varying resolutions, resulting in a total of 14,105 images. Furthermore, interviews were conducted with both sorters and individuals lacking sorting knowledge to gather their sorting processes and reasoning for reuse or recycling, which were then used to tag the collected images. Additionally, for social implementation, a system was considered to coordinate and present reusable clothing items together for potential outfits.

Authentication for paper PDF access

A password is required to view paper PDFs. If you are a registered participant, please log on the site from Participant Log In.
You could view the PDF with entering the PDF viewing password bellow.

Password