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BibTex RIS Kaynak Göster
Yıl 2023, Cilt: 24, 55 - 62, 30.11.2023
https://doi.org/10.55549/epstem.1406224

Öz

Kaynakça

  • Donati, L. L., Fontanini, T., Tagliaferri, F., & Prati, A. (2020). An energy saving road sweeper using deep vision for garbage detection. Applied Sciences, 10(22), 8146.
  • Min, H., Zhu, X., Yan, B., & Yu, Y. (2019). Research on Visual Algorithm of road garbage based on intelligent control of road sweeper. Journal of Physics, 1302(3).
  • Nasrullah, M., & Diker, A. (2021). Derin ogrenme ve YOLO algoritmaları ile nesne tespiti ve serit belirleme. 2 nd International Conference on Intelligent Transportation Systems. Balıkesir, Turkey.
  • Ozel, M. A., Baysal, S. S., & Sahin, M. E. (2021). Derin ogrenme algoritması (YOLO) ile dinamik test suresince suspansiyon parcalarında catlak tespiti. Europan Journal of Science and Technology, 26(1-5).

Detecting Litter in Street Sweepers Using Deep Learning

Yıl 2023, Cilt: 24, 55 - 62, 30.11.2023
https://doi.org/10.55549/epstem.1406224

Öz

Street sweeping vehicles are essential equipment in our daily lives designed to clean streets and roads. With numerous mechanical components, they play a significant role in collecting all types of waste and contributing to environmental cleanliness. These vehicles typically consist of rotating brushes, collecting belts, and components involving water or air currents. Among these parts, brushes and vacuums are the most energy-consuming elements in street sweepers. Moreover, they are often operated in full power mode due to semi-automatic control systems, leaving the remaining control to the driver. However, this practice results in energy wastage and noise pollution. The aim of this study is to adjust vacuum suction in street sweepers according to the size of waste using image processing and deep learning techniques, thus achieving energy conservation. In this research, the YOLOv7 model and OpenCV are employed to train artificial intelligence for waste detection in street sweepers and accordingly regulate vacuum suction.

Kaynakça

  • Donati, L. L., Fontanini, T., Tagliaferri, F., & Prati, A. (2020). An energy saving road sweeper using deep vision for garbage detection. Applied Sciences, 10(22), 8146.
  • Min, H., Zhu, X., Yan, B., & Yu, Y. (2019). Research on Visual Algorithm of road garbage based on intelligent control of road sweeper. Journal of Physics, 1302(3).
  • Nasrullah, M., & Diker, A. (2021). Derin ogrenme ve YOLO algoritmaları ile nesne tespiti ve serit belirleme. 2 nd International Conference on Intelligent Transportation Systems. Balıkesir, Turkey.
  • Ozel, M. A., Baysal, S. S., & Sahin, M. E. (2021). Derin ogrenme algoritması (YOLO) ile dinamik test suresince suspansiyon parcalarında catlak tespiti. Europan Journal of Science and Technology, 26(1-5).
Toplam 4 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yazılım Mühendisliği (Diğer)
Bölüm Makaleler
Yazarlar

Suheda Gokbudak

Emir Enes Tas

Onur Ozer

Veysel Tilegi

Erken Görünüm Tarihi 18 Aralık 2023
Yayımlanma Tarihi 30 Kasım 2023
Yayımlandığı Sayı Yıl 2023Cilt: 24

Kaynak Göster

APA Gokbudak, S., Tas, E. E., Ozer, O., Tilegi, V. (2023). Detecting Litter in Street Sweepers Using Deep Learning. The Eurasia Proceedings of Science Technology Engineering and Mathematics, 24, 55-62. https://doi.org/10.55549/epstem.1406224