DEEPBIN: Deep Learning Based Garbage Classification for Households Using Sustainable Natural Technologies
Abstract Today, things that are accessible worldwide are upgrading to innovative technology. In this research, an intelligent garbage system will be designed with State-of-the-art methods using deep learning technologies. Garbage is highly produced due to urbanization and the rising population in ur...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Song, Yu [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Journal of grid computing - Springer Netherlands, 2003, 22(2023), 1 vom: 19. Dez. |
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| Übergeordnetes Werk: |
volume:22 ; year:2023 ; number:1 ; day:19 ; month:12 |
| Links: |
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| DOI / URN: |
10.1007/s10723-023-09722-6 |
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| Katalog-ID: |
SPR054130999 |
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| 520 | |a Abstract Today, things that are accessible worldwide are upgrading to innovative technology. In this research, an intelligent garbage system will be designed with State-of-the-art methods using deep learning technologies. Garbage is highly produced due to urbanization and the rising population in urban areas. It is essential to manage daily trash from homes and living environments. This research aims to provide an intelligent IoT-based garbage bin system, and classification is done using Deep learning techniques. This smart bin is capable of sensing more varieties of garbage from home. Though there are more technologies successfully implemented with IoT and machine learning, there is still a need for sustainable natural technologies to manage daily waste. The innovative IoT-based garbage system uses various sensors like humidity, temperature, gas, and liquid sensors to identify the garbage condition. Initially, the Smart Garbage Bin system is designed, and then the data are collected using a garbage annotation application. Next, the deep learning method is used for object detection and classification of garbage images. Arithmetic Optimization Algorithm (AOA) with Improved RefineDet (IRD) is used for object detection. Next, the EfficientNet-B0 model is used for the classification of garbage images. The garbage content is identified, and the content is prepared to train the deep learning model to perform efficient classification tasks. For result evaluation, smart bins are deployed in real-time, and accuracy is estimated. Furthermore, fine-tuning region-specific litter photos led to enhanced categorization. | ||
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| 650 | 4 | |a Sustainable technologies |7 (dpeaa)DE-He213 | |
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| 650 | 4 | |a IoT |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Sensors |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Household garbage management |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a He, Xin |4 aut | |
| 700 | 1 | |a Tang, Xiwang |4 aut | |
| 700 | 1 | |a Yin, Bo |4 aut | |
| 700 | 1 | |a Du, Jie |4 aut | |
| 700 | 1 | |a Liu, Jiali |4 aut | |
| 700 | 1 | |a Zhao, Zhongbao |4 aut | |
| 700 | 1 | |a Geng, Shigang |4 aut | |
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10.1007/s10723-023-09722-6 doi (DE-627)SPR054130999 (SPR)s10723-023-09722-6-e DE-627 ger DE-627 rakwb eng 510 004 VZ 54.25 bkl 54.32 bkl Song, Yu verfasserin aut DEEPBIN: Deep Learning Based Garbage Classification for Households Using Sustainable Natural Technologies 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Today, things that are accessible worldwide are upgrading to innovative technology. In this research, an intelligent garbage system will be designed with State-of-the-art methods using deep learning technologies. Garbage is highly produced due to urbanization and the rising population in urban areas. It is essential to manage daily trash from homes and living environments. This research aims to provide an intelligent IoT-based garbage bin system, and classification is done using Deep learning techniques. This smart bin is capable of sensing more varieties of garbage from home. Though there are more technologies successfully implemented with IoT and machine learning, there is still a need for sustainable natural technologies to manage daily waste. The innovative IoT-based garbage system uses various sensors like humidity, temperature, gas, and liquid sensors to identify the garbage condition. Initially, the Smart Garbage Bin system is designed, and then the data are collected using a garbage annotation application. Next, the deep learning method is used for object detection and classification of garbage images. Arithmetic Optimization