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Authors: Gábor Paller and Gábor Élő

Affiliation: Széchenyi István University, Information Society Research & Education Group, Egyetem tér 1. Győr, Hungary

Keyword(s): Image Processing, Waste Detection, Machine Learning.

Abstract: Plastic waste in living waters is a worldwide problem. One particular variant of this problem is floating plastic waste, e.g. plastic bottles or bags. Rivers often carry large amount of floating plastic waste, due to unauthorized or not properly maintained waste dumps installed in the rivers’ flood plain. It is of utmost importance that environmental protection agencies be aware of such large-scale plastic pollutions so that they can initiate appropriate countermeasures. This paper presents two iterations of an early warning system designed to alert environmental protection agencies of plastic waste pollution. These systems are based on processing camera images but while the first iteration uses motion detection for identifying relevant images, the second iteration adopted a machine learning algorithm deployed in edge computing architecture. Better selectivity of the machine learning-based solution significantly eases the burden on the operators of the early warning system.

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Paper citation in several formats:
Paller, G. and Élő, G. (2022). Towards a Floating Plastic Waste Early Warning System. In Proceedings of the 11th International Conference on Sensor Networks - SENSORNETS; ISBN 978-989-758-551-7; ISSN 2184-4380, SciTePress, pages 45-50. DOI: 10.5220/0010894500003118

@conference{sensornets22,
author={Gábor Paller. and Gábor Élő.},
title={Towards a Floating Plastic Waste Early Warning System},
booktitle={Proceedings of the 11th International Conference on Sensor Networks - SENSORNETS},
year={2022},
pages={45-50},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010894500003118},
isbn={978-989-758-551-7},
issn={2184-4380},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Sensor Networks - SENSORNETS
TI - Towards a Floating Plastic Waste Early Warning System
SN - 978-989-758-551-7
IS - 2184-4380
AU - Paller, G.
AU - Élő, G.
PY - 2022
SP - 45
EP - 50
DO - 10.5220/0010894500003118
PB - SciTePress

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