Object detection based software system for automatic evaluation of cursogramas images

Autores
Pytel, Pablo; Almad, Matías; Leguizamón, Rocío; Vegega, Cinthia; Pollo Cattaneo, Ma Florencia
Año de publicación
2021
Idioma
inglés
Tipo de recurso
parte de libro
Estado
versión publicada
Descripción
The aim of this work is to describe the tasks performed to carry out the development of a software system capable of detecting and recognizing the symbols of Cursogramas in images by using a Deep Learning model that has been trained from scratch. In this way, we seek to assist teachers of an undergraduate subject to automatically evaluate diagrams made as part of the practical exercise of their students. For this purpose, in addition to having carried out a process of understanding the problem and identifying the available data, tasks of technology selection and construction of each of the components that are part of the system are also carried out. Therefore, although the problem domain belongs to the field of university education, thiswork is more related to the engineering and technological aspect of the application of Artificial Intelligence to solve complex problems.
UTN FRBA
Fil: Pytel, Pablo. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Grupo de Estudio en Metodologías de Ingeniería en Software (GEMIS); Argentina.
Fil: Almad, Matías. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Grupo de Estudio en Metodologías de Ingeniería en Software (GEMIS); Argentina.
Fil: Leguizamón, Rocío. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Grupo de Estudio en Metodologías de Ingeniería en Software (GEMIS); Argentina.
Fil: Vegega, Cinthia. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Grupo de Estudio en Metodologías de Ingeniería en Software (GEMIS); Argentina.
Fil: Pollo Cattaneo, Ma Florencia. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Grupo de Estudio en Metodologías de Ingeniería en Software (GEMIS); Argentina.
Peer Reviewed
Fuente
Springer Nature Switzerland AG, 39–54. (2021)
Materia
cursogramas
object detection
deep learning
artificial intelligence
Nivel de accesibilidad
acceso abierto
Condiciones de uso
2024-03-20T22:46:05Z
Repositorio
Repositorio Institucional Abierto (UTN)
Institución
Universidad Tecnológica Nacional
OAI Identificador
oai:ria.utn.edu.ar:20.500.12272/9993
Acceder
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oai_identifier_str oai:ria.utn.edu.ar:20.500.12272/9993
network_acronym_str RIAUTN
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network_name_str Repositorio Institucional Abierto (UTN)
spelling Object detection based software system for automatic evaluation of cursogramas imagesPytel, PabloAlmad, MatíasLeguizamón, RocíoVegega, CinthiaPollo Cattaneo, Ma Florenciacursogramasobject detectiondeep learningartificial intelligenceThe aim of this work is to describe the tasks performed to carry out the development of a software system capable of detecting and recognizing the symbols of Cursogramas in images by using a Deep Learning model that has been trained from scratch. In this way, we seek to assist teachers of an undergraduate subject to automatically evaluate diagrams made as part of the practical exercise of their students. For this purpose, in addition to having carried out a process of understanding the problem and identifying the available data, tasks of technology selection and construction of each of the components that are part of the system are also carried out. Therefore, although the problem domain belongs to the field of university education, thiswork is more related to the engineering and technological aspect of the application of Artificial Intelligence to solve complex problems.UTN FRBAFil: Pytel, Pablo. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Grupo de Estudio en Metodologías de Ingeniería en Software (GEMIS); Argentina.Fil: Almad, Matías. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Grupo de Estudio en Metodologías de Ingeniería en Software (GEMIS); Argentina.Fil: Leguizamón, Rocío. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Grupo de Estudio en Metodologías de Ingeniería en Software (GEMIS); Argentina.Fil: Vegega, Cinthia. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Grupo de Estudio en Metodologías de Ingeniería en Software (GEMIS); Argentina.Fil: Pollo Cattaneo, Ma Florencia. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Grupo de Estudio en Metodologías de Ingeniería en Software (GEMIS); Argentina.Peer ReviewedUTN FRBA2024-03-20T22:46:05Z2024-03-20T22:46:05Z2021-01-01info:eu-repo/semantics/bookPartinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_3248info:ar-repo/semantics/parteDeLibropdfapplication/pdfSpringer Nature Switzerland AG 2021http://hdl.handle.net/20.500.12272/999310.1007/978-3-030-89654-6_4Springer Nature Switzerland AG, 39–54. (2021)reponame:Repositorio Institucional Abierto (UTN)instname:Universidad Tecnológica Nacionalenginfo:eu-repo/semantics/openAccess2024-03-20T22:46:05Zhttp://creativecommons.org/licenses/by-nc-sa/4.0/Atribución-NoComercial-CompartirIgual 4.0 InternacionalPablo Pytel, Matías Almad, Rocío Leguizamón, Cinthia Vegega, Ma Florencia Pollo-CattaneoLicencia Creative Commons Atribución- No Comercial2026-10-01T11:56:48Zoai:ria.utn.edu.ar:20.500.12272/9993instacron:UTNInstitucionalhttp://ria.utn.edu.ar/Universidad públicaNo correspondehttp://ria.utn.edu.ar/oaigestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:a2026-10-01 11:56:49.355Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacionalfalse
dc.title.none.fl_str_mv Object detection based software system for automatic evaluation of cursogramas images
title Object detection based software system for automatic evaluation of cursogramas images
