Detecting Litter in Street Sweepers Using Deep Learning

Suheda Gokbudak; Emir Enes Tas; Onur Ozer; Veysel Tilegi

doi:10.55549/epstem.1406224

Conference Paper

Detecting Litter in Street Sweepers Using Deep Learning

Year 2023, Volume: 24, 55 - 62, 30.11.2023

Suheda Gokbudak Emir Enes Tas Onur Ozer Veysel Tilegi

https://doi.org/10.55549/epstem.1406224

Abstract

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.

Keywords

Waste detection , Artificial intelligence , OpenCV , YOLOv7 , Street sweeper

References

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).

Year 2023, Volume: 24, 55 - 62, 30.11.2023

Suheda Gokbudak Emir Enes Tas Onur Ozer Veysel Tilegi

https://doi.org/10.55549/epstem.1406224

Abstract

References

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).

There are 4 citations in total.

Details

Primary Language	English
Subjects	Software Engineering (Other)
Journal Section	Articles
Authors	Suheda Gokbudak Emir Enes Tas Onur Ozer Veysel Tilegi
Early Pub Date	December 18, 2023
Publication Date	November 30, 2023
Published in Issue	Year 2023 Volume: 24

Cite

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