Literature DB >> 32469764

Estimation of plastic marine debris volumes on beaches using unmanned aerial vehicles and image processing based on deep learning.

Shin'ichiro Kako1, Shohei Morita2, Tetsuya Taneda3.   

Abstract

Plastic marine debris (PMD) is of global concern. To help address this problem, a novel approach for estimating PMD volumes using a combination of unmanned aerial vehicle (UAV) surveys and image processing based on deep learning is proposed. A three-dimensional model and orthoscopic image of a beach, constructed via Structure from Motion software using UAV-derived data, enabled PMD volumes to be computed by edge detection through image processing. The accuracy of the method was verified by estimating the volumes of test debris placed on a beach in known sizes and shapes. The proposed approach shows potential for estimating PMD volumes with an error of <5%. Compared with subjective methods based on beach surveys, this approach can accurately, rapidly, and objectively calculate the PMD volume on a beach and can be used to improve the efficiency of beach surveys and identify beaches that need preferential cleaning.
Copyright © 2020 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Deep learning; Image processing; Plastic marine debris; UAV

Year:  2020        PMID: 32469764     DOI: 10.1016/j.marpolbul.2020.111127

Source DB:  PubMed          Journal:  Mar Pollut Bull        ISSN: 0025-326X            Impact factor:   5.553


  1 in total

1.  A panoramic view and swot analysis of artificial intelligence for achieving the sustainable development goals by 2030: progress and prospects.

Authors:  Iván Palomares; Eugenio Martínez-Cámara; Rosana Montes; Pablo García-Moral; Manuel Chiachio; Juan Chiachio; Sergio Alonso; Francisco J Melero; Daniel Molina; Bárbara Fernández; Cristina Moral; Rosario Marchena; Javier Pérez de Vargas; Francisco Herrera
Journal:  Appl Intell (Dordr)       Date:  2021-06-11       Impact factor: 5.086

  1 in total

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