Algorithm (AOA) with Improved RefineDet (IRD) is used for object detection. Next, the EfficientNet-B0 model is used for the classification of garbage images. The garbage content is identified, and the content is prepared to train the deep learning model to perform efficient classification tasks. For result evaluation, smart bins are deployed in real-time, and accuracy is estimated. Furthermore, fine-tuning region-specific litter photos led to enhanced categorization. Smart garbage bin (dpeaa)DE-He213 Sustainable technologies (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 IoT (dpeaa)DE-He213 Sensors (dpeaa)DE-He213 Household garbage management (dpeaa)DE-He213 He, Xin aut Tang, Xiwang aut Yin, Bo aut Du, Jie aut Liu, Jiali aut Zhao, Zhongbao aut Geng, Shigang aut Enthalten in Journal of grid computing Springer Netherlands, 2003 22(2023), 1 vom: 19. Dez. (DE-627)359787843 (DE-600)2098457-1 1572-9184 nnns volume:22 year:2023 number:1 day:19 month:12 https://dx.doi.org/10.1007/s10723-023-09722-6 lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-MAT GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.25 VZ 54.32 VZ AR 22 2023 1 19 12 |
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10.1007/s10723-023-09722-6 doi (DE-627)SPR054130999 (SPR)s10723-023-09722-6-e DE-627 ger DE-627 rakwb eng 510 004 VZ 54.25 bkl 54.32 bkl Song, Yu verfasserin aut DEEPBIN: Deep Learning Based Garbage Classification for Households Using Sustainable Natural Technologies 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Today, things that are accessible worldwide are upgrading to innovative technology. In this research, an intelligent garbage system will be designed with State-of-the-art methods using deep learning technologies. Garbage is highly produced due to urbanization and the rising population in urban areas. It is essential to manage daily trash from homes and living environments. This research aims to provide an intelligent IoT-based garbage bin system, and classification is done using Deep learning techniques. This smart bin is capable of sensing more varieties of garbage from home. Though there are more technologies successfully implemented with IoT and machine learning, there is still a need for sustainable natural technologies to manage daily waste. The innovative IoT-based garbage system uses various sensors like humidity, temperature, gas, and liquid sensors to identify the garbage condition. Initially, the Smart Garbage Bin system is designed, and then the data are collected using a garbage annotation application. Next, the deep learning method is used for object detection and classification of garbage images. Arithmetic Optimization Algorithm (AOA) with Improved RefineDet (IRD) is used for object detection. Next, the EfficientNet-B0 model is used for the classification of garbage images. The garbage content is identified, and the content is prepared to train the deep learning model to perform efficient classification tasks. For result evaluation, smart bins are deployed in real-time, and accuracy is estimated. Furthermore, fine-tuning region-specific litter photos led to enhanced categorization. Smart garbage bin (dpeaa)DE-He213 Sustainable technologies (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 IoT (dpeaa)DE-He213 Sensors (dpeaa)DE-He213 Household garbage management (dpeaa)DE-He213 He, Xin aut Tang, Xiwang aut Yin, Bo aut Du, Jie aut Liu, Jiali aut Zhao, Zhongbao aut Geng, Shigang aut Enthalten in Journal of grid computing Springer Netherlands, 2003 22(2023), 1 vom: 19. Dez. (DE-627)359787843 (DE-600)2098457-1 1572-9184 nnns volume:22 year:2023 number:1 day:19 month:12 https://dx.doi.org/10.1007/s10723-023-09722-6 lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-MAT GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.25 VZ 54.32 VZ AR 22 2023 1 19 12 |
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10.1007/s10723-023-09722-6 doi (DE-627)SPR054130999 (SPR)s10723-023-09722-6-e DE-627 ger DE-627 rakwb eng 510 004 VZ 54.25 bkl 54.32 bkl Song, Yu verfasserin aut DEEPBIN: Deep Learning Based Garbage Classification for Households Using Sustainable Natural Technologies 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Today, things that are accessible worldwide are upgrading to innovative technology. In this research, an intelligent garbage system will be designed with State-of-the-art methods using deep learning technologies. Garbage is highly produced due to urbanization and the rising population in urban areas. It is essential to manage daily trash from homes and living environments. This research aims to provide an intelligent IoT-based garbage bin system, and classification is done using Deep learning techniques. This smart bin is capable of sensing more varieties of garbage