spellingShingle Object detection based software system for automatic evaluation of cursogramas images
Pytel, Pablo
cursogramas
object detection
deep learning
artificial intelligence
title_short Object detection based software system for automatic evaluation of cursogramas images
title_full Object detection based software system for automatic evaluation of cursogramas images
title_fullStr Object detection based software system for automatic evaluation of cursogramas images
title_full_unstemmed Object detection based software system for automatic evaluation of cursogramas images
title_sort Object detection based software system for automatic evaluation of cursogramas images
dc.creator.none.fl_str_mv Pytel, Pablo
Almad, Matías
Leguizamón, Rocío
Vegega, Cinthia
Pollo Cattaneo, Ma Florencia
author Pytel, Pablo
author_facet Pytel, Pablo
Almad, Matías
Leguizamón, Rocío
Vegega, Cinthia
Pollo Cattaneo, Ma Florencia
author_role author
author2 Almad, Matías
Leguizamón, Rocío
Vegega, Cinthia
Pollo Cattaneo, Ma Florencia
author2_role author
author
author
author
dc.subject.none.fl_str_mv cursogramas
object detection
deep learning
artificial intelligence
topic cursogramas
object detection
deep learning
artificial intelligence
dc.description.none.fl_txt_mv The aim of this work is to describe the tasks performed to carry out the development of a software system capable of detecting and recognizing the symbols of Cursogramas in images by using a Deep Learning model that has been trained from scratch. In this way, we seek to assist teachers of an undergraduate subject to automatically evaluate diagrams made as part of the practical exercise of their students. For this purpose, in addition to having carried out a process of understanding the problem and identifying the available data, tasks of technology selection and construction of each of the components that are part of the system are also carried out. Therefore, although the problem domain belongs to the field of university education, thiswork is more related to the engineering and technological aspect of the application of Artificial Intelligence to solve complex problems.
UTN FRBA
Fil: Pytel, Pablo. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Grupo de Estudio en Metodologías de Ingeniería en Software (GEMIS); Argentina.
Fil: Almad, Matías. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Grupo de Estudio en Metodologías de Ingeniería en Software (GEMIS); Argentina.
Fil: Leguizamón, Rocío. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Grupo de Estudio en Metodologías de Ingeniería en Software (GEMIS); Argentina.
Fil: Vegega, Cinthia. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Grupo de Estudio en Metodologías de Ingeniería en Software (GEMIS); Argentina.
Fil: Pollo Cattaneo, Ma Florencia. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Grupo de Estudio en Metodologías de Ingeniería en Software (GEMIS); Argentina.
Peer Reviewed
description The aim of this work is to describe the tasks performed to carry out the development of a software system capable of detecting and recognizing the symbols of Cursogramas in images by using a Deep Learning model that has been trained from scratch. In this way, we seek to assist teachers of an undergraduate subject to automatically evaluate diagrams made as part of the practical exercise of their students. For this purpose, in addition to having carried out a process of understanding the problem and identifying the available data, tasks of technology selection and construction of each of the components that are part of the system are also carried out. Therefore, although the problem domain belongs to the field of university education, thiswork is more related to the engineering and technological aspect of the application of Artificial Intelligence to solve complex problems.
publishDate 2021
dc.date.none.fl_str_mv 2021-01-01
2024-03-20T22:46:05Z
2024-03-20T22:46:05Z
dc.type.none.fl_str_mv info:eu-repo/semantics/bookPart
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_3248
info:ar-repo/semantics/parteDeLibro
format bookPart
status_str publishedVersion
dc.identifier.none.fl_str_mv Springer Nature Switzerland AG 2021
http://hdl.handle.net/20.500.12272/9993
10.1007/978-3-030-89654-6_4
identifier_str_mv Springer Nature Switzerland AG 2021
10.1007/978-3-030-89654-6_4
url http://hdl.handle.net/20.500.12272/9993
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
2024-03-20T22:46:05Z
http://creativecommons.org/licenses/by-nc-sa/4.0/
Atribución-NoComercial-CompartirIgual 4.0 Internacional
Pablo Pytel, Matías Almad, Rocío Leguizamón, Cinthia Vegega, Ma Florencia Pollo-Cattaneo
Licencia Creative Commons Atribución- No Comercial
eu_rights_str_mv openAccess
rights_invalid_str_mv 2024-03-20T22:46:05Z
http://creativecommons.org/licenses/by-nc-sa/4.0/
Atribución-NoComercial-CompartirIgual 4.0 Internacional
Pablo Pytel, Matías Almad, Rocío Leguizamón, Cinthia Vegega, Ma Florencia Pollo-Cattaneo
Licencia Creative Commons Atribución- No Comercial
dc.format.none.fl_str_mv pdf
application/pdf
dc.publisher.none.fl_str_mv UTN FRBA
publisher.none.fl_str_mv UTN FRBA
dc.source.none.fl_str_mv Springer Nature Switzerland AG, 39–54. (2021)
reponame:Repositorio Institucional Abierto (UTN)
instname:Universidad Tecnológica Nacional
reponame_str Repositorio Institucional Abierto (UTN)
collection Repositorio Institucional Abierto (UTN)
instname_str Universidad Tecnológica Nacional
repository.name.fl_str_mv Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacional
repository.mail.fl_str_mv gestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.ar
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score 13.365483

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