<?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>S1646-98952025000200067</article-id>
<article-id pub-id-type="doi">10.17013/risti.58.67-80</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Sistema de Visión Artificial para detección de fallas en tela terminada]]></article-title>
<article-title xml:lang="en"><![CDATA[Artificial Vision System for Failure Detection In Finished Fabric]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Morales]]></surname>
<given-names><![CDATA[Jorge]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Morales]]></surname>
<given-names><![CDATA[Juan]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Instituto Tecnológico Universitario ISMAC  ]]></institution>
<addr-line><![CDATA[Quito ]]></addr-line>
<country>Ecuador</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Politécnica de Valencia  ]]></institution>
<addr-line><![CDATA[Valencia ]]></addr-line>
<country>Spain</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2025</year>
</pub-date>
<numero>58</numero>
<fpage>67</fpage>
<lpage>80</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.pt/scielo.php?script=sci_arttext&amp;pid=S1646-98952025000200067&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.pt/scielo.php?script=sci_abstract&amp;pid=S1646-98952025000200067&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.pt/scielo.php?script=sci_pdf&amp;pid=S1646-98952025000200067&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen La detección de fallas en tela terminada es llevada a cabo mediante la inspección visual que una persona realiza para determinar la afectación directa de diferentes defectos en parte del tejido y así determinar su calidad, tomando como referencia la norma técnica internacional ASTM D 5430-07. Este estudio propone un sistema de Visión Artificial para la detección de las tres principales fallas que se pueden producir en los tejidos como son: hilo sucio, caída de tejido y manchas, mediante cámaras, que en tiempo real encuentran las diferentes fallas mediante algoritmos de detección empleando el programa Python con librerías de cv2. Una vez detectada la falla en la tela, el sistema muestra dónde se encuentra la anomalía mediante la visualización en una pantalla. Para la validación de la detección de las fallas, se realiza la comparación con los métodos: LBP, HAAR y HOG, dando como mejor resultado para la detección de anomalías el LBP.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Fault detection in finished fabric is carried out through visual inspection by a person to determine the direct impact of various defects on parts of the fabric and thus determine its quality, referencing the international technical standard ASTM D 5430-07. This study proposes an Artificial Vision system for detecting the three main faults that can occur in fabrics: dirty thread, fabric drop, and stains. Using cameras, the system detects these faults in real-time through detection algorithms employing the Python program with cv2 libraries. Once a fault in the fabric is detected, the system displays the location of the anomaly on a screen. To validate the detection of faults, the comparison is made with the methods: LBP, HAAR and HOG, giving the best result for the detection of anomalies the LBP.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Visión Artificial]]></kwd>
<kwd lng="es"><![CDATA[Tejido]]></kwd>
<kwd lng="es"><![CDATA[LBP]]></kwd>
<kwd lng="es"><![CDATA[HAAR]]></kwd>
<kwd lng="es"><![CDATA[HOG]]></kwd>
<kwd lng="en"><![CDATA[Artificial Vision]]></kwd>
<kwd lng="en"><![CDATA[Fabric]]></kwd>
<kwd lng="en"><![CDATA[LBP]]></kwd>
<kwd lng="en"><![CDATA[HAAR]]></kwd>
<kwd lng="en"><![CDATA[HOG]]></kwd>
</kwd-group>
</article-meta>
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