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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
PubMed
Summary
This summary is machine-generated.

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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.

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.

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  • 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.