Waste Classification for Sustainable Development Using Image Recognition with Deep Learning Neural Network Models
<p dir="ltr">The proper handling of waste is one of the biggest challenges of modern society. Municipal Solid Waste (MSW) requires categorization into a number of types, including bio, plastic, glass, metal, paper, etc. The most efficient techniques proposed by researchers so far inc...
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2022
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| _version_ | 1864513531082178560 |
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| author | Meena Malik (17542089) |
| author2 | Sachin Sharma (25799) Mueen Uddin (4903510) Chin-Ling Chen (17542074) Chih-Ming Wu (17542092) Punit Soni (17542095) Shikha Chaudhary (14145747) |
| author2_role | author author author author author author |
| author_facet | Meena Malik (17542089) Sachin Sharma (25799) Mueen Uddin (4903510) Chin-Ling Chen (17542074) Chih-Ming Wu (17542092) Punit Soni (17542095) Shikha Chaudhary (14145747) |
| author_role | author |
| dc.creator.none.fl_str_mv | Meena Malik (17542089) Sachin Sharma (25799) Mueen Uddin (4903510) Chin-Ling Chen (17542074) Chih-Ming Wu (17542092) Punit Soni (17542095) Shikha Chaudhary (14145747) |
| dc.date.none.fl_str_mv | 2022-06-13T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3390/su14127222 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Waste_Classification_for_Sustainable_Development_Using_Image_Recognition_with_Deep_Learning_Neural_Network_Models/24717537 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Environmental engineering Information and computing sciences Artificial intelligence Machine learning litter classification convolution neural networks machine learning EfficientNet-B0 |
| dc.title.none.fl_str_mv | Waste Classification for Sustainable Development Using Image Recognition with Deep Learning Neural Network Models |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">The proper handling of waste is one of the biggest challenges of modern society. Municipal Solid Waste (MSW) requires categorization into a number of types, including bio, plastic, glass, metal, paper, etc. The most efficient techniques proposed by researchers so far include neural networks. In this paper, a detailed summarization was made of the existing deep learning techniques that have been proposed to classify waste. This paper proposes an architecture for the classification of litter into the categories specified in the benchmark approaches. The architecture used for classification was EfficientNet-B0. These are compound-scaling based models proposed by Google that are pretrained on ImageNet and have an accuracy of 74% to 84% in top-1 over ImageNet. This research proposes EfficientNet-B0 model tuning for images specific to particular demographic regions for efficient classification. This type of model tuning over transfer learning provides a customized model for classification, highly optimized for a particular region. It was shown that such a model had comparable accuracy to that of EfficientNet-B3, however, with a significantly smaller number of parameters required by the B3 model. Thus, the proposed technique achieved efficiency on the order of 4X in terms of FLOPS. Moreover, it resulted in improvised classifications as a result of fine-tuning over region-wise specific litter images.</p><h2>Other Information</h2><p dir="ltr">Published in: Sustainability<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/su14127222" target="_blank">https://dx.doi.org/10.3390/su14127222</a></p><p dir="ltr">Disclaimer: The University of Doha for Science and Technology replaced the now-former College of the North Atlantic-Qatar after an Amiri decision in 2022. UDST has become and first national applied University in Qatar; it is also second national University in the country.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_44c22ec8ea207ec10a4543e948970a9f |
| identifier_str_mv | 10.3390/su14127222 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24717537 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Waste Classification for Sustainable Development Using Image Recognition with Deep Learning Neural Network ModelsMeena Malik (17542089)Sachin Sharma (25799)Mueen Uddin (4903510)Chin-Ling Chen (17542074)Chih-Ming Wu (17542092)Punit Soni (17542095)Shikha Chaudhary (14145747)EngineeringEnvironmental engineeringInformation and computing sciencesArtificial intelligenceMachine learninglitter classificationconvolution neural networksmachine learningEfficientNet-B0<p dir="ltr">The proper handling of waste is one of the biggest challenges of modern society. Municipal Solid Waste (MSW) requires categorization into a number of types, including bio, plastic, glass, metal, paper, etc. The most efficient techniques proposed by researchers so far include neural networks. In this paper, a detailed summarization was made of the existing deep learning techniques that have been proposed to classify waste. This paper proposes an architecture for the classification of litter into the categories specified in the benchmark approaches. The architecture used for classification was EfficientNet-B0. These are compound-scaling based models proposed by Google that are pretrained on ImageNet and have an accuracy of 74% to 84% in top-1 over ImageNet. This research proposes EfficientNet-B0 model tuning for images specific to particular demographic regions for efficient classification. This type of model tuning over transfer learning provides a customized model for classification, highly optimized for a particular region. It was shown that such a model had comparable accuracy to that of EfficientNet-B3, however, with a significantly smaller number of parameters required by the B3 model. Thus, the proposed technique achieved efficiency on the order of 4X in terms of FLOPS. Moreover, it resulted in improvised classifications as a result of fine-tuning over region-wise specific litter images.</p><h2>Other Information</h2><p dir="ltr">Published in: Sustainability<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/su14127222" target="_blank">https://dx.doi.org/10.3390/su14127222</a></p><p dir="ltr">Disclaimer: The University of Doha for Science and Technology replaced the now-former College of the North Atlantic-Qatar after an Amiri decision in 2022. UDST has become and first national applied University in Qatar; it is also second national University in the country.</p>2022-06-13T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/su14127222https://figshare.com/articles/journal_contribution/Waste_Classification_for_Sustainable_Development_Using_Image_Recognition_with_Deep_Learning_Neural_Network_Models/24717537CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/247175372022-06-13T03:00:00Z |
| spellingShingle | Waste Classification for Sustainable Development Using Image Recognition with Deep Learning Neural Network Models Meena Malik (17542089) Engineering Environmental engineering Information and computing sciences Artificial intelligence Machine learning litter classification convolution neural networks machine learning EfficientNet-B0 |
| status_str | publishedVersion |
| title | Waste Classification for Sustainable Development Using Image Recognition with Deep Learning Neural Network Models |
| title_full | Waste Classification for Sustainable Development Using Image Recognition with Deep Learning Neural Network Models |
| title_fullStr | Waste Classification for Sustainable Development Using Image Recognition with Deep Learning Neural Network Models |
| title_full_unstemmed | Waste Classification for Sustainable Development Using Image Recognition with Deep Learning Neural Network Models |
| title_short | Waste Classification for Sustainable Development Using Image Recognition with Deep Learning Neural Network Models |
| title_sort | Waste Classification for Sustainable Development Using Image Recognition with Deep Learning Neural Network Models |
| topic | Engineering Environmental engineering Information and computing sciences Artificial intelligence Machine learning litter classification convolution neural networks machine learning EfficientNet-B0 |