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An automated solid waste detection using the optimized YOLO model for riverine management
Due to urbanization, solid waste pollution is an increasing concern for rivers, possibly threatening human health, ecological integrity, and ecosystem services. Riverine management in urban landscapes requires best management practices since the river is a vital component in urban ecological civiliz...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9412171/ https://www.ncbi.nlm.nih.gov/pubmed/36033781 http://dx.doi.org/10.3389/fpubh.2022.907280 |
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author | Zailan, Nur Athirah Azizan, Muhammad Mokhzaini Hasikin, Khairunnisa Mohd Khairuddin, Anis Salwa Khairuddin, Uswah |
author_facet | Zailan, Nur Athirah Azizan, Muhammad Mokhzaini Hasikin, Khairunnisa Mohd Khairuddin, Anis Salwa Khairuddin, Uswah |
author_sort | Zailan, Nur Athirah |
collection | PubMed |
description | Due to urbanization, solid waste pollution is an increasing concern for rivers, possibly threatening human health, ecological integrity, and ecosystem services. Riverine management in urban landscapes requires best management practices since the river is a vital component in urban ecological civilization, and it is very imperative to synchronize the connection between urban development and river protection. Thus, the implementation of proper and innovative measures is vital to control garbage pollution in the rivers. A robot that cleans the waste autonomously can be a good solution to manage river pollution efficiently. Identifying and obtaining precise positions of garbage are the most crucial parts of the visual system for a cleaning robot. Computer vision has paved a way for computers to understand and interpret the surrounding objects. The development of an accurate computer vision system is a vital step toward a robotic platform since this is the front-end observation system before consequent manipulation and grasping systems. The scope of this work is to acquire visual information about floating garbage on the river, which is vital in building a robotic platform for river cleaning robots. In this paper, an automated detection system based on the improved You Only Look Once (YOLO) model is developed to detect floating garbage under various conditions, such as fluctuating illumination, complex background, and occlusion. The proposed object detection model has been shown to promote rapid convergence which improves the training time duration. In addition, the proposed object detection model has been shown to improve detection accuracy by strengthening the non-linear feature extraction process. The results showed that the proposed model achieved a mean average precision (mAP) value of 89%. Hence, the proposed model is considered feasible for identifying five classes of garbage, such as plastic bottles, aluminum cans, plastic bags, styrofoam, and plastic containers. |
format | Online Article Text |
id | pubmed-9412171 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-94121712022-08-27 An automated solid waste detection using the optimized YOLO model for riverine management Zailan, Nur Athirah Azizan, Muhammad Mokhzaini Hasikin, Khairunnisa Mohd Khairuddin, Anis Salwa Khairuddin, Uswah Front Public Health Public Health Due to urbanization, solid waste pollution is an increasing concern for rivers, possibly threatening human health, ecological integrity, and ecosystem services. Riverine management in urban landscapes requires best management practices since the river is a vital component in urban ecological civilization, and it is very imperative to synchronize the connection between urban development and river protection. Thus, the implementation of proper and innovative measures is vital to control garbage pollution in the rivers. A robot that cleans the waste autonomously can be a good solution to manage river pollution efficiently. Identifying and obtaining precise positions of garbage are the most crucial parts of the visual system for a cleaning robot. Computer vision has paved a way for computers to understand and interpret the surrounding objects. The development of an accurate computer vision system is a vital step toward a robotic platform since this is the front-end observation system before consequent manipulation and grasping systems. The scope of this work is to acquire visual information about floating garbage on the river, which is vital in building a robotic platform for river cleaning robots. In this paper, an automated detection system based on the improved You Only Look Once (YOLO) model is developed to detect floating garbage under various conditions, such as fluctuating illumination, complex background, and occlusion. The proposed object detection model has been shown to promote rapid convergence which improves the training time duration. In addition, the proposed object detection model has been shown to improve detection accuracy by strengthening the non-linear feature extraction process. The results showed that the proposed model achieved a mean average precision (mAP) value of 89%. Hence, the proposed model is considered feasible for identifying five classes of garbage, such as plastic bottles, aluminum cans, plastic bags, styrofoam, and plastic containers. Frontiers Media S.A. 2022-08-12 /pmc/articles/PMC9412171/ /pubmed/36033781 http://dx.doi.org/10.3389/fpubh.2022.907280 Text en Copyright © 2022 Zailan, Azizan, Hasikin, Mohd Khairuddin and Khairuddin. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Public Health Zailan, Nur Athirah Azizan, Muhammad Mokhzaini Hasikin, Khairunnisa Mohd Khairuddin, Anis Salwa Khairuddin, Uswah An automated solid waste detection using the optimized YOLO model for riverine management |
title | An automated solid waste detection using the optimized YOLO model for riverine management |
title_full | An automated solid waste detection using the optimized YOLO model for riverine management |
title_fullStr | An automated solid waste detection using the optimized YOLO model for riverine management |
title_full_unstemmed | An automated solid waste detection using the optimized YOLO model for riverine management |
title_short | An automated solid waste detection using the optimized YOLO model for riverine management |
title_sort | automated solid waste detection using the optimized yolo model for riverine management |
topic | Public Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9412171/ https://www.ncbi.nlm.nih.gov/pubmed/36033781 http://dx.doi.org/10.3389/fpubh.2022.907280 |
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