Deep learning-based automated garbage image classification using light-weight models

Gozde Yolcu Öztel

Makale detayları

Deep learning-based automated garbage image classification using light-weight models

Yazarlar:

Gozde Yolcu Öztel

Yayıncı:

Kütahya Dumlupinar University

Ana Başlıklar:

Computer Vision

Dergi:

Journal of Scientific Reports-A Issue 062, pp.171-181

Belge Tipi:

Makale

Yayın Tarihi:

9/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001353097

pISSN:

2687-6167

Anahtar kelimeler:

Özet:

 Garbage classification is essential for various aspects like health, economy and environment. The awareness of the users separation of garbage has not been gained yet. So, garbage classification is done manually in waste sorting facilities. This is time-consuming and a difficult task; thus, automated systems are needed for this area. With this motivation, a deep learning-based system that provides automated classification of garbage has been developed in this study. In the study, five-types waste were used. These are glass, plastic, battery, paper and metal waste. MobileNetv3, ShuffleNet, EfficientNetb2, SqueezeNet, GoogleNet and MobileViT models were retrained with the transfer learning approach to classify garbage images. According to the training and testing processes performed on the Garbage Dataset, EfficientNetb2 obtained the highest performance with 0.9768 accuracy, 0.9769 precision, 0.9768 recall, and 0.9768 F1-score values. To better demonstrate the benefits of transfer learning approach, EfficientNetb2 was also trained from scratch and almost 18 smaller accuracy score than transfer learning was obtained. Moreover, to evaluate system generalization ability, the trained EficientNetb2 model was tested on another dataset. 0.9700 accuracy, 0.9706 precision, 0.9700 recall, 0.9701 f1-score values were obtained in the second dataset. The high scores on the second dataset may be related to less complex data included in this dataset and high-system generalization capacity. The future target of the study may be to integrate the system into an embedded system. Therefore, light-weight models were specially selected. These models use fewer parameters, run in less time, and require less processing load, which are requirements of the embedded systems. This system can be used in smart city projects, waste management systems, and even the development of robotic waste collection technologies. In addition to helping reduce environmental pollution, it can also contribute to reducing human labor and contributing to recycling.

 Garbage classification is essential for various aspects like health, economy and environment. The awareness of the users separation of garbage has not been gained yet. So, garbage classification is done manually in waste sorting facilities. This is time-consuming and a difficult task; thus, automated systems are needed for this area. With this motivation, a deep learning-based system that provides automated classification of garbage has been developed in this study. In the study, five-types waste were used. These are glass, plastic, battery, paper and metal waste. MobileNetv3, ShuffleNet, EfficientNetb2, SqueezeNet, GoogleNet and MobileViT models were retrained with the transfer learning approach to classify garbage images. According to the training and testing processes performed on the Garbage Dataset, EfficientNetb2 obtained the highest performance with 0.9768 accuracy, 0.9769 precision, 0.9768 recall, and 0.9768 F1-score values. To better demonstrate the benefits of transfer learning approach, EfficientNetb2 was also trained from scratch and almost 18 smaller accuracy score than transfer learning was obtained. Moreover, to evaluate system generalization ability, the trained EficientNetb2 model was tested on another dataset. 0.9700 accuracy, 0.9706 precision, 0.9700 recall, 0.9701 f1-score values were obtained in the second dataset. The high scores on the second dataset may be related to less complex data included in this dataset and high-system generalization capacity. The future target of the study may be to integrate the system into an embedded system. Therefore, light-weight models were specially selected. These models use fewer parameters, run in less time, and require less processing load, which are requirements of the embedded systems. This system can be used in smart city projects, waste management systems, and even the development of robotic waste collection technologies. In addition to helping reduce environmental pollution, it can also contribute to reducing human labor and contributing to recycling.

