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DEEPBIN: Deep Learning Based Garbage Classification for Households Using Sustainable Natural Technologies

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Abstract

Today, things that are accessible worldwide are upgrading to innovative technology. In this research, an intelligent garbage system will be designed with State-of-the-art methods using deep learning technologies. Garbage is highly produced due to urbanization and the rising population in urban areas. It is essential to manage daily trash from homes and living environments. This research aims to provide an intelligent IoT-based garbage bin system, and classification is done using Deep learning techniques. This smart bin is capable of sensing more varieties of garbage from home. Though there are more technologies successfully implemented with IoT and machine learning, there is still a need for sustainable natural technologies to manage daily waste. The innovative IoT-based garbage system uses various sensors like humidity, temperature, gas, and liquid sensors to identify the garbage condition. Initially, the Smart Garbage Bin system is designed, and then the data are collected using a garbage annotation application. Next, the deep learning method is used for object detection and classification of garbage images. Arithmetic Optimization Algorithm (AOA) with Improved RefineDet (IRD) is used for object detection. Next, the EfficientNet-B0 model is used for the classification of garbage images. The garbage content is identified, and the content is prepared to train the deep learning model to perform efficient classification tasks. For result evaluation, smart bins are deployed in real-time, and accuracy is estimated. Furthermore, fine-tuning region-specific litter photos led to enhanced categorization.

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Data Availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

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Funding

This work was sponsored in part by Program of China Hebei education department (grant No. ZD2022088, ZC2023041, BJ2021049), Project of science and technology of Hebei Province (grant No.22374208D).

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Authors and Affiliations

  1. Hebei Key Laboratory of Agroecological Safety, Hebei University of Environmental Engineering, Qinhuangdao, 066102, China

    Yu Song, Xin He, Xiwang Tang, Jie Du, Jiali Liu, Zhongbao Zhao & Shigang Geng

  2. Institute of Microbiology Heilongjiang Academy of Sciences, Haerbin, 150010, China

    Bo Yin

Authors
  1. Yu Song
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  2. Xin He
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  3. Xiwang Tang
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  4. Bo Yin
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  5. Jie Du
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  6. Jiali Liu
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  7. Zhongbao Zhao
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  8. Shigang Geng
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Contributions

Yu Song: Conceptualization, Methodology, Formal analysis, Supervision, Writing—original draft, Writing—review & editing.

Xin He: Investigation, Data Curation, Validation, Resources, Writing—review & editing.

Xiwang Tang: Investigation, Data Curation, Validation, Resources, Writing—review & editing.

Bo Yin: Investigation, Validation, Resources, Writing—review & editing.

Jie Du: Formal analysis, Supervision, Writing.

Jiali Liu: Formal analysis, Supervision, Writing.

Zhongbao Zhao: Validation, Resources, Writing—review & editing.

Shigang Geng: Validation, Resources, Writing—review & editing.

Corresponding author

Correspondence to Shigang Geng.

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Song, Y., He, X., Tang, X. et al. DEEPBIN: Deep Learning Based Garbage Classification for Households Using Sustainable Natural Technologies. J Grid Computing 22, 2 (2024). https://doi.org/10.1007/s10723-023-09722-6

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  • DOI: https://doi.org/10.1007/s10723-023-09722-6

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