<?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>2007-6835</journal-id>
<journal-title><![CDATA[Revista ALCONPAT]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. ALCONPAT]]></abbrev-journal-title>
<issn>2007-6835</issn>
<publisher>
<publisher-name><![CDATA[Asociación Latinoamericana de Control de Calidad, Patología y Recuperación de la Construcción A.C.]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2007-68352025000100005</article-id>
<article-id pub-id-type="doi">10.21041/ra.v15i1.783</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[YOLOv8-based model for automatic detection of residential roof damage]]></article-title>
<article-title xml:lang="es"><![CDATA[Modelo basado en YOLOv8 para la detección automática de daños en tejados residenciales]]></article-title>
<article-title xml:lang="pt"><![CDATA[Modelo baseado em YOLOv8 para detecção automática de danos em telhados residenciais]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Silva]]></surname>
<given-names><![CDATA[A. S.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Azevedo]]></surname>
<given-names><![CDATA[A. R.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Neto]]></surname>
<given-names><![CDATA[F. H. A. M.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ferreira]]></surname>
<given-names><![CDATA[P. H.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Federal de Bahía Departamento de Ingeniería Estructural y de la Construcción ]]></institution>
<addr-line><![CDATA[Salvador ]]></addr-line>
<country>Brazil</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Federal de Bahía Departamento de Matemáticas y Estadística ]]></institution>
<addr-line><![CDATA[Salvador ]]></addr-line>
<country>Brazil</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>04</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>04</month>
<year>2025</year>
</pub-date>
<volume>15</volume>
<numero>1</numero>
<fpage>50</fpage>
<lpage>63</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2007-68352025000100005&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2007-68352025000100005&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2007-68352025000100005&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT: This study developed an automated image recognition model for inspecting residential roofs using the YOLOv8 architecture to identify three types of damage. The methodology involved images from 167 buildings captured by drones and annotated in CVAT, which were used to train and test the model. YOLOv8 was applied for anomaly detection and classification, achieving 79% precision. The limitations were the small dataset and the limited variety of capture angles. The originality of the work lies in the innovative use of YOLOv8 for roof inspection. Future research will focus on developing the YOLOv9 and YOLOv10 architectures and expanding the dataset and damage classes.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN: Este estudio desarrolló un modelo automatizado de reconocimiento de imágenes para la inspección de techos residenciales utilizando la arquitectura YOLOv8, con el objetivo de identificar tres tipos de daños. La metodología incluyó imágenes de 167 edificios, capturadas por drones y anotadas en CVAT, que se utilizaron para entrenar y probar el modelo. YOLOv8 se aplicó para la detección y clasificación de anomalías, logrando un 79% de precisión. Las principales limitaciones fueron el pequeño tamaño del conjunto de datos y la limitada variedad de ángulos de captura. La originalidad del trabajo radica en el uso innovador de YOLOv8 para la inspección de techos. Investigaciones futuras se centrarán en el desarrollo de las arquitecturas YOLOv9 y YOLOv10, además de ampliar el conjunto de datos y las clases de daños.]]></p></abstract>
<abstract abstract-type="short" xml:lang="pt"><p><![CDATA[RESUMO: Este estudo desenvolveu um modelo automatizado de reconhecimento de imagens para inspeção de telhados residenciais, utilizando a arquitetura YOLOv8, com o objetivo de identificar três tipos de danos. A metodologia contou com imagens de 167 edifícios, capturadas por drones e anotadas no CVAT, utilizadas para treinar e testar o modelo. A YOLOv8 foi aplicada na detecção e classificação de anomalias, alcançando 79% de precisão. As principais limitações foram o tamanho reduzido do conjunto de dados e a pouca variedade de ângulos de captura. A originalidade do trabalho está no uso inovador da YOLOv8 para a inspeção de telhados. Pesquisas futuras incluirão o desenvolvimento das arquiteturas YOLOv9 e YOLOv10, bem como a ampliação do conjunto de dados e das classes de problemas.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[maintenance management]]></kwd>
<kwd lng="en"><![CDATA[machine learning]]></kwd>
<kwd lng="en"><![CDATA[You Only Look Once (YOLO)]]></kwd>
<kwd lng="en"><![CDATA[roof inspection]]></kwd>
<kwd lng="en"><![CDATA[rooftop assessment]]></kwd>
<kwd lng="es"><![CDATA[gestión de mantenimiento]]></kwd>
<kwd lng="es"><![CDATA[aprendizaje automático]]></kwd>
<kwd lng="es"><![CDATA[You Only Look Once (YOLO)]]></kwd>
<kwd lng="es"><![CDATA[inspección de techos]]></kwd>
<kwd lng="es"><![CDATA[evaluación de techos]]></kwd>
<kwd lng="pt"><![CDATA[gestão da manutenção]]></kwd>
<kwd lng="pt"><![CDATA[aprendizado de máquina]]></kwd>
<kwd lng="pt"><![CDATA[You Only Look Once (YOLO)]]></kwd>
<kwd lng="pt"><![CDATA[inspeção de telhado]]></kwd>
<kwd lng="pt"><![CDATA[avaliação de telhado]]></kwd>
</kwd-group>
</article-meta>
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