from home. Though there are more technologies successfully implemented with IoT and machine learning, there is still a need for sustainable natural technologies to manage daily waste. The innovative IoT-based garbage system uses various sensors like humidity, temperature, gas, and liquid sensors to identify the garbage condition. Initially, the Smart Garbage Bin system is designed, and then the data are collected using a garbage annotation application. Next, the deep learning method is used for object detection and classification of garbage images. Arithmetic Optimization Algorithm (AOA) with Improved RefineDet (IRD) is used for object detection. Next, the EfficientNet-B0 model is used for the classification of garbage images. The garbage content is identified, and the content is prepared to train the deep learning model to perform efficient classification tasks. For result evaluation, smart bins are deployed in real-time, and accuracy is estimated. Furthermore, fine-tuning region-specific litter photos led to enhanced categorization. Smart garbage bin (dpeaa)DE-He213 Sustainable technologies (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 IoT (dpeaa)DE-He213 Sensors (dpeaa)DE-He213 Household garbage management (dpeaa)DE-He213 He, Xin aut Tang, Xiwang aut Yin, Bo aut Du, Jie aut Liu, Jiali aut Zhao, Zhongbao aut Geng, Shigang aut Enthalten in Journal of grid computing Springer Netherlands, 2003 22(2023), 1 vom: 19. Dez. (DE-627)359787843 (DE-600)2098457-1 1572-9184 nnns volume:22 year:2023 number:1 day:19 month:12 https://dx.doi.org/10.1007/s10723-023-09722-6 lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-MAT GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.25 VZ 54.32 VZ AR 22 2023 1 19 12 |
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10.1007/s10723-023-09722-6 doi (DE-627)SPR054130999 (SPR)s10723-023-09722-6-e DE-627 ger DE-627 rakwb eng 510 004 VZ 54.25 bkl 54.32 bkl Song, Yu verfasserin aut DEEPBIN: Deep Learning Based Garbage Classification for Households Using Sustainable Natural Technologies 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Today, things that are accessible worldwide are upgrading to innovative technology. In this research, an intelligent garbage system will be designed with State-of-the-art methods using deep learning technologies. Garbage is highly produced due to urbanization and the rising population in urban areas. It is essential to manage daily trash from homes and living environments. This research aims to provide an intelligent IoT-based garbage bin system, and classification is done using Deep learning techniques. This smart bin is capable of sensing more varieties of garbage from home. Though there are more technologies successfully implemented with IoT and machine learning, there is still a need for sustainable natural technologies to manage daily waste. The innovative IoT-based garbage system uses various sensors like humidity, temperature, gas, and liquid sensors to identify the garbage condition. Initially, the Smart Garbage Bin system is designed, and then the data are collected using a garbage annotation application. Next, the deep learning method is used for object detection and classification of garbage images. Arithmetic Optimization Algorithm (AOA) with Improved RefineDet (IRD) is used for object detection. Next, the EfficientNet-B0 model is used for the classification of garbage images. The garbage content is identified, and the content is prepared to train the deep learning model to perform efficient classification tasks. For result evaluation, smart bins are deployed in real-time, and accuracy is estimated. Furthermore, fine-tuning region-specific litter photos led to enhanced categorization. Smart garbage bin (dpeaa)DE-He213 Sustainable technologies (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 IoT (dpeaa)DE-He213 Sensors (dpeaa)DE-He213 Household garbage management (dpeaa)DE-He213 He, Xin aut Tang, Xiwang aut Yin, Bo aut Du, Jie aut Liu, Jiali aut Zhao, Zhongbao aut Geng, Shigang aut Enthalten in Journal of grid computing Springer Netherlands, 2003 22(2023), 1 vom: 19. Dez. (DE-627)359787843 (DE-600)2098457-1 1572-9184 nnns volume:22 year:2023 number:1 day:19 month:12 https://dx.doi.org/10.1007/s10723-023-09722-6 lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-MAT GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.25 VZ 54.32 VZ AR 22 2023 1 19 12 |
