Deep learning based visual automated sorting system for remanufacturing

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Abstract

Remanufacturing is a crucial component of the circular economy concept which emphasises sustainable consumption habits. This study proposes a novel automated sorting system for remanufacturing which is based on deep convolutional neural networks(CNN). To demonstrate its applicability, the proposed deep learning (DL) system was used to distinguish among dry, wet, oily and defected surfaces. The test was conducted on four locally sourced 3" x 6" plates. Sample image data were captured using a USB webcam. The network training was done with 75% of the data while the balance data were used for testing. In this preliminary study, the DCNN classified the features with up to 99.74% accuracy on validation data and above 96% accuracy on live video feed; demonstrating that it can accurately sort components. This study is the first to propose a low-cost sorting system for remanufacturing based on the deep CNN and logic gates. The results show that the method is an accurate, reliable, cost-effective and fast technique that can potentially outperform existing sorting systems in the remanufacturing industry.
Original languageEnglish
Title of host publication2020 IEEE Green Technologies Conference (GreenTech)
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages196-198
Number of pages3
ISBN (Print)9781728150178
DOIs
Publication statusPublished - 16 Dec 2020
EventGreenTech 2020: 2020 IEEE Green Technologies Conference - Oklahoma City, United States
Duration: 1 Apr 20203 Apr 2020

Conference

ConferenceGreenTech 2020
CountryUnited States
CityOklahoma City
Period1/04/203/04/20

Keywords

  • remanufacturing
  • sorting in remanufacturing
  • sorting systems
  • deep learning for sorting

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