 Garbage classification is essential for various aspects like health, economy and environment. The awareness of the users separation of garbage has not been gained yet. So, garbage classification is done manually in waste sorting facilities. This is time-consuming and a difficult task; thus, automated systems are needed for this area. With this motivation, a deep learning-based system that provides automated classification of garbage has been developed in this study. In the study, five-types waste were used. These are glass, plastic, battery, paper and metal waste. MobileNetv3, ShuffleNet, EfficientNetb2, SqueezeNet, GoogleNet and MobileViT models were retrained with the transfer learning approach to classify garbage images. According to the training and testing processes performed on the Garbage Dataset, EfficientNetb2 obtained the highest performance with 0.9768 accuracy, 0.9769 precision, 0.9768 recall, and 0.9768 F1-score values. To better demonstrate the benefits of transfer learning approach, EfficientNetb2 was also trained from scratch and almost 18 smaller accuracy score than transfer learning was obtained. Moreover, to evaluate system generalization ability, the trained EficientNetb2 model was tested on another dataset. 0.9700 accuracy, 0.9706 precision, 0.9700 recall, 0.9701 f1-score values were obtained in the second dataset. The high scores on the second dataset may be related to less complex data included in this dataset and high-system generalization capacity. The future target of the study may be to integrate the system into an embedded system. Therefore, light-weight models were specially selected. These models use fewer parameters, run in less time, and require less processing load, which are requirements of the embedded systems. This system can be used in smart city projects, waste management systems, and even the development of robotic waste collection technologies. In addition to helping reduce environmental pollution, it can also contribute to reducing human labor and contributing to recycling.

 Garbage classification is essential for various aspects like health, economy and environment. The awareness of the users separation of garbage has not been gained yet. So, garbage classification is done manually in waste sorting facilities. This is time-consuming and a difficult task; thus, automated systems are needed for this area. With this motivation, a deep learning-based system that provides automated classification of garbage has been developed in this study. In the study, five-types waste were used. These are glass, plastic, battery, paper and metal waste. MobileNetv3, ShuffleNet, EfficientNetb2, SqueezeNet, GoogleNet and MobileViT models were retrained with the transfer learning approach to classify garbage images. According to the training and testing processes performed on the Garbage Dataset, EfficientNetb2 obtained the highest performance with 0.9768 accuracy, 0.9769 precision, 0.9768 recall, and 0.9768 F1-score values. To better demonstrate the benefits of transfer learning approach, EfficientNetb2 was also trained from scratch and almost 18 smaller accuracy score than transfer learning was obtained. Moreover, to evaluate system generalization ability, the trained EficientNetb2 model was tested on another dataset. 0.9700 accuracy, 0.9706 precision, 0.9700 recall, 0.9701 f1-score values were obtained in the second dataset. The high scores on the second dataset may be related to less complex data included in this dataset and high-system generalization capacity. The future target of the study may be to integrate the system into an embedded system. Therefore, light-weight models were specially selected. These models use fewer parameters, run in less time, and require less processing load, which are requirements of the embedded systems. This system can be used in smart city projects, waste management systems, and even the development of robotic waste collection technologies. In addition to helping reduce environmental pollution, it can also contribute to reducing human labor and contributing to recycling.

Makale detayları

Deep learning-based automated garbage image classification using light-weight models

Yazarlar:

Gozde Yolcu Öztel

Yayıncı:

Kütahya Dumlupinar University

Ana Başlıklar:

Computer Vision

Dergi:

Journal of Scientific Reports-A Issue 062, pp.171-181

Belge Tipi:

Makale

Yayın Tarihi:

9/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001353097

pISSN:

2687-6167

garbage image classification

transfer learning

deep learning

CNN

MobileViT

Sayı

Makale detayları

Deep learning-based automated garbage image classification using light-weight models

Yazarlar: Gozde Yolcu Öztel

Yayıncı: Kütahya Dumlupinar University

Ana Başlıklar: Computer Vision

Dergi: Journal of Scientific Reports-A Issue 062, pp.171-181

Belge Tipi: Makale

Yayın Tarihi: 9/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001353097

pISSN: 2687-6167

garbage image classification

transfer learning

deep learning

CNN

MobileViT

Sayı

Yazarlar:

Gozde Yolcu Öztel

Yayıncı:

Kütahya Dumlupinar University

Ana Başlıklar:

Computer Vision

Dergi:

Journal of Scientific Reports-A Issue 062, pp.171-181

Belge Tipi:

Makale

Yayın Tarihi:

9/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001353097

pISSN:

2687-6167