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10.1007/s10723-023-09722-6 doi (DE-627)SPR054130999 (SPR)s10723-023-09722-6-e DE-627 ger DE-627 rakwb eng 510 004 VZ 54.25 bkl 54.32 bkl Song, Yu verfasserin aut DEEPBIN: Deep Learning Based Garbage Classification for Households Using Sustainable Natural Technologies 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Today, things that are accessible worldwide are upgrading to innovative technology. In this research, an intelligent garbage system will be designed with State-of-the-art methods using deep learning technologies. Garbage is highly produced due to urbanization and the rising population in urban areas. It is essential to manage daily trash from homes and living environments. This research aims to provide an intelligent IoT-based garbage bin system, and classification is done using Deep learning techniques. This smart bin is capable of sensing more varieties of garbage from home. Though there are more technologies successfully implemented with IoT and machine learning, there is still a need for sustainable natural technologies to manage daily waste. The innovative IoT-based garbage system uses various sensors like humidity, temperature, gas, and liquid sensors to identify the garbage condition. Initially, the Smart Garbage Bin system is designed, and then the data are collected using a garbage annotation application. Next, the deep learning method is used for object detection and classification of garbage images. Arithmetic Optimization Algorithm (AOA) with Improved RefineDet (IRD) is used for object detection. Next, the EfficientNet-B0 model is used for the classification of garbage images. The garbage content is identified, and the content is prepared to train the deep learning model to perform efficient classification tasks. For result evaluation, smart bins are deployed in real-time, and accuracy is estimated. Furthermore, fine-tuning region-specific litter photos led to enhanced categorization. Smart garbage bin (dpeaa)DE-He213 Sustainable technologies (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 IoT (dpeaa)DE-He213 Sensors (dpeaa)DE-He213 Household garbage management (dpeaa)DE-He213 He, Xin aut Tang, Xiwang aut Yin, Bo aut Du, Jie aut Liu, Jiali aut Zhao, Zhongbao aut Geng, Shigang aut Enthalten in Journal of grid computing Springer Netherlands, 2003 22(2023), 1 vom: 19. Dez. (DE-627)359787843 (DE-600)2098457-1 1572-9184 nnns volume:22 year:2023 number:1 day:19 month:12 https://dx.doi.org/10.1007/s10723-023-09722-6 lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-MAT GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.25 VZ 54.32 VZ AR 22 2023 1 19 12 |
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Today, things that are accessible worldwide are upgrading to innovative technology. In this research, an intelligent garbage system will be designed with State-of-the-art methods using deep learning technologies. Garbage is highly produced due to urbanization and the rising population in urban areas. It is essential to manage daily trash from homes and living environments. This research aims to provide an intelligent IoT-based garbage bin system, and classification is done using Deep learning techniques. This smart bin is capable of sensing more varieties of garbage from home. Though there are more technologies successfully implemented with IoT and machine learning, there is still a need for sustainable natural technologies to manage daily waste. The innovative IoT-based garbage system uses various sensors like humidity, temperature, gas, and liquid sensors to identify the garbage condition. Initially, the Smart Garbage Bin system is designed, and then the data are collected using a garbage annotation application. Next, the deep learning method is used for object detection and classification of garbage images. Arithmetic Optimization Algorithm (AOA) with Improved RefineDet (IRD) is used for object detection. Next, the EfficientNet-B0 model is used for the classification of garbage images. The garbage content is identified, and the content is prepared to train the deep learning model to perform efficient classification tasks. 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deepbin: deep learning based garbage classification for households using sustainable natural technologies |
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DEEPBIN: Deep Learning Based Garbage Classification for Households Using Sustainable Natural Technologies |
| abstract |
