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Image capturing, segmentation and data analysis of shredded refuse streams.

Heimo Gursch1, Elke Schlager1, Franz Thaler2

  • 1Know-Center GmbH, Knowledge Discovery, Graz, Austria.

Waste Management & Research : the Journal of the International Solid Wastes and Public Cleansing Association, ISWA
|June 24, 2024
PubMed
Summary
This summary is machine-generated.

Digitalizing refuse sorting plants uses AI-powered image recognition to analyze shredded waste. This system improves recycling efficiency by identifying refuse composition and volume, optimizing the sorting process.

Keywords:
3D imagingRefuse composition detectionmachine learningmulti-spectral imaging

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Area of Science:

  • Waste Management
  • Computer Vision
  • Artificial Intelligence

Background:

  • Recycling industry faces challenges with dynamic refuse compositions and increasing recycling rates.
  • Digitalization offers solutions for optimizing and automating refuse sorting processes.

Purpose of the Study:

  • To develop and evaluate a system for image capturing, refuse recognition, and data analysis of shredded waste streams.
  • To enable automated adaptation and optimization of sorting processes in digitalization refuse sorting plants.

Main Methods:

  • Multispectral 2D and 3D image acquisition of refuse streams on conveyor belts.
  • Semantic segmentation using a combined convolutional neural network model trained on synthetic data.
  • Integration of segmentation results with 3D volume estimation and shredding machinery data.

Main Results:

  • Achieved up to 75% Intersection over Union in semantic segmentation performance.
  • Developed a unified data representation combining image recognition and volume estimation.
  • Enabled estimation of processed refuse masses based on the integrated dataset.

Conclusions:

  • The developed system demonstrates potential for enhancing refuse sorting efficiency through AI-driven analysis.
  • Utilizing synthetic data for training reduces manual labeling efforts while maintaining performance.
  • The integrated approach provides a basis for accurate estimation of processed refuse masses in automated sorting.