Accumulated CA-CFAR Process in 2-D for Online Object Detection From Sidescan Sonar Data
- Autores
- Villar, Sebastian Aldo; Acosta, Gerardo Gabriel
- Año de publicación
- 2014
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- This paper describes a novel approach to object detection from sidescan sonar (SSS) acoustical images. The current techniques of acoustical images processing consume a great deal of time and computational resources with many parameters to tune in order to obtain good quality images. This is due to the handling of the large data volume generated by these kinds of devices. The technique proposed in this work does not make any a priori assumption about the nature of the SSS image to be processed. However, it is able to make a segmentation of the image into two types of regions: acoustical highlight and seafloor reverberation areas, and based on this, it makes detection. The developed algorithm to achieve this consists of a migration and adaptation of a technique widely used in radar technology for detecting moving objects. This radar technique is known as the cell average-constant false alarm rate (CA-CFAR). This paper presents a drastic improvement of such approach by making an extension into 2-D analysis of the SSS image, in a way similar to integral image used in CA-CFAR detection for pulse Doppler radar. In this form, optimization of the computational effort is achieved. This new technique was called the accumulated cell average-constant false alarm rate in 2-D (ACA-CFAR 2-D). It was applied to pipeline detection and tracking with a very interesting degree of success. In addition, this technique provides similar results to image segmentation with respect to other frequently used approaches, but with much less computational resources and parameters to set. Its simplicity is a strong support of its robustness and accuracy. This feature makes it particularly attractive for using it in real-time applications, such as underwater robotics perception systems. This proposal was tested experimentally with acoustical data from SSS and the results detecting pipelines, and other shapes like sunken vessels or airplanes, are presented in this paper. Likewise, an experimental co- parison with the results obtained with inverse undecimated discrete wavelet transform (UDWT) and active contours techniques is also presented.
Fil: Villar, Sebastian Aldo. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ingeniería Olavarria. Departamento de Electromecánica. Grupo INTELYMEC; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires; Argentina
Fil: Acosta, Gerardo Gabriel. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ingeniería Olavarria. Departamento de Electromecánica. Grupo INTELYMEC; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires; Argentina - Materia
-
Cell Average-Constant False Alarm Rate
Online Object Detection
Sidescan Sonar
Sonar Imagery - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/5247
Ver los metadatos del registro completo
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Accumulated CA-CFAR Process in 2-D for Online Object Detection From Sidescan Sonar DataVillar, Sebastian AldoAcosta, Gerardo GabrielCell Average-Constant False Alarm RateOnline Object DetectionSidescan SonarSonar Imageryhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2This paper describes a novel approach to object detection from sidescan sonar (SSS) acoustical images. The current techniques of acoustical images processing consume a great deal of time and computational resources with many parameters to tune in order to obtain good quality images. This is due to the handling of the large data volume generated by these kinds of devices. The technique proposed in this work does not make any a priori assumption about the nature of the SSS image to be processed. However, it is able to make a segmentation of the image into two types of regions: acoustical highlight and seafloor reverberation areas, and based on this, it makes detection. The developed algorithm to achieve this consists of a migration and adaptation of a technique widely used in radar technology for detecting moving objects. This radar technique is known as the cell average-constant false alarm rate (CA-CFAR). This paper presents a drastic improvement of such approach by making an extension into 2-D analysis of the SSS image, in a way similar to integral image used in CA-CFAR detection for pulse Doppler radar. In this form, optimization of the computational effort is achieved. This new technique was called the accumulated cell average-constant false alarm rate in 2-D (ACA-CFAR 2-D). It was applied to pipeline detection and tracking with a very interesting degree of success. In addition, this technique provides similar results to image segmentation with respect to other frequently used approaches, but with much less computational resources and parameters to set. Its simplicity is a strong support of its robustness and accuracy. This feature makes it particularly attractive for using it in real-time applications, such as underwater robotics perception systems. This proposal was tested experimentally with acoustical data from SSS and the results detecting pipelines, and other shapes like sunken vessels or airplanes, are presented in this paper. Likewise, an experimental co- parison with the results obtained with inverse undecimated discrete wavelet transform (UDWT) and active contours techniques is also presented.Fil: Villar, Sebastian Aldo. