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Dergiler / Journal of Smart Systems Research / 2022 / Cilt: 3 - Sayı: 2

YOLO Tabanlı Atık Aytıştırma

YOLO – Based Waste Detection

Kenan ERİN
Bünyamin BİNGÖL
Barış BORU
Sayfa
120–127
Yayın Tarihi
2022-12-26
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

In this study, while determining the type of waste, the location of the waste will be determined in 3D with a depth camera and image processing techniques

📌 3 Maddede Bu Makale

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Amaç & Hipotez:The management of recycling wastes is one of the most important issues because of the increasing production rates.
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Temel Bulgular & Veri:Automatic sorting systems are defined as systems that separate recyclable waste materials with robotic manipulators where human intervention is minimal.
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Sonuç & Katkı:In this study, while determining the type of waste, the location of the waste will be determined in 3D with a depth camera and image processing techniques
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:YOLO · RealSense · Waste Recognition

Özet

The management of recycling wastes is one of the most important issues because of the increasing production rates. The collecting and recycling of waste are also becoming more crucial for economic and environmental reasons because landfill space is becoming more and more limited. Automatic sorting systems are defined as systems that separate recyclable waste materials with robotic manipulators where human intervention is minimal. In this study, while determining the type of waste, the location of the waste will be determined in 3D with a depth camera and image processing techniques

Abstract

The management of recycling wastes is one of the most important issues because of the increasing production rates. The collecting and recycling of waste are also becoming more crucial for economic and environmental reasons because landfill space is becoming more and more limited. Automatic sorting systems are defined as systems that separate recyclable waste materials with robotic manipulators where human intervention is minimal. In this study, while determining the type of waste, the location of the waste will be determined in 3D with a depth camera and image processing techniques.

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Bu Makaleye Atıf Yap

ERİN, K., BİNGÖL, B., BORU, B. (2022). YOLO Tabanlı Atık Aytıştırma. Journal of Smart Systems Research, Cilt: 3 - Sayı: 2, 120–127. https://ajindex.com/articles/873646

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Kaynakça (8)

  1. Tatzer, P., Wolf, M., Panner, T. (2005). Industrial application for inline material sorting using hyperspectral imaging in the NIR range. Real-Time Imaging, 11(2), 99-107
  2. Scavino, E., Wahab, D. A., Hussain, A., Basri, H., Mustafa, M. M. (2009). Application of automated image analysis to the identification and extraction of recyclable plastic bottles. Journal of Zhejiang University SCIENCE A, 10(6), 794-799
  3. Özkan, K., Ergin, S., Işık, Ş., & Işıklı, İ. (2015). A new classification scheme of plastic wastes based upon recycling labels. Waste Management, 35, 29-35
  4. Meng, S., & Chu, W. T. (2020, February). A study of garbage classification with convolutional neural networks. In 2020 Indo–Taiwan 2nd International Conference on Computing, Analytics and Networks (Indo-Taiwan ICAN) (pp. 152-157). IEEE
  5. Cao, L., & Xiang, W. (2020, June). Application of convolutional neural network based on transfer learning for garbage classification. In 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC) (pp. 1032-1036). IEEE
  6. Liu, J., Balatti, P., Ellis, K., Hadjivelichkov, D., Stoyanov, D., Ajoudani, A., & Kanoulas, D. (2021, July). Garbage collection and sorting with a mobile manipulator using deep learning and whole-body control. In 2020 IEEE-RAS 20th International Conference on Humanoid Robots (Humanoids) (pp. 408-414). IEEE
  7. S. Shinde, A. Kothari, and V. Gupta, “YOLO based Human Action Recognition and Localization,” in Procedia Computer Science, Jan. 2018, vol. 133, pp. 831–838, doi: 10.1016/j.procs.2018.07.112
  8. Hendry and R. C. Chen, “Automatic License Plate Recognition via sliding-window darknet-YOLO deep learning,” Image Vis. Comput., vol. 87, pp. 47–56, Jul. 2019, doi: 10.1016/j.imavis.2019.04.007