Dynamic Graph Neural Network for Garbage Classification Based on Multimodal Feature Fusion
Amid the accelerating pace of global urbanization, the volume of municipal solid garbage has surged dramatically, thereby demanding more efficient and precise garbage management technologies. In this paper, we introduce a novel garbage classification approach that leverages a dynamic graph neural ne...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/14/7688 |
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| author | Yuhang Yang Yuanqing Luo Yingyu Yang Shuang Kang |
| author_facet | Yuhang Yang Yuanqing Luo Yingyu Yang Shuang Kang |
| author_sort | Yuhang Yang |
| collection | DOAJ |
| description | Amid the accelerating pace of global urbanization, the volume of municipal solid garbage has surged dramatically, thereby demanding more efficient and precise garbage management technologies. In this paper, we introduce a novel garbage classification approach that leverages a dynamic graph neural network based on multimodal feature fusion. Specifically, the proposed method employs an enhanced Residual Network Attention Module (RNAM) network to capture deep semantic features and utilizes CIELAB color (LAB) histograms to extract color distribution characteristics, achieving a complementary integration of multimodal information. An adaptive K-nearest neighbor algorithm is utilized to construct the dynamic graph structure, while the incorporation of a multi-head attention layer within the graph neural network facilitates the efficient aggregation of both local and global features. This design significantly enhances the model’s ability to discriminate among various garbage categories. Experimental evaluations reveal that on our self-curated KRHO dataset, all performance metrics approach 1.00, and the overall classification accuracy reaches an impressive 99.33%, surpassing existing mainstream models. Moreover, on the public TrashNet dataset, the proposed method demonstrates equally outstanding classification performance and robustness, achieving an overall accuracy of 99.49%. Additionally, hyperparameter studies indicate that the model attains optimal performance with a learning rate of 2 × 10<sup>−4</sup>, a dropout rate of 0.3, an initial neighbor count of 20, and 8 attention heads. |
| format | Article |
| id | doaj-art-1bdc34c8019a44efa641dc15fa3ec1cd |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-1bdc34c8019a44efa641dc15fa3ec1cd2025-08-20T03:58:30ZengMDPI AGApplied Sciences2076-34172025-07-011514768810.3390/app15147688Dynamic Graph Neural Network for Garbage Classification Based on Multimodal Feature FusionYuhang Yang0Yuanqing Luo1Yingyu Yang2Shuang Kang3School of Environmental and Chemical Engineering, Shenyang University of Technology, Shenyang 110870, ChinaSchool of Environmental and Chemical Engineering, Shenyang University of Technology, Shenyang 110870, ChinaSchool of Environmental and Chemical Engineering, Shenyang University of Technology, Shenyang 110870, ChinaSchool of Mechanical and Control Engineering, Baicheng Normal University, Baicheng 137000, ChinaAmid the accelerating pace of global urbanization, the volume of municipal solid garbage has surged dramatically, thereby demanding more efficient and precise garbage management technologies. In this paper, we introduce a novel garbage classification approach that leverages a dynamic graph neural network based on multimodal feature fusion. Specifically, the proposed method employs an enhanced Residual Network Attention Module (RNAM) network to capture deep semantic features and utilizes CIELAB color (LAB) histograms to extract color distribution characteristics, achieving a complementary integration of multimodal information. An adaptive K-nearest neighbor algorithm is utilized to construct the dynamic graph structure, while the incorporation of a multi-head attention layer within the graph neural network facilitates the efficient aggregation of both local and global features. This design significantly enhances the model’s ability to discriminate among various garbage categories. Experimental evaluations reveal that on our self-curated KRHO dataset, all performance metrics approach 1.00, and the overall classification accuracy reaches an impressive 99.33%, surpassing existing mainstream models. Moreover, on the public TrashNet dataset, the proposed method demonstrates equally outstanding classification performance and robustness, achieving an overall accuracy of 99.49%. Additionally, hyperparameter studies indicate that the model attains optimal performance with a learning rate of 2 × 10<sup>−4</sup>, a dropout rate of 0.3, an initial neighbor count of 20, and 8 attention heads.https://www.mdpi.com/2076-3417/15/14/7688multimodal feature fusionadaptive K-nearest neighbor algorithmdynamic graph neural networkgarbage classification |
| spellingShingle | Yuhang Yang Yuanqing Luo Yingyu Yang Shuang Kang Dynamic Graph Neural Network for Garbage Classification Based on Multimodal Feature Fusion Applied Sciences multimodal feature fusion adaptive K-nearest neighbor algorithm dynamic graph neural network garbage classification |
| title | Dynamic Graph Neural Network for Garbage Classification Based on Multimodal Feature Fusion |
| title_full | Dynamic Graph Neural Network for Garbage Classification Based on Multimodal Feature Fusion |
| title_fullStr | Dynamic Graph Neural Network for Garbage Classification Based on Multimodal Feature Fusion |
| title_full_unstemmed | Dynamic Graph Neural Network for Garbage Classification Based on Multimodal Feature Fusion |
| title_short | Dynamic Graph Neural Network for Garbage Classification Based on Multimodal Feature Fusion |
| title_sort | dynamic graph neural network for garbage classification based on multimodal feature fusion |
| topic | multimodal feature fusion adaptive K-nearest neighbor algorithm dynamic graph neural network garbage classification |
| url | https://www.mdpi.com/2076-3417/15/14/7688 |
| work_keys_str_mv | AT yuhangyang dynamicgraphneuralnetworkforgarbageclassificationbasedonmultimodalfeaturefusion AT yuanqingluo dynamicgraphneuralnetworkforgarbageclassificationbasedonmultimodalfeaturefusion AT yingyuyang dynamicgraphneuralnetworkforgarbageclassificationbasedonmultimodalfeaturefusion AT shuangkang dynamicgraphneuralnetworkforgarbageclassificationbasedonmultimodalfeaturefusion |