Francisco Luis Giambelluca

Francisco Luis Giambelluca
Universidad Nacional de La Plata | UNLP · Departamento de Electrotecnia

PhD Eng. in Electronics

About

9
Publications
1,090
Reads
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6
Citations
Additional affiliations
August 2018 - present
Universidad Nacional de La Plata
Position
  • Teacher Assistant
Education
March 2013 - December 2018
Universidad Nacional de La Plata
Field of study
  • Electronic Engineer

Publications

Publications (9)
Research
Full-text available
Aplicación cuyo fin es ayudar a identificar serpientes de toda la provincia de Buenos Aires, Argentina (link al final). Esta app actúa como una guía de campo, la cual se puede utilizar de manera totalmente offline y no requiere registro para su uso. https://play.google.com/store/apps/details?id=com.Dr_Ing_Francisco_Giambelluca.serpientesbonaerens...
Article
Full-text available
In this paper, automatic and real-time systems were developed to detect and classify two different genera of scorpions using computer vision and deep learning techniques, with the purpose of providing a prevention tool. The images of scorpions were obtained from an arachnology laboratory in Argentina. YOLO (you only look once) and MobileNet models...
Preprint
Full-text available
In this work, a fully automatic and real-time system for the detection of scorpions was developed using computer vision and deep learning techniques. This system is based on the implementation of a double validation process using the shape features and the fluorescent characteristics of scorpions when exposed to ultraviolet (UV) light. The Haar Cas...
Preprint
Full-text available
In this paper, two novel automatic and real-time systems for the detection and classification of two genera of scorpions found in La Plata city (Argentina) were developed using computer vision and deep learning techniques. The object detection technique was implemented with two different methods, YOLO (You Only Look Once) and MobileNet, based on th...
Article
Full-text available
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...

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