SpringerLink
Log in

Research on Garbage Classification Based on Deep Learning

  • Conference paper
  • First Online:
Advanced Manufacturing and Automation XI (IWAMA 2021)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 880))

Included in the following conference series:

Abstract

Many cities in our country are facing serious problems of garbage classification, with the rapid development of artificial intelligence and deep learning related technologies, it can provide a good and effective solution for garbage classification. In this paper, we combine the existing garbage classification standards, pre-processes and label the data set according to different garbage classification basis. Based on the VGG16 deep convolutional neural network structure, the activation function, feature extraction and selection are improved. Through training, verification and optimization of the model, the recognition and classification of the four categories of garbage images are implemented, and the effectiveness of improved VGG16 algorithm is also verified through experiments.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Similar content being viewed by others

References

  1. Li, J.Y., Chen, X.L., Zhang, A.H.: Survey of garbage classification methods based on deep learning. Comput. Eng. 1, 1–11 (2021)

    Google Scholar 

  2. Yang, M., Thung, G.: Classification of trash for recyclability status. CS229 ProjectReport (2016)

    Google Scholar 

  3. Olugboja, A., Wang, Z.H.: Intelligent waste classification system using deep learning convolutional neural network. Proc. Manuf. 608–612 (2019)

    Google Scholar 

  4. Yang, J., Zeng, Z., Wang, K.: GarbageNet: a unified learning framework for robust garbage classification. IEEE Trans. Artif. Intell. (2021)

    Google Scholar 

  5. Kang, Z., Yang, J., Li, G., Zhang, Z.: An automatic garbage classification system based on deep learning. IEEE Access 8, 140019–140029 (2020)

    Article  Google Scholar 

  6. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. ICLR 2015 (2014)

    Google Scholar 

  7. Misra, D.: Mish: A self regularized non-monotonic neural activation function. ar** and weighted mRMR for feature selection and classification of protein sequence data. Int. J. Data Min. Bioinform. 23(1), 47–61 (2020)

    Article  Google Scholar 

Download references

Acknowledgment

The work is supported by Science Research and Technology Development Plan Project of Shiyan (2021K60), Innovation Training Project of Hubei University of Automotive Technology (DC2021022).

Author information

Authors and Affiliations

  1. School of Economics and Management, Hubei University of Automotive Technology, Shiyan, 442002, Hubei, China

    **g Zhou & Jie Qian

  2. Zhejiang Hong Cheng Computer Systems Co., Ltd., HangZhou, 311100, China

    Dongli Lu & Jun Guo

  3. College of Electrical and Information, Hubei University of Automotive Technology, Shiyan, 442002, Hubei, China

    **liang Zhang

Authors
  1. **g Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jie Qian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dongli Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jun Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. **liang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Business, Plymouth University, Plymouth, UK

    Yi Wang

  2. Department of Manufacturing and Civil Engineering, Norwegian University of Science and Technology, Gjøvik, Norway

    Kristian Martinsen

  3. Shanghai University of Engineering Science, Shanghai, China

    Tao Yu

  4. Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology, Trondheim, Norway

    Kesheng Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhou, J., Qian, J., Lu, D., Guo, J., Zhang, J. (2022). Research on Garbage Classification Based on Deep Learning. In: Wang, Y., Martinsen, K., Yu, T., Wang, K. (eds) Advanced Manufacturing and Automation XI. IWAMA 2021. Lecture Notes in Electrical Engineering, vol 880. Springer, Singapore. https://doi.org/10.1007/978-981-19-0572-8_58

Download citation

Publish with us

Policies and ethics

Search

Navigation