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...
Gespeichert in:
Veröffentlicht in: | IEEE access 2020, Vol.8, p.140019-140029 |
---|---|
Hauptverfasser: | , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 140029 |
---|---|
container_issue | |
container_start_page | 140019 |
container_title | IEEE access |
container_volume | 8 |
creator | Kang, Zhuang Yang, Jie Li, Guilan Zhang, Zeyi |
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. |
doi_str_mv | 10.1109/ACCESS.2020.3010496 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_crossref_primary_10_1109_ACCESS_2020_3010496</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>9144549</ieee_id><doaj_id>oai_doaj_org_article_0833483d3833446eb307cd33607b3e73</doaj_id><sourcerecordid>2454642723</sourcerecordid><originalsourceid>FETCH-LOGICAL-c474t-56d0b9f633037c79712810a51ceb0baf74d7a512e2d66ef4ad6eaf3b095d46e23</originalsourceid><addsrcrecordid>eNpNUMFuwjAMjaZNGmJ8AZdKO5clcZq0x65jDAlpB7ZzlDYuKoKWJeXA3y9dEZovtp_8nu1HyJzRBWM0e8mLYrndLjjldAGUUZHJOzLhTGYxJCDv_9WPZOb9noZIA5SoCcnzNsrPfXc0fVNFK-NKs8OoOBjvm7qpAtq10fbiezxGr8ajjUL_hniKNmhc27S7J_JQm4PH2TVPyff78qv4iDefq3WRb-JKKNHHibS0zGoJQEFVKlOMp4yahFVY0tLUSlgVOo7cSom1MFaiqaGkWWKFRA5Tsh51bWf2-uSao3EX3ZlG_wGd22njwhMH1DQFEClYGHIgl0BVZQEkVSWggqD1PGqdXPdzRt_rfXd2bThfc5EIKbjiwxSMU5XrvHdY37Yyqgfr9Wi9HqzXV-sDaz6yGkS8MTImgnAGv-Y8fKk</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2454642723</pqid></control><display><type>article</type><title>An Automatic Garbage Classification System Based on Deep Learning</title><source>IEEE Open Access Journals</source><source>DOAJ Directory of Open Access Journals</source><source>EZB-FREE-00999 freely available EZB journals</source><creator>Kang, Zhuang ; Yang, Jie ; Li, Guilan ; Zhang, Zeyi</creator><creatorcontrib>Kang, Zhuang ; Yang, Jie ; Li, Guilan ; Zhang, Zeyi</creatorcontrib><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.</description><identifier>ISSN: 2169-3536</identifier><identifier>EISSN: 2169-3536</identifier><identifier>DOI: 10.1109/ACCESS.2020.3010496</identifier><identifier>CODEN: IAECCG</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Algorithms ; Applications programs ; Artificial intelligence ; Classification ; Classification algorithms ; Convolution ; Deep learning ; Environmental protection ; Feature extraction ; Garbage collection ; Hardware ; Image classification ; Machine learning ; Mobile computing ; neural networks ; Servomotors ; Waste containers</subject><ispartof>IEEE access, 2020, Vol.8, p.140019-140029</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c474t-56d0b9f633037c79712810a51ceb0baf74d7a512e2d66ef4ad6eaf3b095d46e23</citedby><cites>FETCH-LOGICAL-c474t-56d0b9f633037c79712810a51ceb0baf74d7a512e2d66ef4ad6eaf3b095d46e23</cites><orcidid>0000-0001-9945-4472 ; 0000-0002-5511-705X ; 0000-0002-1839-252X ; 0000-0001-5191-2980</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9144549$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,864,2102,4024,27633,27923,27924,27925,54933</link.rule.ids></links><search><creatorcontrib>Kang, Zhuang</creatorcontrib><creatorcontrib>Yang, Jie</creatorcontrib><creatorcontrib>Li, Guilan</creatorcontrib><creatorcontrib>Zhang, Zeyi</creatorcontrib><title>An Automatic Garbage Classification System Based on Deep Learning</title><title>IEEE access</title><addtitle>Access</addtitle><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.</description><subject>Algorithms</subject><subject>Applications programs</subject><subject>Artificial intelligence</subject><subject>Classification</subject><subject>Classification algorithms</subject><subject>Convolution</subject><subject>Deep learning</subject><subject>Environmental protection</subject><subject>Feature extraction</subject><subject>Garbage collection</subject><subject>Hardware</subject><subject>Image classification</subject><subject>Machine learning</subject><subject>Mobile computing</subject><subject>neural networks</subject><subject>Servomotors</subject><subject>Waste