Novel automatic scorpion-detection and -recognition system based on machine-learning techniques

Autores
Giambelluca, Francisco Luis; Cappelletti, Marcelo Ángel; Osio, Jorge; Giambelluca, Luis Alberto
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
All species of scorpions have the ability to inoculate venom, some of them even with the possibility of killing a human. Therefore, early detection and identification is essential to minimize scorpion stings. In this paper, we propose a novel automatic system for the detection and recognition of scorpions using computer vision and machine learning approaches. Two complementary image processing techniques were used for the proposed detection method in order to accurately and reliably detect the presence of scorpions. The first based on the fluorescence characteristics of scorpions when are exposed to ultraviolet (UV) light, and the second on the shape features of the scorpions. On the other hand, three models based on machine learning algorithms for the image recognition and classification of scorpions have been compared. In particular, the three species of scorpions found in La Plata city (Argentina): Bothriurus bonariensis (of no sanitary importance), and Tityus trivittatus and Tityus confluence (both of sanitary importance), have been researched using the Local Binary Pattern Histogram (LBPH) algorithm and deep neural networks with transfer learning (DNN with TL) and data augmentation (DNN with TL and DA) approaches. Confusion matrix and Receiver Operating Characteristic (ROC) curve were used for evaluating the quality of these models. Results obtained show that the DNN with TL and DA model is the most efficient model to simultaneously differentiate between Tityus and Bothriurus (for health security) and between Tityus trivittatus and Tityus confluence (for biological research purposes).
Fil: Giambelluca, Francisco Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; Argentina
Fil: Cappelletti, Marcelo Ángel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; Argentina. Universidad Nacional Arturo Jauretche; Argentina
Fil: Osio, Jorge. Universidad Nacional Arturo Jauretche; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; Argentina
Fil: Giambelluca, Luis Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Estudios Parasitológicos y de Vectores. Universidad Nacional de La Plata. Facultad de Ciencias Naturales y Museo. Centro de Estudios Parasitológicos y de Vectores; Argentina
Materia
DATA AUGMENTATION
LOCAL BINARY PATTERN
MACHINE LEARNING
SCORPION IMAGE CLASSIFICATION
TRANSFER LEARNING
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/140751
Acceder
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network_acronym_str CONICETDig
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network_name_str CONICET Digital (CONICET)
spelling Novel automatic scorpion-detection and -recognition system based on machine-learning techniquesGiambelluca, Francisco LuisCappelletti, Marcelo ÁngelOsio, JorgeGiambelluca, Luis AlbertoDATA AUGMENTATIONLOCAL BINARY PATTERNMACHINE LEARNINGSCORPION IMAGE CLASSIFICATIONTRANSFER LEARNINGhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2All species of scorpions have the ability to inoculate venom, some of them even with the possibility of killing a human. Therefore, early detection and identification is essential to minimize scorpion stings. In this paper, we propose a novel automatic system for the detection and recognition of scorpions using computer vision and machine learning approaches. Two complementary image processing techniques were used for the proposed detection method in order to accurately and reliably detect the presence of scorpions. The first based on the fluorescence characteristics of scorpions when are exposed to ultraviolet (UV) light, and the second on the shape features of the scorpions. On the other hand, three models based on machine learning algorithms for the image recognition and classification of scorpions have been compared. In particular, the three species of scorpions found in La Plata city (Argentina): Bothriurus bonariensis (of no sanitary importance), and Tityus trivittatus and Tityus confluence (both of sanitary importance), have been researched using the Local Binary Pattern Histogram (LBPH) algorithm and deep neural networks with transfer learning (DNN with TL) and data augmentation (DNN with TL and DA) approaches. Confusion matrix and Receiver Operating Characteristic (ROC) curve were used for evaluating the quality of these models. Results obtained show that the DNN with TL and DA model is the most efficient model to simultaneously differentiate between Tityus and Bothriurus (for health security) and between Tityus trivittatus and Tityus confluence (for biological research purposes).Fil: Giambelluca, Francisco Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; ArgentinaFil: Cappelletti, Marcelo Ángel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; Argentina. Universidad Nacional Arturo Jauretche; ArgentinaFil: Osio, Jorge. Universidad Nacional Arturo Jauretche; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; ArgentinaFil: Giambelluca, Luis Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Estudios Parasitológicos y de Vectores. Universidad Nacional de La Plata. Facultad de Ciencias Naturales y Museo. Centro de Estudios Parasitológicos y de Vectores; ArgentinaIOP Publishing2020-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/140751Giambelluca, Francisco Luis; Cappelletti, Marcelo Ángel; Osio, Jorge; Giambelluca, Luis Alberto; Novel automatic scorpion-detection and -recognition system based on machine-learning techniques; IOP Publishing; Machine Learning: Science and Technology; 2; 12-2020; 1-162632-2153CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://iopscience.iop.org/article/10.1088/2632-2153/abd51dinfo:eu-repo/semantics/altIdentifier/doi/10.1088/2632-2153/abd51dinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T15:01:00Zoai:ri.conicet.gov.ar:11336/140751instacron: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:34982025-10-15 15:01:00.569CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Novel automatic scorpion-detection and -recognition system based on machine-learning techniques
title Novel automatic scorpion-detection and -recognition system based on machine-learning techniques
