Acta Marisiensis.
Seria Technologica



ISSN 2668-4217
ISSN-L 2668-4217
(Online)


Română

HomeEditorial boardSubmit paperPublication ethicsContactIndexing
Year 2023
Volume 20 (XXXVII), no 1
Volume 20 (XXXVII), no 2

Year 2022
Volume 19 (XXXVI), no 1
Volume 19 (XXXVI), no 2

Year 2021
Volume 18 (XXXV), no 1
Volume 18 (XXXV), no 2

Year 2020
Volume 17 (XXXIV), no 1
Volume 17 (XXXIV), no 2

Year 2019
Volume 16 (XXXIII), no 1
Volume 16 (XXXIII), no 2

Year 2018
Volume 15 (XXXII), no 1
Volume 15 (XXXII), no 2

Year 2017
Volume 14 (XXXI), no 1
Volume 14 (XXXI), no 2

Year 2016
Volume 13 (XXX), no 1
Volume 13 (XXX), no 2

Year 2015
Volume 12 (XXIX), no 1
Volume 12 (XXIX), no 2

Year 2014
Volume 11 (XXVIII), no 1
Volume 11 (XXVIII), no 2

Year 2013
Volume 10 (XXVII), no 1
Volume 10 (XXVII), no 2

Year 2012
Volume 9 (XXVI), no 1
Volume 9 (XXVI), no 2

Year 2011
Volume 8 (XXV), no 1
Volume 8 (XXV), no 2

Year 2010
Volume 7 (XXIV), no 1
Volume 7 (XXIV), no 2

Year 2009
Volume 6 (XXIII)

2022, Volume 19 (XXXVI), no 1

Underwater Image Detection for Cleaning Purposes; Techniques Used for Detection Based on Machine Learning

Author(s):
Ornelta GOXHAJ, Nilay Gul YILMAZ, Istanbul Technical University, Turkey
Lida KOUHALVANDI, Dogus University, Turkey
Ibraheem SHAYEA, Istanbul Technical University, Turkey
Azızul AZIZAN, Universiti Teknologi, Malaysia

Abstract:
Serious problems are on the rise, especially in these current times. The world is facing too many environmental threats. Water pollution is one of the main issues threatening the future. In some parts of the world, the water’s surface is covered by mucilage, which is dangerous for both aquatic animals and humans. This article firstly defines mucilage and highlights the reasons for its production. Afterwards to tackle water pollution, cleaning systems using image detection with the help of machine learning supervised classification algorithms are highlighted. This paper showcases the machine learning and classification used as well as the best solution for convolutional neural network and regionbased convolutional neural network methods.

DOI: https://doi.org/10.2478/amset-2022-0006

Pages: 28-35

View full article
Update: 21-Mar-2024 © Published by University Press