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Application of MobileNetV2 to waste classification

Author

Listed:
  • Liying Yong
  • Le Ma
  • Dandan Sun
  • Liping Du

Abstract

Today, the topic of waste separation has been raised for a long time, and some waste separation devices have been installed in large communities. However, the vast majority of domestic waste is still not properly sorted and put out, and the disposal of domestic waste still relies mostly on manual classification. The research in this paper applies deep learning to this persistent problem, which has important significance and impact. The domestic waste is classified into four categories: recyclable waste, kitchen waste, hazardous waste and other waste. The garbage classification model trained based on MobileNetV2 deep neural network can classify domestic garbage quickly and accurately, which can save a lot of labor, material and time costs. The absolute accuracy of the trained network model is 82.92%. In comparison with CNN network model, the classification accuracy of MobileNetV2 model is 15.42% higher than that of CNN model. In addition, the trained model is light enough to be better applied to mobile.

Suggested Citation

  • Liying Yong & Le Ma & Dandan Sun & Liping Du, 2023. "Application of MobileNetV2 to waste classification," PLOS ONE, Public Library of Science, vol. 18(3), pages 1-16, March.
    • Handle: RePEc:plo:pone00:0282336
    DOI: 10.1371/journal.pone.0282336
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