<?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>2218-3620</journal-id>
<journal-title><![CDATA[Revista Universidad y Sociedad]]></journal-title>
<abbrev-journal-title><![CDATA[Universidad y Sociedad]]></abbrev-journal-title>
<issn>2218-3620</issn>
<publisher>
<publisher-name><![CDATA[Editorial "Universo Sur"]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2218-36202023000400661</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Comparativa de modelos de detección de objetos y personas en espacios cerrados de acceso público]]></article-title>
<article-title xml:lang="en"><![CDATA[Comparison of detection models for objects and people in closed spaces with public access]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Bográn Ortiz]]></surname>
<given-names><![CDATA[Lester Yonabel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Martínez Hernández]]></surname>
<given-names><![CDATA[Jairo Jonathan]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Nacional Autónoma  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Honduras</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Nacional Autónoma  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Honduras</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>08</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>08</month>
<year>2023</year>
</pub-date>
<volume>15</volume>
<numero>4</numero>
<fpage>661</fpage>
<lpage>672</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S2218-36202023000400661&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S2218-36202023000400661&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S2218-36202023000400661&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN Este trabajo tiene como objetivo presentar una comparativa de diferentes técnicas de detección de objetos y determinar cuál de ellos es el más adecuado para detectar personas en tiempo real y así controlar eficientemente el aforo de personas en espacios públicos cerrados para ayudar a prevenir la propagación del COVID-19. En este estudio se han puesto a prueba soluciones basadas en redes neuronales como R-CNN, YOLO y SSD, así como soluciones no neuronales como SVM y HOG. Se trabajo con modelos entrenados con del conjunto de datos MS COCO, y se utilizó el dataset Wisenet para evaluar los diferentes modelos. Todos los modelos fueron puestos a prueba en tres aspectos diferentes, siendo estos la precesión, velocidad de inferencia y el error. En las pruebas el modelo YOLO demostró un rendimiento mayor a los demás, mientras que la implementación de SSD demostró el rendimiento más bajo en mayoría de las pruebas.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT This work aims to present a comparison of different object detection techniques and determine which of them is the most suitable to detect people in real time and thus efficiently control the capacity of people in closed public spaces to help prevent the spread of COVID-19. In this study, solutions based on neural networks such as R-CNN, YOLO and SSD, as well as non-neural solutions such as SVM and HOG, have been tested. We worked with models trained with the MS COCO dataset, and the Wisenet dataset was used to evaluate the different models. All models were tested in three different aspects, these being precession, speed of inference and error. In tests, the YOLO model demonstrated higher performance than the others, while the SSD implementation demonstrated the lowest performance in most tests.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Detección de objetos]]></kwd>
<kwd lng="es"><![CDATA[Visión por computador]]></kwd>
<kwd lng="es"><![CDATA[Redes neuronales convolucionales]]></kwd>
<kwd lng="es"><![CDATA[Aprendizaje Profundo]]></kwd>
<kwd lng="es"><![CDATA[HOG]]></kwd>
<kwd lng="en"><![CDATA[Object detection]]></kwd>
<kwd lng="en"><![CDATA[Computer visión]]></kwd>
<kwd lng="en"><![CDATA[Convolutional neural networks]]></kwd>
<kwd lng="en"><![CDATA[HOG]]></kwd>
</kwd-group>
</article-meta>
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