Intelligent garbage classification system based on improve MobileNetV3-Large
In response to the call for implementing national waste classification, this paper proposes an intelligent waste classification system based on the improved MobileNetV3-Large, which can raise the national awareness of waste classification through the combination of software and hardware. The softwar...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-12-01
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| Series: | Connection Science |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/09540091.2022.2067127 |
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| _version_ | 1849414185445752832 |
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| author | Yi Zhao Hancheng Huang Zhixiang Li Huang Yiwang Manjie Lu |
| author_facet | Yi Zhao Hancheng Huang Zhixiang Li Huang Yiwang Manjie Lu |
| author_sort | Yi Zhao |
| collection | DOAJ |
| description | In response to the call for implementing national waste classification, this paper proposes an intelligent waste classification system based on the improved MobileNetV3-Large, which can raise the national awareness of waste classification through the combination of software and hardware. The software module is based on WeChat applet and offers functions for image recognition, text recognition, speech recognition, points-based quiz and so on. The hardware module is based on Raspberry Pi and covers image shooting, image recognition, automatic classification with automatic announcement and so on. The algorithm model applied to the image classification adopts a network model based on MobileNetV3-Large. This network model is enabled to classify garbage images through deep separable convolution, inverse residual structure, lightweight attention structure and the hard_ swish activation function. The text classification model adopts a network model based on LSTM, extracts text features through word embedding, enhancing the effect of garbage text classification. After testing, the system can leverage deep learning to realise intelligent garbage classification. The image recognition accuracy of the algorithm model was found to reach 81%, while the text recognition accuracy was as high as 97.61%. |
| format | Article |
| id | doaj-art-67e5c9c7539640cda4e0ee1d7af3d584 |
| institution | Kabale University |
| issn | 0954-0091 1360-0494 |
| language | English |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj-art-67e5c9c7539640cda4e0ee1d7af3d5842025-08-20T03:33:54ZengTaylor & Francis GroupConnection Science0954-00911360-04942022-12-013411299132110.1080/09540091.2022.20671272067127Intelligent garbage classification system based on improve MobileNetV3-LargeYi Zhao0Hancheng Huang1Zhixiang Li2Huang Yiwang3Manjie Lu4Guangdong Ocean UniversityGuangdong Ocean UniversityGuangdong Ocean UniversityTongren UniversityGuangdong Ocean UniversityIn response to the call for implementing national waste classification, this paper proposes an intelligent waste classification system based on the improved MobileNetV3-Large, which can raise the national awareness of waste classification through the combination of software and hardware. The software module is based on WeChat applet and offers functions for image recognition, text recognition, speech recognition, points-based quiz and so on. The hardware module is based on Raspberry Pi and covers image shooting, image recognition, automatic classification with automatic announcement and so on. The algorithm model applied to the image classification adopts a network model based on MobileNetV3-Large. This network model is enabled to classify garbage images through deep separable convolution, inverse residual structure, lightweight attention structure and the hard_ swish activation function. The text classification model adopts a network model based on LSTM, extracts text features through word embedding, enhancing the effect of garbage text classification. After testing, the system can leverage deep learning to realise intelligent garbage classification. The image recognition accuracy of the algorithm model was found to reach 81%, while the text recognition accuracy was as high as 97.61%.http://dx.doi.org/10.1080/09540091.2022.2067127deep learninggarbage classificationmobilenetv3-largelstmcombination of software and hardware |
| spellingShingle | Yi Zhao Hancheng Huang Zhixiang Li Huang Yiwang Manjie Lu Intelligent garbage classification system based on improve MobileNetV3-Large Connection Science deep learning garbage classification mobilenetv3-large lstm combination of software and hardware |
| title | Intelligent garbage classification system based on improve MobileNetV3-Large |
| title_full | Intelligent garbage classification system based on improve MobileNetV3-Large |
| title_fullStr | Intelligent garbage classification system based on improve MobileNetV3-Large |
| title_full_unstemmed | Intelligent garbage classification system based on improve MobileNetV3-Large |
| title_short | Intelligent garbage classification system based on improve MobileNetV3-Large |
| title_sort | intelligent garbage classification system based on improve mobilenetv3 large |
| topic | deep learning garbage classification mobilenetv3-large lstm combination of software and hardware |
| url | http://dx.doi.org/10.1080/09540091.2022.2067127 |
| work_keys_str_mv | AT yizhao intelligentgarbageclassificationsystembasedonimprovemobilenetv3large AT hanchenghuang intelligentgarbageclassificationsystembasedonimprovemobilenetv3large AT zhixiangli intelligentgarbageclassificationsystembasedonimprovemobilenetv3large AT huangyiwang intelligentgarbageclassificationsystembasedonimprovemobilenetv3large AT manjielu intelligentgarbageclassificationsystembasedonimprovemobilenetv3large |