Advances in Instance Segmentation: Technologies, Metrics and Applications in Computer Vision

Instance segmentation is an advanced technique in computer vision that focuses on identifying and classifying each individual object in an image at the pixel level. Unlike semantic segmentation, which groups pixels of similar objects without distinguishing between different instances, instance segme...

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Detalles Bibliográficos
Autores: Molina, José Manuel, Llerena Caña, Juan Pedro|||0000-0002-3476-6261, Usero Aragonés, Luis|||0000-0001-8658-9992, Patricio Guisado, Miguel Ángel
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universidad de Alcalá (UAH)
Repositorio:e_Buah Biblioteca Digital Universidad de Alcalá
Idioma:inglés
OAI Identifier:oai:ebuah.uah.es:10017/64626
Acceso en línea:http://hdl.handle.net/10017/64626
https://dx.doi.org/10.1016/j.neucom.2025.129584
Access Level:acceso abierto
Palabra clave:Computer Vision
Instance Segmentation
Evaluation Metrics
Datasets
Informática
Computer science
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spelling Advances in Instance Segmentation: Technologies, Metrics and Applications in Computer VisionMolina, José ManuelLlerena Caña, Juan Pedro|||0000-0002-3476-6261Usero Aragonés, Luis|||0000-0001-8658-9992Patricio Guisado, Miguel ÁngelComputer VisionInstance SegmentationEvaluation MetricsDatasetsInformáticaComputer scienceInstance segmentation is an advanced technique in computer vision that focuses on identifying and classifying each individual object in an image at the pixel level. Unlike semantic segmentation, which groups pixels of similar objects without distinguishing between different instances, instance segmentation assigns unique labels to each object, even if they are of the same class. This makes it possible not only to detect the presence and category of objects in an image but also to locate each specific instance and clearly distinguish them from each other. This problem not only advances the technical and theoretical understanding of how machines see and process digital images, but also has a direct impact on various industries and sectors where computer vision is an essential part of the system. In this paper, we present the current deep learning-based technologies, the metrics used for their evaluation, and a review of general and concrete datasets in general and drone-specific contexts. The results of this study provide a compendium of easily deployable deep learning-based technologies. This review paper aims to accelerate the process of understanding and using instance segmentation technologies for the reader.20252025-01-2720252025-01-2720272027-01-27journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10017/64626https://dx.doi.org/10.1016/j.neucom.2025.129584reponame:e_Buah Biblioteca Digital Universidad de Alcaláinstname:Universidad de Alcalá (UAH)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:ebuah.uah.es:10017/646262026-06-18T11:13:07Z
dc.title.none.fl_str_mv Advances in Instance Segmentation: Technologies, Metrics and Applications in Computer Vision
title Advances in Instance Segmentation: Technologies, Metrics and Applications in Computer Vision
spellingShingle Advances in Instance Segmentation: Technologies, Metrics and Applications in Computer Vision
Molina, José Manuel
Computer Vision
Instance Segmentation
Evaluation Metrics
Datasets
Informática
Computer science
title_short Advances in Instance Segmentation: Technologies, Metrics and Applications in Computer Vision
title_full Advances in Instance Segmentation: Technologies, Metrics and Applications in Computer Vision
title_fullStr Advances in Instance Segmentation: Technologies, Metrics and Applications in Computer Vision
title_full_unstemmed Advances in Instance Segmentation: Technologies, Metrics and Applications in Computer Vision
title_sort Advances in Instance Segmentation: Technologies, Metrics and Applications in Computer Vision
dc.creator.none.fl_str_mv Molina, José Manuel
Llerena Caña, Juan Pedro|||0000-0002-3476-6261
Usero Aragonés, Luis|||0000-0001-8658-9992
Patricio Guisado, Miguel Ángel
author Molina, José Manuel
author_facet Molina, José Manuel
Llerena Caña, Juan Pedro|||0000-0002-3476-6261
Usero Aragonés, Luis|||0000-0001-8658-9992
Patricio Guisado, Miguel Ángel
author_role author
author2 Llerena Caña, Juan Pedro|||0000-0002-3476-6261
Usero Aragonés, Luis|||0000-0001-8658-9992
Patricio Guisado, Miguel Ángel
author2_role author
author
author
dc.subject.none.fl_str_mv Computer Vision
Instance Segmentation
Evaluation Metrics
Datasets
Informática
Computer science
topic Computer Vision
Instance Segmentation
Evaluation Metrics
Datasets
Informática
Computer science
description Instance segmentation is an advanced technique in computer vision that focuses on identifying and classifying each individual object in an image at the pixel level. Unlike semantic segmentation, which groups pixels of similar objects without distinguishing between different instances, instance segmentation assigns unique labels to each object, even if they are of the same class. This makes it possible not only to detect the presence and category of objects in an image but also to locate each specific instance and clearly distinguish them from each other. This problem not only advances the technical and theoretical understanding of how machines see and process digital images, but also has a direct impact on various industries and sectors where computer vision is an essential part of the system. In this paper, we present the current deep learning-based technologies, the metrics used for their evaluation, and a review of general and concrete datasets in general and drone-specific contexts. The results of this study provide a compendium of easily deployable deep learning-based technologies. This review paper aims to accelerate the process of understanding and using instance segmentation technologies for the reader.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-01-27
2025
2025-01-27
2027
2027-01-27
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10017/64626
https://dx.doi.org/10.1016/j.neucom.2025.129584
url http://hdl.handle.net/10017/64626
https://dx.doi.org/10.1016/j.neucom.2025.129584
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:e_Buah Biblioteca Digital Universidad de Alcalá
instname:Universidad de Alcalá (UAH)
instname_str Universidad de Alcalá (UAH)
reponame_str e_Buah Biblioteca Digital Universidad de Alcalá
collection e_Buah Biblioteca Digital Universidad de Alcalá
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