spellingShingle Novel automatic scorpion-detection and -recognition system based on machine-learning techniques
Giambelluca, Francisco Luis
DATA AUGMENTATION
LOCAL BINARY PATTERN
MACHINE LEARNING
SCORPION IMAGE CLASSIFICATION
TRANSFER LEARNING
title_short Novel automatic scorpion-detection and -recognition system based on machine-learning techniques
title_full Novel automatic scorpion-detection and -recognition system based on machine-learning techniques
title_fullStr Novel automatic scorpion-detection and -recognition system based on machine-learning techniques
title_full_unstemmed Novel automatic scorpion-detection and -recognition system based on machine-learning techniques
title_sort Novel automatic scorpion-detection and -recognition system based on machine-learning techniques
dc.creator.none.fl_str_mv Giambelluca, Francisco Luis
Cappelletti, Marcelo Ángel
Osio, Jorge
Giambelluca, Luis Alberto
author Giambelluca, Francisco Luis
author_facet Giambelluca, Francisco Luis
Cappelletti, Marcelo Ángel
Osio, Jorge
Giambelluca, Luis Alberto
author_role author
author2 Cappelletti, Marcelo Ángel
Osio, Jorge
Giambelluca, Luis Alberto
author2_role author
author
author
dc.subject.none.fl_str_mv DATA AUGMENTATION
LOCAL BINARY PATTERN
MACHINE LEARNING
SCORPION IMAGE CLASSIFICATION
TRANSFER LEARNING
topic DATA AUGMENTATION
LOCAL BINARY PATTERN
MACHINE LEARNING
SCORPION IMAGE CLASSIFICATION
TRANSFER LEARNING
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv All species of scorpions have the ability to inoculate venom, some of them even with the possibility of killing a human. Therefore, early detection and identification is essential to minimize scorpion stings. In this paper, we propose a novel automatic system for the detection and recognition of scorpions using computer vision and machine learning approaches. Two complementary image processing techniques were used for the proposed detection method in order to accurately and reliably detect the presence of scorpions. The first based on the fluorescence characteristics of scorpions when are exposed to ultraviolet (UV) light, and the second on the shape features of the scorpions. On the other hand, three models based on machine learning algorithms for the image recognition and classification of scorpions have been compared. In particular, the three species of scorpions found in La Plata city (Argentina): Bothriurus bonariensis (of no sanitary importance), and Tityus trivittatus and Tityus confluence (both of sanitary importance), have been researched using the Local Binary Pattern Histogram (LBPH) algorithm and deep neural networks with transfer learning (DNN with TL) and data augmentation (DNN with TL and DA) approaches. Confusion matrix and Receiver Operating Characteristic (ROC) curve were used for evaluating the quality of these models. Results obtained show that the DNN with TL and DA model is the most efficient model to simultaneously differentiate between Tityus and Bothriurus (for health security) and between Tityus trivittatus and Tityus confluence (for biological research purposes).
Fil: Giambelluca, Francisco Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; Argentina
Fil: Cappelletti, Marcelo Ángel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; Argentina. Universidad Nacional Arturo Jauretche; Argentina
Fil: Osio, Jorge. Universidad Nacional Arturo Jauretche; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; Argentina
Fil: Giambelluca, Luis Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Estudios Parasitológicos y de Vectores. Universidad Nacional de La Plata. Facultad de Ciencias Naturales y Museo. Centro de Estudios Parasitológicos y de Vectores; Argentina
description All species of scorpions have the ability to inoculate venom, some of them even with the possibility of killing a human. Therefore, early detection and identification is essential to minimize scorpion stings. In this paper, we propose a novel automatic system for the detection and recognition of scorpions using computer vision and machine learning approaches. Two complementary image processing techniques were used for the proposed detection method in order to accurately and reliably detect the presence of scorpions. The first based on the fluorescence characteristics of scorpions when are exposed to ultraviolet (UV) light, and the second on the shape features of the scorpions. On the other hand, three models based on machine learning algorithms for the image recognition and classification of scorpions have been compared. In particular, the three species of scorpions found in La Plata city (Argentina): Bothriurus bonariensis (of no sanitary importance), and Tityus trivittatus and Tityus confluence (both of sanitary importance), have been researched using the Local Binary Pattern Histogram (LBPH) algorithm and deep neural networks with transfer learning (DNN with TL) and data augmentation (DNN with TL and DA) approaches. Confusion matrix and Receiver Operating Characteristic (ROC) curve were used for evaluating the quality of these models. Results obtained show that the DNN with TL and DA model is the most efficient model to simultaneously differentiate between Tityus and Bothriurus (for health security) and between Tityus trivittatus and Tityus confluence (for biological research purposes).
publishDate 2020
dc.date.none.fl_str_mv 2020-12
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/11336/140751
Giambelluca, Francisco Luis; Cappelletti, Marcelo Ángel; Osio, Jorge; Giambelluca, Luis Alberto; Novel automatic scorpion-detection and -recognition system based on machine-learning techniques; IOP Publishing; Machine Learning: Science and Technology; 2; 12-2020; 1-16
2632-2153
CONICET Digital
CONICET
url http://hdl.handle.net/11336/140751
identifier_str_mv Giambelluca, Francisco Luis; Cappelletti, Marcelo Ángel; Osio, Jorge; Giambelluca, Luis Alberto; Novel automatic scorpion-detection and -recognition system based on machine-learning techniques; IOP Publishing; Machine Learning: Science and Technology; 2; 12-2020; 1-16
2632-2153
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://iopscience.iop.org/article/10.1088/2632-2153/abd51d
info:eu-repo/semantics/altIdentifier/doi/10.1088/2632-2153/abd51d
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv IOP Publishing
publisher.none.fl_str_mv IOP Publishing
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.22299

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