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RGB and RGNIR image dataset for machine learning in plastic waste detection.

Owen Tamin1, Ervin Gubin Moung2,3, Jamal Ahmad Dargham4

  • 1Faculty of Science and Natural Resources, Universiti Malaysia Sabah, Jalan UMS, Kota Kinabalu, 88400, Sabah, Malaysia.

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PubMed
Summary

A new dataset combines standard RGB and near-infrared (NIR) spectral imaging for plastic waste. This resource aids machine learning models in accurately detecting and classifying diverse plastic waste, advancing environmental solutions.

Keywords:
Automatic sorting methodsColour spacesDeep learningEnvironmental issuesMachine learningPlastic waste datasetPlastic waste detectionRGBRGNIRSpectral imaging

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Area of Science:

  • Environmental Science
  • Computer Science
  • Materials Science

Background:

  • Plastic waste poses a significant environmental challenge, necessitating advanced sorting technologies.
  • Spectral imaging offers potential for plastic identification but faces limitations in cost, complexity, and resolution.
  • Machine learning (ML) shows promise for analyzing plastic waste data, but requires comprehensive datasets.

Purpose of the Study:

  • To address the lack of publicly available datasets combining RGB and near-infrared (NIR) spectral data for plastic waste detection.
  • To introduce a novel, comprehensive dataset designed for training ML models for plastic waste identification.
  • To facilitate advancements in automated plastic waste sorting and management.

Main Methods:

  • Collected onshore images of plastic waste using standard RGB and Red-Green-Near-Infrared (RGNIR) spectral channels.
  • Captured 405 images per dataset along riverbanks and beaches.
  • Pre-processed and annotated a total of 1,344 plastic waste objects for ML model training.

Main Results:

  • Developed two distinct datasets: one RGB and one RGNIR spectral dataset.
  • Successfully annotated 1,344 plastic waste objects, providing detailed feature information.
  • The dataset uniquely integrates RGB and NIR spectral data, offering richer information than standard RGB alone.

Conclusions:

  • The introduced dataset provides a valuable, unique resource for researchers in plastic waste detection.
  • This dataset is expected to enhance the performance of ML models in identifying and classifying plastic waste.
  • The findings encourage further research into spectral imaging and ML for improved plastic waste management strategies.