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Deep Learning System for Recycled Clothing Classification Linked to Cloud and Edge Computing.

Sun-Kuk Noh1

  • 1SW Convergence Education Institute, Chosun University, Gwangju, Republic of Korea.

Computational Intelligence and Neuroscience
|November 17, 2022
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Summary
This summary is machine-generated.

This study introduces a deep learning clothing classification system (DLCCS) using AI, cloud, and edge computing to improve used clothing recycling. The system efficiently sorts clothing images, reducing waste and improving worker health.

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Area of Science:

  • Information Technology
  • Artificial Intelligence
  • Environmental Science

Background:

  • High annual clothing consumption leads to significant textile waste.
  • Current used clothing collection and recycling systems are inefficient, lacking automation and facing labor challenges.
  • Health concerns for workers in manual clothing sorting processes are prevalent.

Purpose of the Study:

  • To propose a Deep Learning Clothing Classification System (DLCCS) integrating cloud and edge computing.
  • To enhance the efficiency and automation of used clothing classification and processing.
  • To address challenges in recycling systems, including labor shortages and worker health issues.

Main Methods:

  • Utilizing Convolutional Neural Network (CNN) for deep learning-based image classification of clothing.
  • Implementing a system that classifies clothing images into two and nine categories.
  • Employing edge computing for real-time data analysis from Internet of Things (IoT) devices before cloud transmission.

Main Results:

  • The DLCCS demonstrated efficient classification of clothing images.
  • Edge computing facilitated timely data analysis, improving transmission velocity and reducing latency.
  • The system showed potential for automating clothing classification processes.

Conclusions:

  • The proposed DLCCS offers an efficient solution for used clothing classification and recycling.
  • Integration of AI, cloud, and edge computing can significantly improve textile waste management.
  • The system is expected to reduce clothing resource waste and mitigate health risks for workers.