Clean Our City: An Automatic Urban Garbage Classification Algorithm Using Computer Vision and Transfer Learning Technologies

To improve the quality of human life in the city, the first thing to solve is the problem of urban garbage. So far, the best way to solve this problem is garbage classification. At present, many algorithms have been put forward one after another. Previous research proposed some computer vision syste...

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Vydáno v:	Journal of physics. Conference series Ročník 1994; číslo 1; s. 12022 - 12032
Hlavní autoři:	Chen, Yuke, Han, Wendi, Jin, Jizhi, Wang, Hao, Xing, Qiaochu, Zhang, Yiwen
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Bristol IOP Publishing 01.08.2021
Témata:	Algorithms Classification Computer vision Datasets Garbage Image classification Machine learning Mechanical arms Model accuracy Vision systems
ISSN:	1742-6588, 1742-6596
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Abstract	To improve the quality of human life in the city, the first thing to solve is the problem of urban garbage. So far, the best way to solve this problem is garbage classification. At present, many algorithms have been put forward one after another. Previous research proposed some computer vision systems to solve the problem of urban garbage classification. In recent years, with the development of computer hardware and large-scale data sets, the algorithm based on depth learning has shown superior performance in the field of image classification. Thus, the features designed by traditional methods are gradually replaced, which far exceed these traditional image classification algorithms in classification accuracy. This study proposes an algorithm based on InceptionV3 networks and test the algorithm on a large-scale garbage classification data-set. The data set was divided into 80% training sets, 10% validation set, and 10% test set and use the transfer learning approach. The model achieved an accuracy of 93.125%, which solved image garbage classification very well. What is more, the algorithm can play an important role in the medical area and help control the mechanical arm.
AbstractList	To improve the quality of human life in the city, the first thing to solve is the problem of urban garbage. So far, the best way to solve this problem is garbage classification. At present, many algorithms have been put forward one after another. Previous research proposed some computer vision systems to solve the problem of urban garbage classification. In recent years, with the development of computer hardware and large-scale data sets, the algorithm based on depth learning has shown superior performance in the field of image classification. Thus, the features designed by traditional methods are gradually replaced, which far exceed these traditional image classification algorithms in classification accuracy. This study proposes an algorithm based on InceptionV3 networks and test the algorithm on a large-scale garbage classification data-set. The data set was divided into 80% training sets, 10% validation set, and 10% test set and use the transfer learning approach. The model achieved an accuracy of 93.125%, which solved image garbage classification very well. What is more, the algorithm can play an important role in the medical area and help control the mechanical arm.
Author	Han, Wendi Chen, Yuke Xing, Qiaochu Jin, Jizhi Zhang, Yiwen Wang, Hao
Author_xml	– sequence: 1 givenname: Yuke surname: Chen fullname: Chen, Yuke organization: Wuhan University of Technology , China – sequence: 2 givenname: Wendi surname: Han fullname: Han, Wendi organization: University of Notthingham , United Kingdom – sequence: 3 givenname: Jizhi surname: Jin fullname: Jin, Jizhi organization: University of Liverpool , United Kingdom – sequence: 4 givenname: Hao surname: Wang fullname: Wang, Hao organization: Dalian University of Technology , China – sequence: 5 givenname: Qiaochu surname: Xing fullname: Xing, Qiaochu organization: King’s college London, Strand , United Kingdom – sequence: 6 givenname: Yiwen surname: Zhang fullname: Zhang, Yiwen organization: Tianjin University , China
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SubjectTerms	Algorithms Classification Computer vision Datasets Garbage Image classification Machine learning Mechanical arms Model accuracy Vision systems
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Title	Clean Our City: An Automatic Urban Garbage Classification Algorithm Using Computer Vision and Transfer Learning Technologies
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