kaz29Вестник Казахстанско-Британского технического университетаHerald of the Kazakh-British Technical University1998-66882959-8109Казахстанско-Британский Технический Университет10.55452/1998-6688-2024-21-3-116-127kaz29-1373Research ArticleКОМПЬЮТЕРНЫЕ НАУКИCOMPUTER SCIENCEГЛУБОКИЕ НЕЙРОННЫЕ СЕТИ КАК ИНСТРУМЕНТ ПОВЫШЕНИЯ ЭФФЕКТИВНОСТИ СОРТИРОВКИ ПЛАСТИКОВЫХ ОТХОДОВDEEP NEURAL NETWORKS AS A TOOL FOR ENHANCING THE EFFICIENCY OF PLASTIC WASTE SORTINGhttps://orcid.org/0000-0002-8939-8806АлимбековаН.AlimbekovaN.

докторант 

010008, г. Астана;010000, г. Астана

PhD student 

010008, Astana;010000, Astana

nazakhatovna@gmail.comhttps://orcid.org/0000-0002-4436-897XХашимШ.HashimSh.

PhD, профессор 

Куала-Лумпур

PhD, professor 

Kuala-Lumpur

sjh@upm.edu.myhttps://orcid.org/0000-0003-1042-0415ЖумадиллаеваА.ZhumadillayevaA.

к.т.н,, доцент 

010008, г. Астана

Candidate of Technical Sciences, associate professor 

010008, Astana;010000, Astana

zhumadillayeva_ak@enu.kzhttps://orcid.org/0000-0002-2363-911XАйымбайС.AiymbayS.

м.т.н. 

010000, г. Астана

Master of Technical Sciences 

010000, Astana

sungat.aktau@gmail.comЕвразийский национальный университет им. Л.Н. Гумилева; Международный университет АстанаКазахстанL.N. Gumilyov Eurasian National University; Astana International UniversityKazakhstanУниверситет Путра МалайзияМалайзияPutra Malaysia UniversityMalaysiaЕвразийский национальный университет им. Л.Н. ГумилеваКазахстанL.N. Gumilyov Eurasian National University; Astana IT UniversityKazakhstanAstana IT UniversityКазахстанAstana IT UniversityKazakhstan202401102024213116127Copyright © Алимбекова Н., Хашим Ш., Жумадиллаева А., Айымбай С., 20242024Алимбекова Н., Хашим Ш., Жумадиллаева А., Айымбай С.Alimbekova N., Hashim S., Zhumadillayeva A., Aiymbay S.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://vestnik.kbtu.edu.kz/jour/article/view/1373

В индустрии вторсырья наблюдается острая потребность в качественном сортированном материале. Проблематика сортировочных центров, связанная с трудностями сортировки и очистки пластика, приводит к накоплению отходов на свалках вместо их переработки, подчеркивая необходимость развития эффективных автоматизированных методов сортировки. В этом исследовании предлагается интеллектуальная модель классификации пластиков, разработанная на основе сверточной нейронной сети (CNN) с использованием таких архитектур, как MobileNet, ResNet и EfficientNet. Модели были обучены на наборе данных, состоящем из более чем 4000 изображений, распределенных по пяти категориям пластика. Среди протестированных архитектур EfficientNet-SED продемонстрировала самую высокую точность классификации – 99,1%, что соответствует результатам предыдущих исследований в этой области. Эти результаты подчеркивают потенциал использования передовых архитектур CNN для повышения эффективности процессов переработки пластика.

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.

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The authors declare that there are no conflicts of interest present.