An Automatic Garbage Classification System Based on Deep Learning
Garbage classification has always been an important issue in environmental protection, resource recycling and social livelihood. In order to improve the efficiency of front-end garbage collection, an automatic garbage classification system is proposed based on deep learning. Firstly, the overall sys...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9144549/ |
| _version_ | 1818444585337094144 |
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| author | Zhuang Kang Jie Yang Guilan Li Zeyi Zhang |
| author_facet | Zhuang Kang Jie Yang Guilan Li Zeyi Zhang |
| author_sort | Zhuang Kang |
| collection | DOAJ |
| description | Garbage classification has always been an important issue in environmental protection, resource recycling and social livelihood. In order to improve the efficiency of front-end garbage collection, an automatic garbage classification system is proposed based on deep learning. Firstly, the overall system of the garbage bin is designed, including the hardware structure and the mobile app. Secondly, the proposed garbage classification algorithm is based on ResNet-34 algorithm, and its network structure is further optimized by three aspects, including the multi feature fusion of input images, the feature reuse of the residual unit, and the design of a new activation function. Finally, the superiority of the proposed classification algorithm is verified with the constructed garbage data. The classification accuracy of the proposed algorithm is enhanced by 1.01%. The experimental results show that the classification accuracy is as high as 99%, the classification cycle of the system is as quick as 0.95 s. |
| first_indexed | 2024-12-14T19:18:16Z |
| format | Article |
| id | doaj.art-0833483d3833446eb307cd33607b3e73 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T19:18:16Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-0833483d3833446eb307cd33607b3e732022-12-21T22:50:26ZengIEEEIEEE Access2169-35362020-01-01814001914002910.1109/ACCESS.2020.30104969144549An Automatic Garbage Classification System Based on Deep LearningZhuang Kang0https://orcid.org/0000-0002-5511-705XJie Yang1https://orcid.org/0000-0002-1839-252XGuilan Li2https://orcid.org/0000-0001-9945-4472Zeyi Zhang3https://orcid.org/0000-0001-5191-2980School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou, ChinaSchool of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou, ChinaSchool of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou, ChinaSchool of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou, ChinaGarbage classification has always been an important issue in environmental protection, resource recycling and social livelihood. In order to improve the efficiency of front-end garbage collection, an automatic garbage classification system is proposed based on deep learning. Firstly, the overall system of the garbage bin is designed, including the hardware structure and the mobile app. Secondly, the proposed garbage classification algorithm is based on ResNet-34 algorithm, and its network structure is further optimized by three aspects, including the multi feature fusion of input images, the feature reuse of the residual unit, and the design of a new activation function. Finally, the superiority of the proposed classification algorithm is verified with the constructed garbage data. The classification accuracy of the proposed algorithm is enhanced by 1.01%. The experimental results show that the classification accuracy is as high as 99%, the classification cycle of the system is as quick as 0.95 s.https://ieeexplore.ieee.org/document/9144549/Artificial intelligencedeep learningimage classificationneural networks |
| spellingShingle | Zhuang Kang Jie Yang Guilan Li Zeyi Zhang An Automatic Garbage Classification System Based on Deep Learning IEEE Access Artificial intelligence deep learning image classification neural networks |
| title | An Automatic Garbage Classification System Based on Deep Learning |
| title_full | An Automatic Garbage Classification System Based on Deep Learning |
| title_fullStr | An Automatic Garbage Classification System Based on Deep Learning |
| title_full_unstemmed | An Automatic Garbage Classification System Based on Deep Learning |
| title_short | An Automatic Garbage Classification System Based on Deep Learning |
| title_sort | automatic garbage classification system based on deep learning |
| topic | Artificial intelligence deep learning image classification neural networks |
| url | https://ieeexplore.ieee.org/document/9144549/ |
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