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Smartphone-based bulky waste classification using convolutional neural networks

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Abstract

The rapid urbanization process is escalating the urban waste problem, and ineffective management has worsened the issue, leading to severe consequences to the population health and economy. Although many countries have started to charge money for large household items, it is time-consuming and challenging for collectors to distinguish various types of bulky waste manually. As a result, this study introduces a mobile-based automatic bulky waste classification system. The original contributions include (1) a fine-tuned VGG-19 model is proposed to classify 95 types of bulky wastes; (2) three hybrid models are introduced to efficiently handle the imbalanced data problem, including class-weight VGG-19 (CW_VGG19), eXtreme Gradient Boosting VGG-19 (XGB_VGG19), and Light Gradient Boosting Machine VGG-19 (LGB_VGG19); (3) a large dataset that includes 95 classes, and each class contains over 500 images; and (4) the development of a mobile application that used the proposed model. Experiments show that the model obtained an accuracy of 86.19%, which outperforms existing models in classifying bulky waste. Moreover, the proposed hybrid models showed their robustness against imbalanced data under various scenarios.

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Acknowledgements

This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2020R1A6A1A03038540) and by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (2019-0-00136, Development of AI-Convergence Technologies for Smart City Industry Productivity Innovation).

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Sejong University, Seoul, South Korea

    Hanxiang Wang, Yanfen Li, L. Minh Dang, Dongil Han & Hyeonjoon Moon

  2. Gatda corporation, 31 Baekbeom-ro 21-gil, Mapo-gu, Seoul, Republic of Korea

    Jaesung Ko

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  1. Hanxiang Wang
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  2. Yanfen Li
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  3. L. Minh Dang
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  4. Jaesung Ko
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  5. Dongil Han
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  6. Hyeonjoon Moon
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Correspondence to Hyeonjoon Moon.

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Wang, H., Li, Y., Dang, L.M. et al. Smartphone-based bulky waste classification using convolutional neural networks. Multimed Tools Appl 79, 29411–29431 (2020). https://doi.org/10.1007/s11042-020-09571-5

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  • DOI: https://doi.org/10.1007/s11042-020-09571-5

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