Using Style-Transfer to Understand Material Classification for Robotic Sorting of Recycled Beverage Containers

Mark D. McDonnell, Bahar Moezzi, Russell S.A. Brinkworth

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Robotic sorting machines are increasingly being investigated for use in recycling centers. We consider the problem of automatically classifying images of recycled beverage containers by material type, i.e. glass, plastic, metal or liquid-packaging-board, when the containers are not in their original condition, meaning their shape and size may be deformed, and coloring and packaging labels may be damaged or dirty. We describe a retrofitted computer vision system and deep convolutional neural network classifier designed for this purpose, that enabled a sorting machine's accuracy and speed to reach commercially viable benchmarks. We investigate what was more important for highly accurate container material recognition: shape, size, color, texture or all of these? To help answer this question, we made use of style-transfer methods from the field of deep learning. We found that removing either texture or shape cues significantly reduced the accuracy in container material classification, while removing color had a minor negative effect. Unlike recent work on generic objects in ImageNet, networks trained to classify by container material type learned better from object shape than texture. Our findings show that commercial sorting of recycled beverage containers by material type at high accuracy is feasible, even when the containers are in poor condition. Furthermore, we reinforce the recent finding that convolutional neural networks can learn predominantly either from texture cues or shape.

Original languageEnglish
Title of host publication2019 Digital Image Computing
Subtitle of host publicationTechniques and Applications, DICTA 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781728138572
DOIs
Publication statusPublished - Dec 2019
Externally publishedYes
Event2019 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2019 - Perth, Australia
Duration: 2 Dec 20194 Dec 2019

Publication series

Name2019 Digital Image Computing: Techniques and Applications, DICTA 2019

Conference

Conference2019 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2019
CountryAustralia
CityPerth
Period2/12/194/12/19

Keywords

  • style-transfer
  • Recycled plastic
  • beverage containers
  • robotic sorting machines

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