An experimental study on marine debris location and recognition using object detection
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http://hdl.handle.net/10045/130834
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Camp Dublin Core | Valor | Idioma |
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dc.contributor | Reconocimiento de Formas e Inteligencia Artificial | es_ES |
dc.contributor.author | Sánchez Ferrer, Alejandro | - |
dc.contributor.author | Valero-Mas, Jose J. | - |
dc.contributor.author | Gallego, Antonio-Javier | - |
dc.contributor.author | Calvo-Zaragoza, Jorge | - |
dc.contributor.other | Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos | es_ES |
dc.contributor.other | Universidad de Alicante. Instituto Universitario de Investigación Informática | es_ES |
dc.date.accessioned | 2023-01-09T10:21:29Z | - |
dc.date.available | 2023-01-09T10:21:29Z | - |
dc.date.issued | 2022-12-26 | - |
dc.identifier.citation | Pattern Recognition Letters. 2023, 168: 154-161. https://doi.org/10.1016/j.patrec.2022.12.019 | es_ES |
dc.identifier.issn | 0167-8655 (Print) | - |
dc.identifier.issn | 1872-7344 (Online) | - |
dc.identifier.uri | http://hdl.handle.net/10045/130834 | - |
dc.description.abstract | The large amount of debris in our oceans is a global problem that dramatically impacts marine fauna and flora. While a large number of human-based campaigns have been proposed to tackle this issue, these efforts have been deemed insufficient due to the insurmountable amount of existing litter. In response to that, there exists a high interest in the use of autonomous underwater vehicles (AUV) that may locate, identify, and collect this garbage automatically. To perform such a task, AUVs consider state-of-the-art object detection techniques based on deep neural networks due to their reported high performance. Nevertheless, these techniques generally require large amounts of data with fine-grained annotations. In this work, we explore the capabilities of the reference object detector Mask Region-based Convolutional Neural Networks for automatic marine debris location and classification in the context of limited data availability. Considering the recent CleanSea corpus, we pose several scenarios regarding the amount of available train data and study the possibility of mitigating the adverse effects of data scarcity with synthetic marine scenes. Our results achieve a new state of the art in the task, establishing a new reference for future research. In addition, it is shown that the task still has room for improvement and that the lack of data can be somehow alleviated, yet to a limited extent. | es_ES |
dc.description.sponsorship | This work was supported by the Generalitat Valenciana (GV) TADMar project INVEST/2022/450. Computing resources were provided by the GV and the European Union through the FEDER funding program (IDIFEDER/2020/003). The second author is supported by grant APOSTD/2020/256 from GV. | es_ES |
dc.language | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.rights | © 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). | es_ES |
dc.subject | Underwater debris detection | es_ES |
dc.subject | Marine pollution | es_ES |
dc.subject | Deep neural networks | es_ES |
dc.subject | Object detection | es_ES |
dc.title | An experimental study on marine debris location and recognition using object detection | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.peerreviewed | si | es_ES |
dc.identifier.doi | 10.1016/j.patrec.2022.12.019 | - |
dc.relation.publisherversion | https://doi.org/10.1016/j.patrec.2022.12.019 | es_ES |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es_ES |
Apareix a la col·lecció: | INV - GRFIA - Artículos de Revistas |
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