Experimental evaluation of segmentation algorithms for corner detection in sonar images

Detalhes bibliográficos
Autor(a) principal: Pedro Miguel Linhares Oliveira
Data de Publicação: 2019
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://hdl.handle.net/10216/121944
Resumo: The development of AUVs represents one of the latest great achievements of engineering and science for the exploration and monitoring of the underwater world. The use of these vehicles facilitates the collection of data and monitoring of these environments, allowing us to perform previously impossible operations. Autonomous navigation continues to present many challenges. The question of total autonomy is yet to be solved. Currently, AUVs are not able to navigate without some outside assistance for long periods. When underwater, due to the high attenuation suffered by the GPS and radio-frequency signals, the use of acoustic communications and sensors offers better results. Imaging sonars have been one of the most appealing technologies for acquiring information in underwater environments because they can yield acoustic images of the surroundings and have a set of particular characteristics that are well suited for obstacle detection and characterization tasks. Corners usually appear very distinct from the rest of the scene in sonar images, generally characterized by sharp intensities in a vertical direction. The detection of corners is particularly useful in human-structured environments such as tanks because the knowledge on their position provides a way to compute the vehicle position inside it. The combination of some basic operations typically used for image segmentation can be applied to the raw sonar image to detect and localize these spots. This dissertation proposes and evaluates with experimental data a set of image segmentation algorithms for corner detection in sonar scans. A detailed description of the necessary steps to accomplish this is provided as well as a critical analysis of the results following a few relevant metrics for autonomous navigation.
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spelling Experimental evaluation of segmentation algorithms for corner detection in sonar imagesEngenharia electrotécnica, electrónica e informáticaElectrical engineering, Electronic engineering, Information engineeringThe development of AUVs represents one of the latest great achievements of engineering and science for the exploration and monitoring of the underwater world. The use of these vehicles facilitates the collection of data and monitoring of these environments, allowing us to perform previously impossible operations. Autonomous navigation continues to present many challenges. The question of total autonomy is yet to be solved. Currently, AUVs are not able to navigate without some outside assistance for long periods. When underwater, due to the high attenuation suffered by the GPS and radio-frequency signals, the use of acoustic communications and sensors offers better results. Imaging sonars have been one of the most appealing technologies for acquiring information in underwater environments because they can yield acoustic images of the surroundings and have a set of particular characteristics that are well suited for obstacle detection and characterization tasks. Corners usually appear very distinct from the rest of the scene in sonar images, generally characterized by sharp intensities in a vertical direction. The detection of corners is particularly useful in human-structured environments such as tanks because the knowledge on their position provides a way to compute the vehicle position inside it. The combination of some basic operations typically used for image segmentation can be applied to the raw sonar image to detect and localize these spots. This dissertation proposes and evaluates with experimental data a set of image segmentation algorithms for corner detection in sonar scans. A detailed description of the necessary steps to accomplish this is provided as well as a critical analysis of the results following a few relevant metrics for autonomous navigation.2019-07-152019-07-15T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/10216/121944TID:202396746engPedro Miguel Linhares Oliveirainfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-29T14:29:54Zoai:repositorio-aberto.up.pt:10216/121944Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:02:34.263398Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Experimental evaluation of segmentation algorithms for corner detection in sonar images
title Experimental evaluation of segmentation algorithms for corner detection in sonar images
spellingShingle Experimental evaluation of segmentation algorithms for corner detection in sonar images
Pedro Miguel Linhares Oliveira
Engenharia electrotécnica, electrónica e informática
Electrical engineering, Electronic engineering, Information engineering
title_short Experimental evaluation of segmentation algorithms for corner detection in sonar images
title_full Experimental evaluation of segmentation algorithms for corner detection in sonar images
title_fullStr Experimental evaluation of segmentation algorithms for corner detection in sonar images
title_full_unstemmed Experimental evaluation of segmentation algorithms for corner detection in sonar images
title_sort Experimental evaluation of segmentation algorithms for corner detection in sonar images
author Pedro Miguel Linhares Oliveira
author_facet Pedro Miguel Linhares Oliveira
author_role author
dc.contributor.author.fl_str_mv Pedro Miguel Linhares Oliveira
dc.subject.por.fl_str_mv Engenharia electrotécnica, electrónica e informática
Electrical engineering, Electronic engineering, Information engineering
topic Engenharia electrotécnica, electrónica e informática
Electrical engineering, Electronic engineering, Information engineering
description The development of AUVs represents one of the latest great achievements of engineering and science for the exploration and monitoring of the underwater world. The use of these vehicles facilitates the collection of data and monitoring of these environments, allowing us to perform previously impossible operations. Autonomous navigation continues to present many challenges. The question of total autonomy is yet to be solved. Currently, AUVs are not able to navigate without some outside assistance for long periods. When underwater, due to the high attenuation suffered by the GPS and radio-frequency signals, the use of acoustic communications and sensors offers better results. Imaging sonars have been one of the most appealing technologies for acquiring information in underwater environments because they can yield acoustic images of the surroundings and have a set of particular characteristics that are well suited for obstacle detection and characterization tasks. Corners usually appear very distinct from the rest of the scene in sonar images, generally characterized by sharp intensities in a vertical direction. The detection of corners is particularly useful in human-structured environments such as tanks because the knowledge on their position provides a way to compute the vehicle position inside it. The combination of some basic operations typically used for image segmentation can be applied to the raw sonar image to detect and localize these spots. This dissertation proposes and evaluates with experimental data a set of image segmentation algorithms for corner detection in sonar scans. A detailed description of the necessary steps to accomplish this is provided as well as a critical analysis of the results following a few relevant metrics for autonomous navigation.
publishDate 2019
dc.date.none.fl_str_mv 2019-07-15
2019-07-15T00:00:00Z
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