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Aquatic Debris Detection Using Embedded Camera Sensors
Aquatic debris monitoring is of great importance to human health, aquatic habitats and water transport. In this paper, we first introduce the prototype of an aquatic sensor node equipped with an embedded camera sensor. Based on this sensing platform, we propose a fast and accurate debris detection a...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4367351/ https://www.ncbi.nlm.nih.gov/pubmed/25647741 http://dx.doi.org/10.3390/s150203116 |
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author | Wang, Yong Wang, Dianhong Lu, Qian Luo, Dapeng Fang, Wu |
author_facet | Wang, Yong Wang, Dianhong Lu, Qian Luo, Dapeng Fang, Wu |
author_sort | Wang, Yong |
collection | PubMed |
description | Aquatic debris monitoring is of great importance to human health, aquatic habitats and water transport. In this paper, we first introduce the prototype of an aquatic sensor node equipped with an embedded camera sensor. Based on this sensing platform, we propose a fast and accurate debris detection algorithm. Our method is specifically designed based on compressive sensing theory to give full consideration to the unique challenges in aquatic environments, such as waves, swaying reflections, and tight energy budget. To upload debris images, we use an efficient sparse recovery algorithm in which only a few linear measurements need to be transmitted for image reconstruction. Besides, we implement the host software and test the debris detection algorithm on realistically deployed aquatic sensor nodes. The experimental results demonstrate that our approach is reliable and feasible for debris detection using camera sensors in aquatic environments. |
format | Online Article Text |
id | pubmed-4367351 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-43673512015-04-30 Aquatic Debris Detection Using Embedded Camera Sensors Wang, Yong Wang, Dianhong Lu, Qian Luo, Dapeng Fang, Wu Sensors (Basel) Article Aquatic debris monitoring is of great importance to human health, aquatic habitats and water transport. In this paper, we first introduce the prototype of an aquatic sensor node equipped with an embedded camera sensor. Based on this sensing platform, we propose a fast and accurate debris detection algorithm. Our method is specifically designed based on compressive sensing theory to give full consideration to the unique challenges in aquatic environments, such as waves, swaying reflections, and tight energy budget. To upload debris images, we use an efficient sparse recovery algorithm in which only a few linear measurements need to be transmitted for image reconstruction. Besides, we implement the host software and test the debris detection algorithm on realistically deployed aquatic sensor nodes. The experimental results demonstrate that our approach is reliable and feasible for debris detection using camera sensors in aquatic environments. MDPI 2015-01-30 /pmc/articles/PMC4367351/ /pubmed/25647741 http://dx.doi.org/10.3390/s150203116 Text en © 2015 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Wang, Yong Wang, Dianhong Lu, Qian Luo, Dapeng Fang, Wu Aquatic Debris Detection Using Embedded Camera Sensors |
title | Aquatic Debris Detection Using Embedded Camera Sensors |
title_full | Aquatic Debris Detection Using Embedded Camera Sensors |
title_fullStr | Aquatic Debris Detection Using Embedded Camera Sensors |
title_full_unstemmed | Aquatic Debris Detection Using Embedded Camera Sensors |
title_short | Aquatic Debris Detection Using Embedded Camera Sensors |
title_sort | aquatic debris detection using embedded camera sensors |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4367351/ https://www.ncbi.nlm.nih.gov/pubmed/25647741 http://dx.doi.org/10.3390/s150203116 |
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