Abstract Today, things that are accessible worldwide are upgrading to innovative technology. In this research, an intelligent garbage system will be designed with State-of-the-art methods using deep learning technologies. Garbage is highly produced due to urbanization and the rising population in urban areas. It is essential to manage daily trash from homes and living environments. This research aims to provide an intelligent IoT-based garbage bin system, and classification is done using Deep learning techniques. This smart bin is capable of sensing more varieties of garbage from home. Though there are more technologies successfully implemented with IoT and machine learning, there is still a need for sustainable natural technologies to manage daily waste. The innovative IoT-based garbage system uses various sensors like humidity, temperature, gas, and liquid sensors to identify the garbage condition. Initially, the Smart Garbage Bin system is designed, and then the data are collected using a garbage annotation application. Next, the deep learning method is used for object detection and classification of garbage images. Arithmetic Optimization Algorithm (AOA) with Improved RefineDet (IRD) is used for object detection. Next, the EfficientNet-B0 model is used for the classification of garbage images. The garbage content is identified, and the content is prepared to train the deep learning model to perform efficient classification tasks. For result evaluation, smart bins are deployed in real-time, and accuracy is estimated. Furthermore, fine-tuning region-specific litter photos led to enhanced categorization. © The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract Today, things that are accessible worldwide are upgrading to innovative technology. In this research, an intelligent garbage system will be designed with State-of-the-art methods using deep learning technologies. Garbage is highly produced due to urbanization and the rising population in urban areas. It is essential to manage daily trash from homes and living environments. This research aims to provide an intelligent IoT-based garbage bin system, and classification is done using Deep learning techniques. This smart bin is capable of sensing more varieties of garbage from home. Though there are more technologies successfully implemented with IoT and machine learning, there is still a need for sustainable natural technologies to manage daily waste. The innovative IoT-based garbage system uses various sensors like humidity, temperature, gas, and liquid sensors to identify the garbage condition. Initially, the Smart Garbage Bin system is designed, and then the data are collected using a garbage annotation application. Next, the deep learning method is used for object detection and classification of garbage images. Arithmetic Optimization Algorithm (AOA) with Improved RefineDet (IRD) is used for object detection. Next, the EfficientNet-B0 model is used for the classification of garbage images. The garbage content is identified, and the content is prepared to train the deep learning model to perform efficient classification tasks. For result evaluation, smart bins are deployed in real-time, and accuracy is estimated. Furthermore, fine-tuning region-specific litter photos led to enhanced categorization. © The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract Today, things that are accessible worldwide are upgrading to innovative technology. In this research, an intelligent garbage system will be designed with State-of-the-art methods using deep learning technologies. Garbage is highly produced due to urbanization and the rising population in urban areas. It is essential to manage daily trash from homes and living environments. This research aims to provide an intelligent IoT-based garbage bin system, and classification is done using Deep learning techniques. This smart bin is capable of sensing more varieties of garbage from home. Though there are more technologies successfully implemented with IoT and machine learning, there is still a need for sustainable natural technologies to manage daily waste. The innovative IoT-based garbage system uses various sensors like humidity, temperature, gas, and liquid sensors to identify the garbage condition. Initially, the Smart Garbage Bin system is designed, and then the data are collected using a garbage annotation application. Next, the deep learning method is used for object detection and classification of garbage images. Arithmetic Optimization Algorithm (AOA) with Improved RefineDet (IRD) is used for object detection. Next, the EfficientNet-B0 model is used for the classification of garbage images. The garbage content is identified, and the content is prepared to train the deep learning model to perform efficient classification tasks. For result evaluation, smart bins are deployed in real-time, and accuracy is estimated. Furthermore, fine-tuning region-specific litter photos led to enhanced categorization. © The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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DEEPBIN: Deep Learning Based Garbage Classification for Households Using Sustainable Natural Technologies |
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He, Xin Tang, Xiwang Yin, Bo Du, Jie Liu, Jiali Zhao, Zhongbao Geng, Shigang |
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|
| score |
7.398144 |