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Classifying Matter by Composition

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Data-centric approach for instance segmentation in optical waste sorting.

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Summary
This summary is machine-generated.

This study introduces a data-centric pipeline to improve household waste sorting using computer vision. By combining pseudo-annotation and data augmentation, the system significantly enhances waste segmentation accuracy, making sorting more efficient and cost-effective.

Keywords:
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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Environmental Technology

Background:

  • Computer vision systems offer improved efficiency and cost reduction in household waste sorting.
  • Current systems face limitations due to poor data annotation, challenging recognition environments, and insufficient data for visible-range cameras.

Purpose of the Study:

  • To develop a data-centric pipeline for enhancing the precision of multiclass household waste segmentation on conveyor belts.
  • To address the limitations of existing computer vision systems in waste sorting applications.

Main Methods:

  • A data-centric pipeline incorporating data balancing, transfer learning, and pseudo-labeling was developed.
  • A pseudo-annotation approach combined with an object-based data augmentation algorithm was employed.
  • A dataset of 5,000 manually labeled and 10,000 pseudo-labeled data points was prepared.

Main Results:

  • The pipeline improved the mean Average Precision (mAP) of the YOLOV8 segmentation model from 67% to 83% for simple scenarios.
  • For complex industrial solutions, the mAP increased from 42% to 59%.
  • Demonstrated successful model training on 'simple' images yielding satisfactory results on 'complex' images.

Conclusions:

  • The proposed data-centric pipeline effectively enhances the precision of computer vision-based waste segmentation.
  • Pseudo-annotation and object-based augmentation are key to overcoming data quality and quantity limitations.
  • The approach offers a cost-effective solution for improving efficiency in automated waste sorting systems.