Computational approach for Automatic identification of possible foci of the mosquito Aedes aegypti from aerial images acquired by UAVs

• Grant number: 19/05748-0
• Support Opportunities: Regular Research Grants
• Start date: September 01, 2019
• End date: February 28, 2022
• Field of knowledge: Engineering - Electrical Engineering
• Principal Investigator: Sidnei Alves de Araújo
• Grantee: Sidnei Alves de Araújo
• Host Institution: Universidade Nove de Julho (UNINOVE). Campus Vergueiro. São Paulo , SP, Brazil
• Associated researchers: Daniel Trevisan Bravo ; Gustavo Araujo Lima ; Marcia Cristina Zago Novaretti ; Vitor Pessoa Colombo ; Wonder Alexandre Luz Alves

Abstract

The current panorama of diseases caused by the Aedes aegypti mosquito in Brazil and in the world has motivated several research efforts in the most diverse areas of knowledge. In addition to health campaigns for prevention, the technology shows up as a great ally, from the use of unmanned aerial vehicles - UAVs (also known as drones) for the acquisition of aerial images, facilitating the work of health surveillance teams. However, such images are usually analyzed manually (visually) and may require a lot of time from health agents. This project proposes the development of a computational approach that employs computer vision (VC) and artificial intelligence (AI) techniques to automatically identify possible outbreaks of the Aedes aegypti mosquito in urban areas, from aerial images acquired by UAVs. This approach will result in a computer vision system (SVC), which may be used by health workers in inspection activities using UAVs, to generate "maps" and/or reports indicating locations representing potential mosquito breeding sites such as uncovered water containers on slabs and roofs (containing water or not), standing water in gutters or slabs and garbage accumulation. This SVC can bring significant contributions to the public health area, assisting in the planning and execution of the actions of the health surveillance teams in in combating the Aedes aegypti mosquito (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

BRAVO, DANIEL TREVISAN; LIMA, GUSTAVO ARAUJO; LUZ ALVES, WONDER ALEXANDRE; COLOMBO, VITOR PESSOA; DJOGBENOU, LUC; DENSER PAMBOUKIAN, SERGIO VICENTE; QUARESMA, CRISTIANO CAPELLANI; DE ARAUJO, SIDNEI ALVES. Automatic detection of potential mosquito breeding sites from aerial images acquired by unmanned aerial vehicles. COMPUTERS ENVIRONMENT AND URBAN SYSTEMS, v. 90, NOV 2021. (19/05748-0)

GUSTAVO A. LIMA; RAFAEL O. COTRIN; PETERSON A. BELAN; SIDNEI A. DE ARAÚJO. Sistema de Visão Computacional para Identificação Automática de Potenciais Focos do Mosquito Aedes aegypti Usando Drones. RISTI - Revista Ibérica de Sistemas e Tecnologias de Informação, n. 43, p. 93-109, 2021-09-30. (19/05748-0)