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An Intelligent Waste-Sorting and Recycling Device Based on Improved EfficientNet

Author

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  • Zhicheng Feng

    (Department of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000, China
    Jiangxi Provincial Key Laboratory of Maglev Technology, Ganzhou 341000, China)

  • Jie Yang

    (Department of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000, China
    Ganjiang Innovation Academy, Chinese Academy of Sciences, Ganzhou 341000, China)

  • Lifang Chen

    (Department of Science, Jiangxi University of Science and Technology, Ganzhou 341000, China)

  • Zhichao Chen

    (Department of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000, China
    Jiangxi Provincial Key Laboratory of Maglev Technology, Ganzhou 341000, China)

  • Linhong Li

    (Department of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000, China
    Jiangxi Provincial Key Laboratory of Maglev Technology, Ganzhou 341000, China)

Abstract

The main source of urban waste is the daily life activities of residents, and the waste sorting of residents’ waste is important for promoting economic recycling, reducing labor costs, and protecting the environment. However, most residents are unable to make accurate judgments about the categories of household waste, which severely limits the efficiency of waste sorting. We have designed an intelligent waste bin that enables automatic waste sorting and recycling, avoiding the extensive knowledge required for waste sorting. To ensure that the waste-classification model is high accuracy and works in real time, GECM-EfficientNet is proposed based on EfficientNet by streamlining the mobile inverted bottleneck convolution (MBConv) module, introducing the efficient channel attention (ECA) module and coordinate attention (CA) module, and transfer learning. The accuracy of GECM-EfficientNet reaches 94.54% and 94.23% on the self-built household waste dataset and TrashNet dataset, with parameters of only 1.23 M. The time of one recognition on the intelligent waste bin is only 146 ms, which satisfies the real-time classification requirement. Our method improves the computational efficiency of the waste-classification model and simplifies the hardware requirements, which contributes to the residents’ waste classification based on intelligent devices.

Suggested Citation

  • Zhicheng Feng & Jie Yang & Lifang Chen & Zhichao Chen & Linhong Li, 2022. "An Intelligent Waste-Sorting and Recycling Device Based on Improved EfficientNet," IJERPH, MDPI, vol. 19(23), pages 1-18, November.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:23:p:15987-:d:988889
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    References listed on IDEAS

    as
    1. Decai Tang & Lei Shi & Xiaojuan Huang & Ziqian Zhao & Biao Zhou & Brandon J. Bethel, 2022. "Influencing Factors on the Household-Waste-Classification Behavior of Urban Residents: A Case Study in Shanghai," IJERPH, MDPI, vol. 19(11), pages 1-16, May.
    2. Qing Yang & Lingmei Fu & Xingxing Liu & Mengying Cheng, 2018. "Evaluating the Efficiency of Municipal Solid Waste Management in China," IJERPH, MDPI, vol. 15(11), pages 1-23, November.
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    Cited by:

    1. 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.
    2. Yekti Wirani & Imairi Eitiveni & Yudho Giri Sucahyo, 2024. "Framework of Smart and Integrated Household Waste Management System: A Systematic Literature Review Using PRISMA," Sustainability, MDPI, vol. 16(12), pages 1-32, June.

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