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Detección automática, clasificación y reconocimiento de escorpiones mediante técnicas de aprendizaje profundo

Authors: Giambelluca, Francisco Luis; Cappelletti, Marcelo Angel;

Detección automática, clasificación y reconocimiento de escorpiones mediante técnicas de aprendizaje profundo

Abstract

Los escorpiones tienen la capacidad de inocular veneno, que utilizan para matar a sus presas y ayudar a la pre digestión, es por ello que se los conoce como un grupo peligroso, si bien son pocas las especies en el mundo que pueden causar un accidente de gravedad al ser humano. La picadura se suele dar accidentalmente, debido a que estos arácnidos pican si son provocados o atacados, es decir, las personas son picadas por escorpiones cuando tienen un contacto involuntario con estos. Por lo tanto, la detección e identificación temprana es esencial para minimizar las picaduras de escorpión. Los escorpiones son animales de actividad nocturna y tienen una característica única que consiste en emitir fluorescencia cian al ser iluminados con luz ultravioleta (UV). En mi tesis doctoral, proponemos un novedoso sistema automático para la detección y reconocimiento de escorpiones utilizando enfoques de visión por computadora y aprendizaje automático.

Universidad Nacional de La Plata

Country
Argentina
Related Organizations
Keywords

Aprendizaje Profundo, Ingeniería, Fluorescencia, Detección de Escorpiones

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green