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ingeniería Olavarria. Departamento de Electromecánica. Grupo INTELYMEC; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires; ArgentinaFil: Acosta, Gerardo Gabriel. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ingeniería Olavarria. Departamento de Electromecánica. Grupo INTELYMEC; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires; ArgentinaInstitute Of Electrical And Electronics Engineers2014-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/zipapplication/pdfapplication/ziphttp://hdl.handle.net/11336/5247Villar, Sebastian Aldo; Acosta, Gerardo Gabriel; Accumulated CA-CFAR Process in 2-D for Online Object Detection From Sidescan Sonar Data; Institute Of Electrical And Electronics Engineers; Ieee Journal Of Oceanic Engineering; 40; 3; 10-2014; 558-5690364-9059enginfo:eu-repo/semantics/altIdentifier/url/http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6930826info:eu-repo/semantics/altIdentifier/doi/10.1109/JOE.2014.2356951info:eu-repo/semantics/altIdentifier/doi/info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:03:01Zoai:ri.conicet.gov.ar:11336/5247instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:03:02.188CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Accumulated CA-CFAR Process in 2-D for Online Object Detection From Sidescan Sonar Data |
title |
Accumulated CA-CFAR Process in 2-D for Online Object Detection From Sidescan Sonar Data |
spellingShingle |
Accumulated CA-CFAR Process in 2-D for Online Object Detection From Sidescan Sonar Data Villar, Sebastian Aldo Cell Average-Constant False Alarm Rate Online Object Detection Sidescan Sonar Sonar Imagery |
title_short |
Accumulated CA-CFAR Process in 2-D for Online Object Detection From Sidescan Sonar Data |
title_full |
Accumulated CA-CFAR Process in 2-D for Online Object Detection From Sidescan Sonar Data |
title_fullStr |
Accumulated CA-CFAR Process in 2-D for Online Object Detection From Sidescan Sonar Data |
title_full_unstemmed |
Accumulated CA-CFAR Process in 2-D for Online Object Detection From Sidescan Sonar Data |
title_sort |
Accumulated CA-CFAR Process in 2-D for Online Object Detection From Sidescan Sonar Data |
dc.creator.none.fl_str_mv |
Villar, Sebastian Aldo Acosta, Gerardo Gabriel |
author |
Villar, Sebastian Aldo |
author_facet |
Villar, Sebastian Aldo Acosta, Gerardo Gabriel |
author_role |
author |
author2 |
Acosta, Gerardo Gabriel |
author2_role |
author |
dc.subject.none.fl_str_mv |
Cell Average-Constant False Alarm Rate Online Object Detection Sidescan Sonar Sonar Imagery |
topic |
Cell Average-Constant False Alarm Rate Online Object Detection Sidescan Sonar Sonar Imagery |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/2.2 https://purl.org/becyt/ford/2 |
dc.description.none.fl_txt_mv |
This paper describes a novel approach to object detection from sidescan sonar (SSS) acoustical images. The current techniques of acoustical images processing consume a great deal of time and computational resources with many parameters to tune in order to obtain good quality images. This is due to the handling of the large data volume generated by these kinds of devices. The technique proposed in this work does not make any a priori assumption about the nature of the SSS image to be processed. However, it is able to make a segmentation of the image into two types of regions: acoustical highlight and seafloor reverberation areas, and based on this, it makes detection. The developed algorithm to achieve this consists of a migration and adaptation of a technique widely used in radar technology for detecting moving objects. This radar technique is known as the cell average-constant false alarm rate (CA-CFAR). This paper presents a drastic improvement of such approach by making an extension into 2-D analysis of the SSS image, in a way similar to integral image used in CA-CFAR detection for pulse Doppler radar. In this form, optimization of the computational effort is achieved. This new technique was called the accumulated cell average-constant false alarm rate in 2-D (ACA-CFAR 2-D). It was applied to pipeline detection and tracking with a very interesting degree of success. In addition, this technique provides similar results to image segmentation with respect to other frequently used approaches, but with much less computational resources and parameters to set. Its simplicity is a strong support of its robustness and accuracy. This feature makes it particularly attractive for using it in real-time applications, such as underwater robotics perception systems. This proposal was tested experimentally with acoustical data from SSS and the results detecting pipelines, and other shapes like sunken vessels or airplanes, are presented in this paper. Likewise, an experimental co- parison with the results obtained with inverse undecimated discrete wavelet transform (UDWT) and active contours techniques is also presented. Fil: Villar, Sebastian Aldo. