Deep Learning Based Garbage Detection for Autonomous Garbage Collection Vehicles

Süleyman UZUN; Dilara KARACA

doi:https://doi.org/10.31590/ejosat.1045510

Makale detayları

Deep Learning Based Garbage Detection for Autonomous Garbage Collection Vehicles

Yazarlar:

Süleyman UZUN | Dilara KARACA

Yayıncı:

Osman SAĞDIÇ

Ana Başlıklar:

Uygulamalı fizik

Dergi:

European Journal of Science and Technology 2021 Vol. Issue 32, pp.1194-1198

Belge Tipi:

Makale

Yayın Tarihi:

12/2021

Belge Dili:

İngilizce

Document ID (Org No.):

2000916519

Sıklık:

Üç Aylık

eISSN:

2148-2683

DOI:

https://doi.org/10.31590/ejosat.1045510

Anahtar kelimeler:

Özet:

 

With the rapid technological advances and the increasing human population, the need for more production has emerged and consumption has increased accordingly. As a result of this increased consumption, more garbage has been generated.  Environmental pollution caused by these garbages emerges as a problem that people have to overcome both in Turkey and in the world. Many studies have been conducted to overcome this problem. Especially today, with the development of autonomous vehicles and artificial intelligence, the solutions using these technologies have increased. In this study, a new data set was created for autonomous garbage collection vehicles and a model was proposed in which these vehicles can be used. The data set was prepared with images of garbage with paper cups, which is one of the most polluting garbage, taken in different places, and images consisting of different garbage without paper cups. Paper cups were detected from these images with pre-trained Squenzenet, VGG-19 and GoogLeNet convolutional neural networks. The performance rate of the SquenzeNet, GoogLeNet and Vgg-19 networks used in the study was found as 97.77%, 96.44% 94.66%, respectively.

 

With the rapid technological advances and the increasing human population, the need for more production has emerged and consumption has increased accordingly. As a result of this increased consumption, more garbage has been generated.  Environmental pollution caused by these garbages emerges as a problem that people have to overcome both in Turkey and in the world. Many studies have been conducted to overcome this problem. Especially today, with the development of autonomous vehicles and artificial intelligence, the solutions using these technologies have increased. In this study, a new data set was created for autonomous garbage collection vehicles and a model was proposed in which these vehicles can be used. The data set was prepared with images of garbage with paper cups, which is one of the most polluting garbage, taken in different places, and images consisting of different garbage without paper cups. Paper cups were detected from these images with pre-trained Squenzenet, VGG-19 and GoogLeNet convolutional neural networks. The performance rate of the SquenzeNet, GoogLeNet and Vgg-19 networks used in the study was found as 97.77%, 96.44% 94.66%, respectively.

Makale detayları

Deep Learning Based Garbage Detection for Autonomous Garbage Collection Vehicles

Yazarlar:

Süleyman UZUN | Dilara KARACA

Yayıncı:

Osman SAĞDIÇ

Ana Başlıklar:

Uygulamalı fizik

Dergi:

European Journal of Science and Technology 2021 Vol. Issue 32, pp.1194-1198

Belge Tipi:

Makale

Yayın Tarihi:

12/2021

Belge Dili:

İngilizce

Document ID (Org No.):

2000916519

Sıklık:

Üç Aylık

eISSN:

2148-2683

DOI:

https://doi.org/10.31590/ejosat.1045510

Transfer learning

Garbage detection

Garbage dataset.

Sayı

Makale detayları

Deep Learning Based Garbage Detection for Autonomous Garbage Collection Vehicles

Yazarlar: Süleyman UZUN | Dilara KARACA

Yayıncı: Osman SAĞDIÇ

Ana Başlıklar: Uygulamalı fizik

Dergi: European Journal of Science and Technology 2021 Vol. Issue 32, pp.1194-1198

Belge Tipi: Makale

Yayın Tarihi: 12/2021

Belge Dili: İngilizce

Document ID (Org No.): 2000916519

Sıklık: Üç Aylık

eISSN: 2148-2683

DOI: https://doi.org/10.31590/ejosat.1045510

Transfer learning

Garbage detection

Garbage dataset.

Sayı

Yazarlar:

Süleyman UZUN | Dilara KARACA

Yayıncı:

Osman SAĞDIÇ

Ana Başlıklar:

Uygulamalı fizik

Dergi:

European Journal of Science and Technology 2021 Vol. Issue 32, pp.1194-1198

Belge Tipi:

Makale

Yayın Tarihi:

12/2021

Belge Dili:

İngilizce

Document ID (Org No.):

2000916519

Sıklık:

Üç Aylık

eISSN:

2148-2683

DOI:

https://doi.org/10.31590/ejosat.1045510