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Image Recognition for Garbage Classification Based on Transfer Learning and Model Fusion

Author

Listed:
  • Wei Liu
  • Hengjie Ouyang
  • Qu Liu
  • Sihan Cai
  • Chun Wang
  • Junjie Xie
  • Wei Hu
  • Zaoli Yang

Abstract

Garbage is an underutilized resource, and garbage classification is one of the effective ways to make full use of these resources. In order to realize the automation of garbage classification, some deep learning models are used for garbage images recognition. A novel garbage image recognition model Garbage Classification Net (GCNet) based on transfer learning and model fusion is proposed in this paper. After extracting garbage image features, EfficientNetv2, Vision Transformer, and DenseNet, respectively, are combined to construct the neural network model of GCNet. Data augmentation is used to expand the dataset and 41,650 garbage images are contained in the new dataset. Compared with other models through experiments, the results show that the proposed model has good convergence, high recall rate and accuracy, and short recognition time.

Suggested Citation

  • Wei Liu & Hengjie Ouyang & Qu Liu & Sihan Cai & Chun Wang & Junjie Xie & Wei Hu & Zaoli Yang, 2022. "Image Recognition for Garbage Classification Based on Transfer Learning and Model Fusion," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-12, August.
    • Handle: RePEc:hin:jnlmpe:4793555
    DOI: 10.1155/2022/4793555
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    Cited by:

    1. Mohammed Imran Basheer Ahmed & Raghad B. Alotaibi & Rahaf A. Al-Qahtani & Rahaf S. Al-Qahtani & Sara S. Al-Hetela & Khawla A. Al-Matar & Noura K. Al-Saqer & Atta Rahman & Linah Saraireh & Mustafa Youl, 2023. "Deep Learning Approach to Recyclable Products Classification: Towards Sustainable Waste Management," Sustainability, MDPI, vol. 15(14), pages 1-16, July.
    2. Ying Zhan & Yue Sun & Junfei Xu, 2023. "A Study on the Recycling Classification Behavior of Express Packaging Based on UTAUT under “Dual Carbon” Targets," Sustainability, MDPI, vol. 15(15), pages 1-22, July.

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