DEEP NEURAL NETWORKS AS A TOOL FOR ENHANCING THE EFFICIENCY OF PLASTIC WASTE SORTING
https://doi.org/10.55452/1998-6688-2024-21-3-116-127
Abstract
In the recycling industry, there is an urgent need for high-quality sorted material. The problems of sorting centers related to the difficulties of sorting and cleaning plastic leads to the accumulation of waste in landfills instead of recycling, emphasizing the need to develop effective automated sorting methods. This study proposes an intelligent plastic classification model developed on the basis of a convolutional neural network (CNN) using architectures such as MobileNet, ResNet and EfficientNet. The models were trained on a dataset of more than 4,000 images distributed across five categories of plastic. Among the tested architectures, proposed EfficientNet-SED demonstrated the highest classification accuracy – 99.1%, which corresponds to the results of previous research in this area. These findings highlight the potential of using advanced CNN architectures to improve the efficiency of plastic recycling processes.
Keywords
Supporting agencies: The research data was sponsored by the Science Committee of the Minister of Science and Higher Education of the Republic of Kazakhstan (Grant No.76 of the research fund AP22685518).
About the Authors
N. Alimbekova
L.N. Gumilyov Eurasian National University;
Astana International University
Kazakhstan
Kazakhstan
PhD student
010008, Astana;
010000, Astana
Sh. Hashim
Putra Malaysia University
Malaysia
Malaysia
PhD, professor
Kuala-Lumpur
A. Zhumadillayeva
L.N. Gumilyov Eurasian National University;
Astana IT University
Kazakhstan
Kazakhstan
Candidate of Technical Sciences, associate professor
010008, Astana;
010000, Astana
S. Aiymbay
Astana IT University
Kazakhstan
Kazakhstan
Master of Technical Sciences
010000, Astana
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Review
For citations:
Alimbekova N., Hashim Sh., Zhumadillayeva A., Aiymbay S. DEEP NEURAL NETWORKS AS A TOOL FOR ENHANCING THE EFFICIENCY OF PLASTIC WASTE SORTING. Herald of the Kazakh-British technical university. 2024;21(3):116-127. https://doi.org/10.55452/1998-6688-2024-21-3-116-127
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