Enhanced ResNet-50 for garbage classification: Feature fusion and depth-separable convolutions.
As people's material living standards continue to improve, the types and quantities of household garbage they generate rapidly increase. Therefore, it is urgent to develop a reasonable and effective method for garbage classification. This is important for resource recycling and environmental im...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0317999 |
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| _version_ | 1832540165087690752 |
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| author | Lingbo Li Runpu Wang Miaojie Zou Fusen Guo Yuheng Ren |
| author_facet | Lingbo Li Runpu Wang Miaojie Zou Fusen Guo Yuheng Ren |
| author_sort | Lingbo Li |
| collection | DOAJ |
| description | As people's material living standards continue to improve, the types and quantities of household garbage they generate rapidly increase. Therefore, it is urgent to develop a reasonable and effective method for garbage classification. This is important for resource recycling and environmental improvement and contributes to the sustainable development of production and the economy. However, existing deep learning-based garbage image classification models generally suffer from low classification accuracy, insufficient robustness, and slow detection speed due to the large number of model parameters. To this end, a new garbage image classification model is proposed, with the ResNet-50 network as the core architecture. Specifically, first, a redundancy-weighted feature fusion module is proposed, enabling the model to fully leverage valuable feature information, thereby improving its performance. At the same time, the module filters out redundant information from multi-scale features, reducing the number of model parameters. Second, the standard 3×3 convolutions in ResNet-50 are replaced with depth-separable convolutions, significantly improving the model's computational efficiency while preserving the feature extraction capability of the original convolutional structure. Finally, to address the issue of class imbalance, a weighting factor is added to the Focal Loss, aiming to mitigate the negative impact of class imbalance on model performance and enhance the model's robustness. Experimental results on the TrashNet dataset show that the proposed model effectively reduces the number of parameters, improves detection speed, and achieves an accuracy of 94.13%, surpassing the vast majority of existing deep learning-based waste image classification models, demonstrating its solid practical value. |
| format | Article |
| id | doaj-art-251c144179634ccd9cdd6eaa1eb19d29 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-251c144179634ccd9cdd6eaa1eb19d292025-02-05T05:32:05ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01201e031799910.1371/journal.pone.0317999Enhanced ResNet-50 for garbage classification: Feature fusion and depth-separable convolutions.Lingbo LiRunpu WangMiaojie ZouFusen GuoYuheng RenAs people's material living standards continue to improve, the types and quantities of household garbage they generate rapidly increase. Therefore, it is urgent to develop a reasonable and effective method for garbage classification. This is important for resource recycling and environmental improvement and contributes to the sustainable development of production and the economy. However, existing deep learning-based garbage image classification models generally suffer from low classification accuracy, insufficient robustness, and slow detection speed due to the large number of model parameters. To this end, a new garbage image classification model is proposed, with the ResNet-50 network as the core architecture. Specifically, first, a redundancy-weighted feature fusion module is proposed, enabling the model to fully leverage valuable feature information, thereby improving its performance. At the same time, the module filters out redundant information from multi-scale features, reducing the number of model parameters. Second, the standard 3×3 convolutions in ResNet-50 are replaced with depth-separable convolutions, significantly improving the model's computational efficiency while preserving the feature extraction capability of the original convolutional structure. Finally, to address the issue of class imbalance, a weighting factor is added to the Focal Loss, aiming to mitigate the negative impact of class imbalance on model performance and enhance the model's robustness. Experimental results on the TrashNet dataset show that the proposed model effectively reduces the number of parameters, improves detection speed, and achieves an accuracy of 94.13%, surpassing the vast majority of existing deep learning-based waste image classification models, demonstrating its solid practical value.https://doi.org/10.1371/journal.pone.0317999 |
| spellingShingle | Lingbo Li Runpu Wang Miaojie Zou Fusen Guo Yuheng Ren Enhanced ResNet-50 for garbage classification: Feature fusion and depth-separable convolutions. PLoS ONE |
| title | Enhanced ResNet-50 for garbage classification: Feature fusion and depth-separable convolutions. |
| title_full | Enhanced ResNet-50 for garbage classification: Feature fusion and depth-separable convolutions. |
| title_fullStr | Enhanced ResNet-50 for garbage classification: Feature fusion and depth-separable convolutions. |
| title_full_unstemmed | Enhanced ResNet-50 for garbage classification: Feature fusion and depth-separable convolutions. |
| title_short | Enhanced ResNet-50 for garbage classification: Feature fusion and depth-separable convolutions. |
| title_sort | enhanced resnet 50 for garbage classification feature fusion and depth separable convolutions |
| url | https://doi.org/10.1371/journal.pone.0317999 |
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