The Smart City Waste Classification Management System

In response to the growing demands of urbanization, our research presents a pioneering Smart City Waste Classification Management System utilizing advanced computer vision techniques for efficient and accurate waste sorting. This system integrates the innovative CT-Net algorithm, which synergizes the strengths of Convolutional Neural Networks (CNNs) and Transformer architectures to tackle the complex challenges posed by varied and unpredictable urban waste characteristics. Extensive evaluations on multiple datasets, including the proprietary Huawei Cloud waste dataset, demonstrate that our model significantly outperforms existing methodologies in terms of precision, robustness, and processing speed. By deploying this technology within urban waste management frameworks, cities can achieve remarkable improvements in sustainability and operational efficiency.

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Publication Details

Journal
Journal of Organizational and End User Computing
Published
2024-08-12
DOI
https://doi.org/10.4018/joeuc.351242
Citations
2
Primary Topic
Municipal Solid Waste Management
Type
article
Field-Weighted Citation Impact
0.59
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article

The Smart City Waste Classification Management System

Mengyun Xie, Xuesong Yang, Wenming Cai, Yayun Liu
2 citations
Journal of Organizational and End User Computing
Municipal Solid Waste Management
0.59
article

The Smart City Waste Classification Management System

Mengyun Xie, Xuesong Yang, Wenming Cai, Yayun Liu
article en
2 citations

Abstract

In response to the growing demands of urbanization, our research presents a pioneering Smart City Waste Classification Management System utilizing advanced computer vision techniques for efficient and accurate waste sorting. This system integrates the innovative CT-Net algorithm, which synergizes the strengths of Convolutional Neural Networks (CNNs) and Transformer architectures to tackle the complex challenges posed by varied and unpredictable urban waste characteristics. Extensive evaluations on multiple datasets, including the proprietary Huawei Cloud waste dataset, demonstrate that our model significantly outperforms existing methodologies in terms of precision, robustness, and processing speed. By deploying this technology within urban waste management frameworks, cities can achieve remarkable improvements in sustainability and operational efficiency.

Journal of Organizational and End User ComputingVol. 36(1)
Yili Normal University (CN), Wuhan University (CN)
Sustainable cities and communities
Openalex Percentile: Top 31%
Municipal Solid Waste Management
0.59
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