A drone system with an object identification algorithm for tracking dengue disease

• Autores: Morán Landa, Diego, Del Rosario Damián, María Fiorela, Portillo Mendoza, Pedro Miguel, Sotomayor Beltran, Carlos Alberto
• Formato: artículo
• Fecha de Publicación: 2022
• Institución: Universidad Tecnológica del Perú
• Repositorio: UTP-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.utp.edu.pe:20.500.12867/6341
• Enlace del recurso: https://hdl.handle.net/20.500.12867/6341; http://10.14569/IJACSA.2022.0131092
• Nivel de acceso: acceso abierto
• Materia: Epidemiological surveillance; Drones; Artificial neural networks; Recognition algorithms; https://purl.org/pe-repo/ocde/ford#1.02.01; https://purl.org/pe-repo/ocde/ford#3.03.00

In recent decades, it has been shown that epidemiological surveillance is one of the most valuable tool that public health has, since it allows us to have an overview of the population general health, thus allowing to anticipate outbreaks of epidemics by helping in timely interventions. Currently there is an increase in cases of dengue disease in several regions of Peru. Therefore, to control this outbreak and to help population centers and human settlements that are far from the city this work puts forward a drone system with an object recognition algorithm. Drones are very efficient in terms of surveillance, allowing easy access to places that are difficult for humans. In this way, drones can carry out the field work that is required in epidemiological surveillance, carrying out photography or video work in real time, and thus identifying infectious foci of diverse diseases. In this work, an object detection algorithm that uses convolutional neural networks and a stable detection model is designed, this allows the detection of water reservoirs that are possible infectious sources of dengue. In addition the efficiency of the algorithm is evaluated through the statistical curves of precision and sensitivity that result of the training of the neural network. To validate the efficiency obtained, the model was applied to test images related to dengue, achieving an efficiency of 99.2%.