Optimization of Several Deep CNN Models for Waste Classification

Mahir Kaya; Samet Ulutürk; Yasemin Çetin Kaya; Onur Altıntaş; Bülent Turan

doi:10.35377/saucis...1257100

EN

Optimization of Several Deep CNN Models for Waste Classification

Abstract

With urbanization, population, and consumption on the rise, urban waste generation is steadily increasing. Consequently, waste management systems have become integral to city life, playing a critical role in resource efficiency and environmental protection. Inadequate waste management systems can adversely affect the environment, human health, and the economy. Accurate and rapid automatic waste classification poses a significant challenge in recycling. Deep learning models have achieved successful image classification in various fields recently. However, the optimal determination of many hyperparameters is crucial in these models. In this study, we developed a deep learning model that achieves the best classification performance by optimizing the depth, width, and other hyperparameters. Our six-layer Convolutional Neural Network (CNN) model with the lowest depth and width produced a successful result with an accuracy value of 89% and an F1 score of 88%. Moreover, several state-of-the-art CNN models such as VGG19, DenseNet169, ResNet101, Xception, InceptionV3, RegnetX008, RegnetY008, EfficientNetV2S trained with transfer learning and fine-tuning. Extensive experimental work has been done to find the optimal hyperparameters with GridSearch. Our most comprehensive DenseNet169 model, which we trained with fine-tuning, provided an accuracy value of 96.42% and an F1 score of 96%. These models can be successfully used in a variety of waste classification automation.

Keywords

References

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Details

Primary Language

English

Subjects

Artificial Intelligence

Journal Section

Research Article

Authors

Mahir Kaya ^*
0000-0001-9182-271X
Türkiye

Samet Ulutürk
0000-0002-7765-6281
Türkiye

Yasemin Çetin Kaya
0000-0002-6745-7705
Türkiye

Onur Altıntaş
0009-0009-2711-5119
Türkiye

Bülent Turan
0000-0003-0673-469X
Türkiye

Early Pub Date

August 27, 2023

Publication Date

August 31, 2023

Submission Date

February 28, 2023

Acceptance Date

July 17, 2023

Published in Issue

Year 1970 Volume: 6 Number: 2

DOI

https://doi.org/10.35377/saucis...1257100

IZ

https://izlik.org/JA94SJ46FL

Cite

RIS / Bibtex

APA

Kaya, M., Ulutürk, S., Çetin Kaya, Y., Altıntaş, O., & Turan, B. (2023). Optimization of Several Deep CNN Models for Waste Classification. Sakarya University Journal of Computer and Information Sciences, 6(2), 91-104. https://doi.org/10.35377/saucis...1257100

AMA

1.Kaya M, Ulutürk S, Çetin Kaya Y, Altıntaş O, Turan B. Optimization of Several Deep CNN Models for Waste Classification. SAUCIS. 2023;6(2):91-104. doi:10.35377/saucis.1257100

Chicago

Kaya, Mahir, Samet Ulutürk, Yasemin Çetin Kaya, Onur Altıntaş, and Bülent Turan. 2023. “Optimization of Several Deep CNN Models for Waste Classification”. Sakarya University Journal of Computer and Information Sciences 6 (2): 91-104. https://doi.org/10.35377/saucis. 1257100.

EndNote

Kaya M, Ulutürk S, Çetin Kaya Y, Altıntaş O, Turan B (August 1, 2023) Optimization of Several Deep CNN Models for Waste Classification. Sakarya University Journal of Computer and Information Sciences 6 2 91–104.

IEEE

[1]M. Kaya, S. Ulutürk, Y. Çetin Kaya, O. Altıntaş, and B. Turan, “Optimization of Several Deep CNN Models for Waste Classification”, SAUCIS, vol. 6, no. 2, pp. 91–104, Aug. 2023, doi: 10.35377/saucis...1257100.

ISNAD

Kaya, Mahir - Ulutürk, Samet - Çetin Kaya, Yasemin - Altıntaş, Onur - Turan, Bülent. “Optimization of Several Deep CNN Models for Waste Classification”. Sakarya University Journal of Computer and Information Sciences 6/2 (August 1, 2023): 91-104. https://doi.org/10.35377/saucis. 1257100.

JAMA

1.Kaya M, Ulutürk S, Çetin Kaya Y, Altıntaş O, Turan B. Optimization of Several Deep CNN Models for Waste Classification. SAUCIS. 2023;6:91–104.

MLA

Kaya, Mahir, et al. “Optimization of Several Deep CNN Models for Waste Classification”. Sakarya University Journal of Computer and Information Sciences, vol. 6, no. 2, Aug. 2023, pp. 91-104, doi:10.35377/saucis. 1257100.

Vancouver

1.Mahir Kaya, Samet Ulutürk, Yasemin Çetin Kaya, Onur Altıntaş, Bülent Turan. Optimization of Several Deep CNN Models for Waste Classification. SAUCIS. 2023 Aug. 1;6(2):91-104. doi:10.35377/saucis. 1257100

Optimization of Several Deep CNN Models for Waste Classification

Öz

Optimization of Several Deep CNN Models for Waste Classification

Abstract

Keywords

References

Details

Primary Language

Subjects

Journal Section

Authors

Early Pub Date

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

Cite

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