Real-time object detection and classification of small and similar figures in image processing
- Autores
- Algorry, Aldo M.; Giles Garcia, Arian; Wolfmann, A Gustavo
- Año de publicación
- 2017
- Idioma
- inglés
- Tipo de recurso
- documento de conferencia
- Estado
- versión publicada
- Descripción
- Fil: Algorry, Aldo M. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Laboratorio de Computación; Argentina.
Fil: Giles García, Arian. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Laboratorio de Computación; Argentina.
Fil: Wolfmann, A. Gustavo. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Laboratorio de Computación; Argentina.
In the current work we present an image processing architecture for realtime object detection and classification. We use a combination of the widely known techniques YOLO v2 and Convolutional Neural Network classifiers, obtaining great improvements in the detection level with a minimum loss of performance compared to YOLO v2. We apply this technique in a domain where the objects to be detected are like each other and occupy small areas in the images, as it occurs with video traffic signs domain. With this approach, we achieve real-time video processingcapabilities for a test set of 10 different signs classes. The tests results achieved process time levels faster than widely recognized algorithms, such as Fast R-CNN and Faster R-CNN, so it allows to project its use in real-time object detection.
Fil: Algorry, Aldo M. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Laboratorio de Computación; Argentina.
Fil: Giles García, Arian. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Laboratorio de Computación; Argentina.
Fil: Wolfmann, A. Gustavo. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Laboratorio de Computación; Argentina.
Otras Ciencias de la Computación e Información - Materia
-
Traffic Signs
Lightweight CNN
YOLO
Computación - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- Repositorio
- Institución
- Universidad Nacional de Córdoba
- OAI Identificador
- oai:rdu.unc.edu.ar:11086/556430
Ver los metadatos del registro completo
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Real-time object detection and classification of small and similar figures in image processingAlgorry, Aldo M.Giles Garcia, ArianWolfmann, A GustavoTraffic SignsLightweight CNNYOLOComputaciónFil: Algorry, Aldo M. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Laboratorio de Computación; Argentina.Fil: Giles García, Arian. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Laboratorio de Computación; Argentina.Fil: Wolfmann, A. Gustavo. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Laboratorio de Computación; Argentina.In the current work we present an image processing architecture for realtime object detection and classification. We use a combination of the widely known techniques YOLO v2 and Convolutional Neural Network classifiers, obtaining great improvements in the detection level with a minimum loss of performance compared to YOLO v2. We apply this technique in a domain where the objects to be detected are like each other and occupy small areas in the images, as it occurs with video traffic signs domain. With this approach, we achieve real-time video processingcapabilities for a test set of 10 different signs classes. The tests results achieved process time levels faster than widely recognized algorithms, such as Fast R-CNN and Faster R-CNN, so it allows to project its use in real-time object detection.Fil: Algorry, Aldo M. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Laboratorio de Computación; Argentina.Fil: Giles García, Arian. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Laboratorio de Computación; Argentina.Fil: Wolfmann, A. Gustavo. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Laboratorio de Computación; Argentina.Otras Ciencias de la Computación e Información2017info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf9781538626528http://hdl.handle.net/11086/556430enginfo:eu-repo/semantics/openAccessreponame:Repositorio Digital Universitario (UNC)instname:Universidad Nacional de Córdobainstacron:UNC2025-09-29T13:44:02Zoai:rdu.unc.edu.ar:11086/556430Institucionalhttps://rdu.unc.edu.ar/Universidad públicaNo correspondehttp://rdu.unc.edu.ar/oai/snrdoca.unc@gmail.comArgentinaNo correspondeNo correspondeNo correspondeopendoar:25722025-09-29 13:44:02.243Repositorio Digital Universitario (UNC) - Universidad Nacional de Córdobafalse |
dc.title.none.fl_str_mv |
Real-time object detection and classification of small and similar figures in image processing |
title |
Real-time object detection and classification of small and similar figures in image processing |
spellingShingle |
Real-time object detection and classification of small and similar figures in image processing Algorry, Aldo M. Traffic Signs Lightweight CNN YOLO Computación |
title_short |
Real-time object detection and classification of small and similar figures in image processing |
title_full |
Real-time object detection and classification of small and similar figures in image processing |
title_fullStr |
Real-time object detection and classification of small and similar figures in image processing |
title_full_unstemmed |
Real-time object detection and classification of small and similar figures in image processing |
title_sort |
Real-time object detection and classification of small and similar figures in image processing |
dc.creator.none.fl_str_mv |
Algorry, Aldo M. Giles Garcia, Arian Wolfmann, A Gustavo |
author |
Algorry, Aldo M. |
author_facet |
Algorry, Aldo M. Giles Garcia, Arian Wolfmann, A Gustavo |
author_role |
author |
author2 |
Giles Garcia, Arian Wolfmann, A Gustavo |
author2_role |
author author |
dc.subject.none.fl_str_mv |
Traffic Signs Lightweight CNN YOLO Computación |
topic |
Traffic Signs Lightweight CNN YOLO Computación |
dc.description.none.fl_txt_mv |
Fil: Algorry, Aldo M. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Laboratorio de Computación; Argentina. Fil: Giles García, Arian. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Laboratorio de Computación; Argentina. Fil: Wolfmann, A. Gustavo. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Laboratorio de Computación; Argentina. In the current work we present an image processing architecture for realtime object detection and classification. We use a combination of the widely known techniques YOLO v2 and Convolutional Neural Network classifiers, obtaining great improvements in the detection level with a minimum loss of performance compared to YOLO v2. We apply this technique in a domain where the objects to be detected are like each other and occupy small areas in the images, as it occurs with video traffic signs domain. With this approach, we achieve real-time video processingcapabilities for a test set of 10 different signs classes. The tests results achieved process time levels faster than widely recognized algorithms, such as Fast R-CNN and Faster R-CNN, so it allows to project its use in real-time object detection. Fil: Algorry, Aldo M. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Laboratorio de Computación; Argentina. Fil: Giles García, Arian. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Laboratorio de Computación; Argentina. Fil: Wolfmann, A. Gustavo. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Laboratorio de Computación; Argentina. Otras Ciencias de la Computación e Información |
description |
Fil: Algorry, Aldo M. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Laboratorio de Computación; Argentina. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_5794 info:ar-repo/semantics/documentoDeConferencia |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
9781538626528 http://hdl.handle.net/11086/556430 |
identifier_str_mv |
9781538626528 |
url |
http://hdl.handle.net/11086/556430 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositorio Digital Universitario (UNC) instname:Universidad Nacional de Córdoba instacron:UNC |
reponame_str |
Repositorio Digital Universitario (UNC) |
collection |
Repositorio Digital Universitario (UNC) |
instname_str |
Universidad Nacional de Córdoba |
instacron_str |
UNC |
institution |
UNC |
repository.name.fl_str_mv |
Repositorio Digital Universitario (UNC) - Universidad Nacional de Córdoba |
repository.mail.fl_str_mv |
oca.unc@gmail.com |
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1844618971979972608 |
score |
13.070432 |