| dc.creator | Giambelluca, Francisco Luis | |
| dc.creator | Cappelletti, Marcelo Angel | |
| dc.creator | Osio, Jorge Rafael | |
| dc.creator | Giambelluca, Luis Alberto | |
| dc.date | 2021-02-26 | |
| dc.date | 2021-09-17T17:50:02Z | |
| dc.date.accessioned | 2023-07-15T03:05:40Z | |
| dc.date.available | 2023-07-15T03:05:40Z | |
| dc.identifier | http://sedici.unlp.edu.ar/handle/10915/125115 | |
| dc.identifier | issn:2632-2153 | |
| dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/7464416 | |
| dc.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). | |
| dc.description | Facultad de Ingeniería | |
| dc.description | Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales | |
| dc.description | Centro de Estudios Parasitológicos y de Vectores | |
| dc.format | application/pdf | |
| dc.language | en | |
| dc.rights | http://creativecommons.org/licenses/by/4.0/ | |
| dc.rights | Creative Commons Attribution 4.0 International (CC BY 4.0) | |
| dc.subject | Ingeniería | |
| dc.subject | data augmentation | |
| dc.subject | local binary pattern | |
| dc.subject | Machine learning | |
| dc.subject | scorpion image classification | |
| dc.subject | Transfer learning | |
| dc.title | Novel automatic scorpion-detection and -recognition system based on machine-learning techniques | |
| dc.type | Articulo | |
| dc.type | Articulo | |
