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Related Experiment Video

Updated: Aug 23, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Edge-Computing Video Analytics Solution for Automated Plastic-Bag Contamination Detection: A Case from Remondis.

Umair Iqbal1, Johan Barthelemy2, Pascal Perez1

  • 1SMART Infrastructure Facility, University of Wollongong, Wollongong, NSW 2522, Australia.

Sensors (Basel, Switzerland)
|October 27, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an AI-powered edge-computing system to automatically detect plastic bag contamination in waste streams, improving recycling efficiency. The optimized YOLOv4 model achieved 63% mAP, significantly reducing manual efforts in waste management.

Keywords:
Artificial Intelligence of Things (AIoT)computer visiondeep learningedge-computingwaste contamination

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Area of Science:

  • Waste Management and Recycling
  • Artificial Intelligence and Computer Vision
  • Sustainable Development

Background:

  • Global waste generation is increasing, posing significant challenges to effective waste management and recycling efforts.
  • Contamination, particularly from plastic bags, renders up to 75% of recyclable waste unusable, hindering sustainable development goals.
  • Manual bin-tagging for contamination detection is labor-intensive and inefficient.

Purpose of the Study:

  • To develop an automated system for detecting plastic bag contamination in waste streams.
  • To leverage edge-computing, Artificial Intelligence (AI), and Artificial Intelligence of Things (AIoT) for real-time waste analytics.
  • To improve the efficiency and accuracy of waste sorting processes for enhanced recycling.

Main Methods:

  • An edge-computing video analytics solution was proposed, capturing waste videos from truck hoppers.
  • Deep learning models, Faster R-CNN and YOLOv4, were trained using the Remondis Contamination Dataset (RCD).
  • System performance was evaluated using metrics like Mean Average Precision (mAP) and Frames Per Second (FPS) on NVIDIA Jetson TX2 hardware.

Main Results:

  • The YOLOv4 model with CSPDarkNet_tiny backbone demonstrated suitability, achieving 63% mAP and 24.8 FPS.
  • Retraining models with real-world deployment data improved performance, enhancing mAP, True Positives (TPs), and reducing False Positives (FPs) and False Negatives (FNs).

Conclusions:

  • The AI-driven edge-computing system offers an efficient and automated solution for detecting plastic bag contamination in waste.
  • The optimized YOLOv4 model provides a viable approach for real-time waste analytics, contributing to improved recycling rates and sustainable waste management.
  • The study provides a cost analysis, offering valuable insights for stakeholders and policymakers in waste management infrastructure.