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Recycled Clothing Classification System Using Intelligent IoT and Deep Learning with AlexNet.

Sun-Kuk Noh1

  • 1National Program of Excellence in Software Center, CHOSUN University, Gwangju, Republic of Korea.

Computational Intelligence and Neuroscience
|April 15, 2021
PubMed
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This study introduces an Internet of Things (IoT) and artificial intelligence (AI) system for classifying recycled clothing. The AI-powered object recognition accurately sorts garments, improving efficiency and reducing waste in textile recycling.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Internet of Things
  • Textile Recycling

Background:

  • The textile industry faces challenges with high resource consumption, energy use, and environmental pollution, partly due to inefficient clothing recycling processes.
  • Manual collection systems for recycled clothing in Korea are problematic, leading to workplace hazards, rising labor costs, and slow processing speeds.
  • Existing clothing recognition technologies struggle with deformed and overlapping garments, hindering automation in recycling.

Purpose of the Study:

  • To propose an automated recycled clothing classification system leveraging Internet of Things (IoT) and artificial intelligence (AI).
  • To address the limitations of manual classification systems in the textile recycling industry.
  • To enhance the efficiency and accuracy of recycled clothing sorting.

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Main Methods:

  • Development of an IoT system integrating a Raspberry Pi and a camera for image acquisition.
  • Application of deep learning, specifically a transfer-learned AlexNet model, for object recognition and clothing classification.
  • Implementation of an AI-driven system to analyze and categorize different types of recycled clothing.

Main Results:

  • The AI system accurately predicted and classified types of recycled clothing, surpassing the capabilities of manual, experience-based sorting.
  • Demonstrated the feasibility of using AI and IoT for automated classification in closed-space textile recycling environments.
  • Achieved accurate classification work, replacing the need for human expertise and reducing reliance on worker experience.

Conclusions:

  • The proposed IoT and AI system offers an innovative solution for recycled clothing classification, moving beyond traditional manual methods.
  • Expected outcomes include process standardization, AI utilization, automation, cost reduction, and improved work efficiency in textile recycling.
  • This technology paves the way for a more sustainable and efficient approach to managing used clothing resources.