Método baseado em aprendizagem de máquina para detecção de pequenas porções d’água em imagens multiespectrais adquiridas por drones

• Ano de defesa: 2023
• Autor(a) principal: Cotrin, Rafael Oliveira
• Orientador(a): Araújo, Sidnei Alves de
• Banca de defesa: Araújo, Sidnei Alves de , Pamboukian, Sergio Vicente Denser , Belan, Peterson Adriano
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Nove de Julho
• Programa de Pós-Graduação: Programa de Pós-Graduação em Informática e Gestão do Conhecimento
• Departamento: Informática
• País: Brasil
• Palavras-chave em Português: RPA; drone; mosquito; aedes aegypt; água; aprendizagem de máquina
• Palavras-chave em Inglês: RPA; drone; mosquito; aedes aegypt; water; machine learning
• Área do conhecimento CNPq: CIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAO
• Link de acesso: http://bibliotecatede.uninove.br/handle/tede/3518

RPAS − Remotely Piloted Aircraft System, popularly known as drones, are used to automatically identify objects and scenarios (normally water tanks, buckets, plant pots, and other containers contained in open-air trash) that characterize potential mosquito breeders, such as Aedes aegypt, from the acquired images. However, despite knowing that water stagnation is an essential condition for mosquito breeding, computer vision systems (SVCs) proposed in the literature for automatic image analysis do not include the detection of water in suspicious objects and scenarios, which which constitutes a technical limitation for the effective use of drones in vector monitoring and control actions. In this work, a method is proposed that employs an Artificial Neural Network of the Multilayer Perceptron type (RNA−MLP) to identify small portions of water in multispectral images acquired by drones. To carry out the experiments, a database was composed containing 151 multispectral images of 1280×960 pixels, which are divided into two sets and were acquired from simulated scenarios containing small containers with and without water in a controlled environment. The results obtained by the proposed method (average precision of 0.759) corroborate its potential to increase the technical solutions of existing SVCs, making them more effective in combating mosquito outbreaks, which can bring contributions to the area of public health.