Using unmanned aerial vehicles to identify unauthorized municipal solid waste dump sites
A.A. Popov, A.M. Tramova, Yu.D. Romanova
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Abstract. The article is devoted to the issues related to the use of unmanned aerial vehicles for waste management. The purpose of the research is to improve the waste management system by recognizing municipal solid waste in images obtained using unmanned aerial vehicles. The object of the study is waste management. The subject of the research is a methodological apparatus that allows: to determine the required number of unmanned aerial vehicles for remotely obtaining the required number of images of surface areas within a specified period of time and to detect unauthorized dumps sites (accumulations of municipal solid waste) in the obtained images. An analysis of examples of using neural networks and machine learning algorithms for recognizing unauthorized dumps sites in images obtained as a result of remote monitoring of the surface using manned and unmanned aerial vehicles is carried out. An algorithm for determining the minimum number of flights of unmanned aerial vehicles to monitor a surface area is built and the features of using the algorithm are considered. The results obtained in the work can be used in the design of a waste management system that includes remote sensing of the surface using unmanned aerial vehicles.
Keywords: “garbage” reform, waste management, unmanned aerial vehicle, control, unauthorized accumulations of waste, surface image, neural network, machine learning, waste recognition, algorithm
For citation. Popov A.A., Tramova A.M., Romanova Yu.D. Using unmanned aerial vehicles to identify unauthorized municipal solid waste dump sites. News of the Kabardino-Balkarian Scientific Center of RAS.2024. Vol. 26. No. 5. Pp. 40–52. DOI: 10.35330/1991-6639-2024-26-5-40-52
References
- Popov A.A., Solomina Ju.K. Analysis of the possibilities of using unmanned aerial vehicles for managing housing and communal services. Fundamental’nye issledovaniya [Fundamental research]. 2018. No. 2. Pp. 144–151. EDN: YSEXJE. (In Russian)
- Filkin T., Sliusar N., Ritzkowski M., Huber-Humer M. unmanned aerial vehicles for operational monitoring of landfills. Drones. 2021. Vol. 5(4). Art. Number: 125. DOI: 10.3390/drones5040125
- Sliusar N., Filkin T., Huber-Humer M., Ritzkowski M. Drone technology in municipal solid waste management and landfilling: A comprehensive review. Waste Management. 2022. Vol. 139. Pp. 1–16. DOI: 10.1016/j.wasman.2021.12.006
- Filkin T., Sliusar N., Ritzkowski M., Huber-Humer M. Unmanned aerial vehicles for operational monitoring of landfills. Drones. 2021. Vol. 5(4). Art. Number: 125. DOI: 10.3390/drones5040125
- Wilkinson A.K. The identification of garbage dumps in the rural areas of Cyprus through the application of deep learning to satellite imagery. arXiv. 2023. Vol. 8. Art. number:02502. DOI: 10.13140/RG.2.2.22837.86244
- Jia Z., Chen Q., Hong S., Cheng Q. Remote sensing recognition of construction waste accumulation based on resources satellite three multi-view stereoscopic image. IOP Conference
Series: Earth and Environmental Science. 2020. Vol. 615. Art. Number: 012027. DOI: 10.1088/1755-1315/615/1/012027 - Torres R.N., Fraternali P. Learning to Identify Illegal Landfills through Scene Classification in Aerial Images. Remote Sensing. 2021. Vol. 13(22). Art. Number: 4520. DOI: 10.3390/rs13224520
- Sharma A.K., Jain A., Chaudhary D. et al. An approach to automatic garbage detection framework designing using CNN. International Journal of Advanced Computer Science and Applications. 2024. Vol. 14(2). Pp. 257–262. DOI: 10.14569/IJACSA.2023.0140231
- Musci M., Serrão G. de M.P., Ferreira M.V.E.N.B. et al. Drones and sustainability: how technology can assist in the automatic detection of waste in hard-to-access areas. Revista de Gestão Social e Ambiental. 2024. Vol. 18(9). Number: e07235. DOI: 10.24857/rgsa.v18n9-109
- Anadkat A.P., Monisha B.V., Puthineedi M. et al. Drone based Solid Waste Detection using Deep Learning & Image Processing. Alliance International Conference on Artificial Intelligence and Machine Learning (AICAAM). Karnataka. India. 26–27 April 2019. Pp. 357–364.
- Torres R.N., Fraternali P. Aerial Waste dataset for landfill discovery in aerial and satellite images. Scientific Data. 2023. Vol. 10. Art. Number: 63. DOI: 10.1038/s41597-023-01976-9
- Wang Z., Yang X., Zheng X., Li H. Vision-Based On-Site construction waste localization using unmanned aerial vehicle. Sensors. 2024. Vol. 24. Art. Number: 2816. DOI: 10.3390/s24092816
- Verma V., Gupta D., Gupta S. et al. A Deep learning-based intelligent garbage detection system using an unmanned aerial vehicle. Symmetry. 2022. Vol. 14. Art. Number: 960. DOI: 10.3390/sym14050960
- Liao Y.-H., Juang J.-G. Real-Time UAV Trash monitoring system. Applied Science. 2022. Vol. 12(4). Art. Number: 1838. DOI: 10.3390/app12041838
- Youme O., Bayet T., Dembele J.M., Cambier C. Deep learning and remote sensing: Detection of dumping waste using UAV. Procedia Computer Science. 2021. Vol. 185. Pp. 361–369. DOI: 10.1016/j.procs.2021.05.037
Information about the authors
Aleksey A. Popov, Candidate of Technical Sciences, Associate Professor, Associate Professor of the Department of Informatics of the Plekhanov Russian University of Economics;
117997, Russia, Moscow, 36 Stremyannyy lane;
Popov.aa@rea.ru, ORCID: https://orcid.org/0000-0002-0692-3629, SPIN-code: 4105-9404
Aziza M. Tramova, Doctor of Economic Sciences, Associate Professor, Professor of the Department of Informatics of the Plekhanov Russian University of Economics;
117997, Russia, Moscow, 36 Stremyannyy lane;
Tramova.am@rea.ru, ORCID: https://orcid.org/0000-0 002-4089-6580, SPIN-code: 8583-3592
Yulia D. Romanova, Candidate of Economic Sciences, Associate Professor, Professor of the Department of Informatics of the Plekhanov Russian University of Economics;
117997, Russia, Moscow, 36 Stremyannyy lane;
Romanova.jud@rea.ru, ORCID: https://orcid.org/0000-0002-8273-0757 , SPIN-code: 8743-9162