Waste Classification for Sustainable Development Using Image Recognition with Deep Learning Neural Network Models Article Swipe
Meena Malik
,
Sachin Sharma
,
Mueen Uddin
,
Chin‐Ling Chen
,
Chih-Ming Wu
,
Punit Soni
,
Shikha Chaudhary
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/su14127222
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/su14127222
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.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/su14127222
- https://www.mdpi.com/2071-1050/14/12/7222/pdf?version=1658817156
- OA Status
- gold
- Cited By
- 129
- References
- 24
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4282831800
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https://openalex.org/W4282831800Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/su14127222Digital Object Identifier
- Title
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Waste Classification for Sustainable Development Using Image Recognition with Deep Learning Neural Network ModelsWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-06-13Full publication date if available
- Authors
-
Meena Malik, Sachin Sharma, Mueen Uddin, Chin‐Ling Chen, Chih-Ming Wu, Punit Soni, Shikha ChaudharyList of authors in order
- Landing page
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https://doi.org/10.3390/su14127222Publisher landing page
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https://www.mdpi.com/2071-1050/14/12/7222/pdf?version=1658817156Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2071-1050/14/12/7222/pdf?version=1658817156Direct OA link when available
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Computer science, Artificial intelligence, Categorization, Contextual image classification, Artificial neural network, Transfer of learning, Automatic summarization, Machine learning, Benchmark (surveying), Scalability, Deep learning, Image (mathematics), Database, Geography, GeodesyTop concepts (fields/topics) attached by OpenAlex
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129Total citation count in OpenAlex
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2025: 56, 2024: 46, 2023: 25, 2022: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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