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ingeniería Olavarria. Departamento de Electromecánica. Grupo INTELYMEC; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires; Argentina Fil: Acosta, Gerardo Gabriel. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ingeniería Olavarria. Departamento de Electromecánica. Grupo INTELYMEC; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires; Argentina |
description |
This paper describes a novel approach to object detection from sidescan sonar (SSS) acoustical images. The current techniques of acoustical images processing consume a great deal of time and computational resources with many parameters to tune in order to obtain good quality images. This is due to the handling of the large data volume generated by these kinds of devices. The technique proposed in this work does not make any a priori assumption about the nature of the SSS image to be processed. However, it is able to make a segmentation of the image into two types of regions: acoustical highlight and seafloor reverberation areas, and based on this, it makes detection. The developed algorithm to achieve this consists of a migration and adaptation of a technique widely used in radar technology for detecting moving objects. This radar technique is known as the cell average-constant false alarm rate (CA-CFAR). This paper presents a drastic improvement of such approach by making an extension into 2-D analysis of the SSS image, in a way similar to integral image used in CA-CFAR detection for pulse Doppler radar. In this form, optimization of the computational effort is achieved. This new technique was called the accumulated cell average-constant false alarm rate in 2-D (ACA-CFAR 2-D). It was applied to pipeline detection and tracking with a very interesting degree of success. In addition, this technique provides similar results to image segmentation with respect to other frequently used approaches, but with much less computational resources and parameters to set. Its simplicity is a strong support of its robustness and accuracy. This feature makes it particularly attractive for using it in real-time applications, such as underwater robotics perception systems. This proposal was tested experimentally with acoustical data from SSS and the results detecting pipelines, and other shapes like sunken vessels or airplanes, are presented in this paper. Likewise, an experimental co- parison with the results obtained with inverse undecimated discrete wavelet transform (UDWT) and active contours techniques is also presented. |
publishDate |
2014 |
dc.date.none.fl_str_mv |
2014-10 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://hdl.handle.net/11336/5247 Villar, Sebastian Aldo; Acosta, Gerardo Gabriel; Accumulated CA-CFAR Process in 2-D for Online Object Detection From Sidescan Sonar Data; Institute Of Electrical And Electronics Engineers; Ieee Journal Of Oceanic Engineering; 40; 3; 10-2014; 558-569 0364-9059 |
url |
http://hdl.handle.net/11336/5247 |
identifier_str_mv |
Villar, Sebastian Aldo; Acosta, Gerardo Gabriel; Accumulated CA-CFAR Process in 2-D for Online Object Detection From Sidescan Sonar Data; Institute Of Electrical And Electronics Engineers; Ieee Journal Of Oceanic Engineering; 40; 3; 10-2014; 558-569 0364-9059 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6930826 info:eu-repo/semantics/altIdentifier/doi/10.1109/JOE.2014.2356951 info:eu-repo/semantics/altIdentifier/doi/ |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
dc.format.none.fl_str_mv |
application/pdf application/pdf application/zip application/pdf application/zip |
dc.publisher.none.fl_str_mv |
Institute Of Electrical And Electronics Engineers |
publisher.none.fl_str_mv |
Institute Of Electrical And Electronics Engineers |
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reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
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Consejo Nacional de Investigaciones Científicas y Técnicas |
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CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
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dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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