containers</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNUMFuwjAMjaZNGmJ8AZdKO5clcZq0x65jDAlpB7ZzlDYuKoKWJeXA3y9dEZovtp_8nu1HyJzRBWM0e8mLYrndLjjldAGUUZHJOzLhTGYxJCDv_9WPZOb9noZIA5SoCcnzNsrPfXc0fVNFK-NKs8OoOBjvm7qpAtq10fbiezxGr8ajjUL_hniKNmhc27S7J_JQm4PH2TVPyff78qv4iDefq3WRb-JKKNHHibS0zGoJQEFVKlOMp4yahFVY0tLUSlgVOo7cSom1MFaiqaGkWWKFRA5Tsh51bWf2-uSao3EX3ZlG_wGd22njwhMH1DQFEClYGHIgl0BVZQEkVSWggqD1PGqdXPdzRt_rfXd2bThfc5EIKbjiwxSMU5XrvHdY37Yyqgfr9Wi9HqzXV-sDaz6yGkS8MTImgnAGv-Y8fKk</recordid><startdate>2020</startdate><enddate>2020</enddate><creator>Kang, Zhuang</creator><creator>Yang, Jie</creator><creator>Li, Guilan</creator><creator>Zhang, Zeyi</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>ESBDL</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7SR</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0001-9945-4472</orcidid><orcidid>https://orcid.org/0000-0002-5511-705X</orcidid><orcidid>https://orcid.org/0000-0002-1839-252X</orcidid><orcidid>https://orcid.org/0000-0001-5191-2980</orcidid></search><sort><creationdate>2020</creationdate><title>An Automatic Garbage Classification System Based on Deep Learning</title><author>Kang, Zhuang ; Yang, Jie ; Li, Guilan ; Zhang, Zeyi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c474t-56d0b9f633037c79712810a51ceb0baf74d7a512e2d66ef4ad6eaf3b095d46e23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Algorithms</topic><topic>Applications programs</topic><topic>Artificial intelligence</topic><topic>Classification</topic><topic>Classification algorithms</topic><topic>Convolution</topic><topic>Deep learning</topic><topic>Environmental protection</topic><topic>Feature extraction</topic><topic>Garbage collection</topic><topic>Hardware</topic><topic>Image classification</topic><topic>Machine learning</topic><topic>Mobile computing</topic><topic>neural networks</topic><topic>Servomotors</topic><topic>Waste containers</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kang, Zhuang</creatorcontrib><creatorcontrib>Yang, Jie</creatorcontrib><creatorcontrib>Li, Guilan</creatorcontrib><creatorcontrib>Zhang, Zeyi</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE Open Access Journals</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>IEEE access</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kang, Zhuang</au><au>Yang, Jie</au><au>Li, Guilan</au><au>Zhang, Zeyi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An Automatic Garbage Classification System Based on Deep Learning</atitle><jtitle>IEEE access</jtitle><stitle>Access</stitle><date>2020</date><risdate>2020</risdate><volume>8</volume><spage>140019</spage><epage>140029</epage><pages>140019-140029</pages><issn>2169-3536</issn><eissn>2169-3536</eissn><coden>IAECCG</coden><abstract>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.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2020.3010496</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0001-9945-4472</orcidid><orcidid>https://orcid.org/0000-0002-5511-705X</orcidid><orcidid>https://orcid.org/0000-0002-1839-252X</orcidid><orcidid>https://orcid.org/0000-0001-5191-2980</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 2169-3536 |
ispartof | IEEE access, 2020, Vol.8, p.140019-140029 |
issn | 2169-3536 2169-3536 |
language | eng |
recordid | cdi_crossref_primary_10_1109_ACCESS_2020_3010496 |
source | IEEE Open Access Journals; DOAJ Directory of Open Access Journals; EZB-FREE-00999 freely available EZB journals |
subjects | Algorithms Applications programs Artificial intelligence Classification Classification algorithms Convolution Deep learning Environmental protection Feature extraction Garbage collection Hardware Image classification Machine learning Mobile computing neural networks Servomotors Waste containers |
title | An Automatic Garbage Classification System Based on Deep Learning |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-23T07%3A06%3A08IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=An%20Automatic%20Garbage%20Classification%20System%20Based%20on%20Deep%20Learning&rft.jtitle=IEEE%20access&rft.au=Kang,%20Zhuang&rft.date=2020&rft.volume=8&rft.spage=140019&rft.epage=140029&rft.pages=140019-140029&rft.issn=2169-3536&rft.eissn=2169-3536&rft.coden=IAECCG&rft_id=info:doi/10.1109/ACCESS.2020.3010496&rft_dat=%3Cproquest_cross%3E2454642723%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2454642723&rft_id=info:pmid/&rft_ieee_id=9144549&rft_doaj_id=oai_doaj_org_article_0833483d3833446eb307cd33607b3e73&rfr_iscdi=true |