<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1646-9895</journal-id>
<journal-title><![CDATA[RISTI - Revista Ibérica de Sistemas e Tecnologias de Informação]]></journal-title>
<abbrev-journal-title><![CDATA[RISTI]]></abbrev-journal-title>
<issn>1646-9895</issn>
<publisher>
<publisher-name><![CDATA[AISTI - Associação Ibérica de Sistemas e Tecnologias de Informação]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1646-98952021000300093</article-id>
<article-id pub-id-type="doi">10.17013/risti.43.93-109</article-id>
<title-group>
<article-title xml:lang="pt"><![CDATA[Sistema de Visão Computacional para Identificação Automática de Potenciais Focos do Mosquito Aedes aegypti Usando Drones]]></article-title>
<article-title xml:lang="en"><![CDATA[Computer Vision System for Automatic Identification of Potential Aedes aegypti Mosquito Breeding Sites Using Drones]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Lima]]></surname>
<given-names><![CDATA[Gustavo A.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Cotrin]]></surname>
<given-names><![CDATA[Rafael O.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Belan]]></surname>
<given-names><![CDATA[Peterson A.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Araújo]]></surname>
<given-names><![CDATA[Sidnei A. de]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidade Nove de Julho São Paulo ]]></institution>
<addr-line><![CDATA[ SP]]></addr-line>
<country>Brasil</country>
</aff>
<pub-date pub-type="pub">
<day>30</day>
<month>09</month>
<year>2021</year>
</pub-date>
<pub-date pub-type="epub">
<day>30</day>
<month>09</month>
<year>2021</year>
</pub-date>
<numero>43</numero>
<fpage>93</fpage>
<lpage>109</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.pt/scielo.php?script=sci_arttext&amp;pid=S1646-98952021000300093&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.pt/scielo.php?script=sci_abstract&amp;pid=S1646-98952021000300093&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.pt/scielo.php?script=sci_pdf&amp;pid=S1646-98952021000300093&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="pt"><p><![CDATA[Resumo Os drones tem se tornado uma importante ferramenta tecnológica para auxiliar no combate aos focos de mosquitos. No entanto, as imagens adquiridas por eles são usualmente analisadas de forma manual, podendo consumir muito tempo nas atividades de inspeção. Neste trabalho é proposto um sistema de visão computacional (SVC) para identificação e geolocalização automática de potenciais criadouros do mosquito Aedes aegypti a partir de imagens aéreas adquiridas por drones. O SVC desenvolvido deu origem a um software, cujo núcleo é composto por uma rede neural convolucional (RNC) que apresentou taxas de acerto e mAP-50 (mean average precision) de 0,9294 e 0,9362 nos experimentos realizados com uma base composta por 500 imagens. Esses resultados, comparados com resultados recentes da literatura, corroboram a adequação da RNC para compor o SVC, o qual pode trazer melhorias para a utilização de drones em programas de prevenção e combate de fontes de reprodução de mosquitos.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Drones have become an important technological tool to help fight mosquito breeding sites. However, the images acquired by them are usually analyzed manually, which can consume a lot of time in inspection activities. In this work, a computer vision system (SVC) is proposed for the automatic identification and geolocation of potential breeding sites of the Aedes aegypti mosquito from aerial images acquired by drones. The developed SVC gave rise to a software, whose core is composed of a convolutional neural network (CNN) that presented rates of recall and mAP-50 (mean average precision) of 0.9294 and 0.9362 in the experiments conducted with a database composed by 500 images. These results, compared with recent results from the literature, corroborate the adequacy of the CNN to compose the SVC, which can bring improvements to the use of drones in programs of prevention and combating mosquito breeding sources.]]></p></abstract>
<kwd-group>
<kwd lng="pt"><![CDATA[Drone]]></kwd>
<kwd lng="pt"><![CDATA[Mosquito]]></kwd>
<kwd lng="pt"><![CDATA[Reconhecimento de Padrões]]></kwd>
<kwd lng="pt"><![CDATA[Visão Computacional]]></kwd>
<kwd lng="pt"><![CDATA[Redes Neurais Convolucionais]]></kwd>
<kwd lng="en"><![CDATA[Drone]]></kwd>
<kwd lng="en"><![CDATA[Mosquito]]></kwd>
<kwd lng="en"><![CDATA[Pattern Recognition]]></kwd>
<kwd lng="en"><![CDATA[Computer Vision]]></kwd>
<kwd lng="en"><![CDATA[Convolutional Neural Networks]]></kwd>
</kwd-group>
</article-meta>
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