DEEP NEURAL NETWORKS AS A TOOL FOR ENHANCING THE EFFICIENCY OF PLASTIC WASTE SORTING
ТЕРЕҢ НЕЙРОНДЫҚ ЖЕЛІЛЕР ПЛАСТИКАЛЫҚ ҚАЛДЫҚТАРДЫ СҰРЫПТАУ ТИІМДІЛІГІН АРТТЫРУ ҚҰРАЛЫ РЕТІНДЕ
ГЛУБОКИЕ НЕЙРОННЫЕ СЕТИ КАК ИНСТРУМЕНТ ПОВЫШЕНИЯ ЭФФЕКТИВНОСТИ СОРТИРОВКИ ПЛАСТИКОВЫХ ОТХОДОВ
Alimbekova N. Hashim S. Zhumadillayeva A. Aiymbay S.
2024Kazakh-British Technical University
Herald of the Kazakh British Technical UNiversity
2024#21Issue 3116 - 127 pp.
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.
classification , convolutional neural network (CNN) , dataset , deep learning , plastic sorting
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L.N. Gumilyov Eurasian National University, Astana, 010008, Kazakhstan
Astana International University, Astana, 010000, Kazakhstan
Putra Malaysia University, Kuala-Lumpur, Malaysia
Astana IT University, Astana, 010000, Kazakhstan
L.N. Gumilyov Eurasian National University
Astana International University
Putra Malaysia University
Astana IT University
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026