Novel automatic scorpion-detection and -recognition system based on machine-learning techniques
All species of scorpions can inject venom, some of them even with the possibility of killing a human. Therefore, early detection and identification are essential to minimize scorpion stings. In this paper, we propose a novel automatic system for the detection and recognition of scorpions using compu...
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Formato: | Articulo |
Lenguaje: | Inglés |
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2021
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Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/125115 |
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I19-R120-10915-125115 |
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Universidad Nacional de La Plata |
institution_str |
I-19 |
repository_str |
R-120 |
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SEDICI (UNLP) |
language |
Inglés |
topic |
Ingeniería data augmentation local binary pattern Machine learning scorpion image classification Transfer learning |
spellingShingle |
Ingeniería data augmentation local binary pattern Machine learning scorpion image classification Transfer learning Giambelluca, Francisco Luis Cappelletti, Marcelo Angel Osio, Jorge Rafael Giambelluca, Luis Alberto Novel automatic scorpion-detection and -recognition system based on machine-learning techniques |
topic_facet |
Ingeniería data augmentation local binary pattern Machine learning scorpion image classification Transfer learning |
description |
All species of scorpions can inject venom, some of them even with the possibility of killing a human. Therefore, early detection and identification are 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 (ML) approaches. Two complementary image-processing techniques were used for the proposed detection method to accurately and reliably detect the presence of scorpions. The first is based on the fluorescent characteristics of scorpions when exposed to ultraviolet light, and the second on the shape features of the scorpions. Also, three models based on ML algorithms for the image recognition and classification of scorpions are compared. In particular, the three species of scorpions found in La Plata city (Argentina): <i>Bothriurus bonariensis</i> (of no sanitary importance), <i>Tityus trivittatus</i>, and <i>Tityus confluence</i> (both of sanitary importance) have been researched using a local binary-pattern histogram algorithm and deep neural networks with transfer learning (DNNs with TL) and data augmentation (DNNs with TL and DA) approaches. A confusion matrix and a receiver operating characteristic curve were used to evaluate the quality of these models. The results obtained show that the model of DNN with TL and DA is the most efficient at simultaneously differentiating between <i>Tityus</i> and <i>Bothriurus</i> (for health security) and between <i>T. trivittatus</i> and <i>T. confluence</i> (for biological research purposes). |
format |
Articulo Articulo |
author |
Giambelluca, Francisco Luis Cappelletti, Marcelo Angel Osio, Jorge Rafael Giambelluca, Luis Alberto |
author_facet |
Giambelluca, Francisco Luis Cappelletti, Marcelo Angel Osio, Jorge Rafael Giambelluca, Luis Alberto |
author_sort |
Giambelluca, Francisco Luis |
title |
Novel automatic scorpion-detection and -recognition system based on machine-learning techniques |
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 |
publishDate |
2021 |
url |
http://sedici.unlp.edu.ar/handle/10915/125115 |
work_keys_str_mv |
AT giambellucafranciscoluis novelautomaticscorpiondetectionandrecognitionsystembasedonmachinelearningtechniques AT cappellettimarceloangel novelautomaticscorpiondetectionandrecognitionsystembasedonmachinelearningtechniques AT osiojorgerafael novelautomaticscorpiondetectionandrecognitionsystembasedonmachinelearningtechniques AT giambellucaluisalberto novelautomaticscorpiondetectionandrecognitionsystembasedonmachinelearningtechniques |
bdutipo_str |
Repositorios |
_version_ |
1764820450059223040 |