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Journals / Avrupa Bilim ve Teknoloji Dergisi / 2026 / Sayı: 32 Özel Sayı

Deep Learning Based Garbage Detection for Autonomous Garbage Collection Vehicles

Süleyman Uzun(SAKARYA UYGULAMALI BİLİMLER ÜNİVERSİTESİ)0000-0001-8246-6733
Dilara Karaca(SAKARYA UYGULAMALI BİLİMLER ÜNİVERSİTESİ)0000-0002-4352-4141
Pages
1194–1198
Publication Date
2021-12-31
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:With the rapid technological advances and the increasing human population, the need for more production has emerged and consumption has increased accordingly.
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Key Findings & Data:As a result of this increased consumption, more garbage has been generated.
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Conclusion & Impact: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.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Transfer learning · Garbage detection · Garbage dataset.

Abstract

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.

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.

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Cite This Article

Uzun, S., Karaca, D. (2021). Deep Learning Based Garbage Detection for Autonomous Garbage Collection Vehicles. Avrupa Bilim ve Teknoloji Dergisi, Sayı: 32 Özel Sayı, 1194–1198. https://doi.org/10.31590/ejosat.1045510

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