<?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>0012-7353</journal-id>
<journal-title><![CDATA[DYNA]]></journal-title>
<abbrev-journal-title><![CDATA[Dyna rev.fac.nac.minas]]></abbrev-journal-title>
<issn>0012-7353</issn>
<publisher>
<publisher-name><![CDATA[Universidad Nacional de Colombia]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0012-73532019000400317</article-id>
<article-id pub-id-type="doi">10.15446/dyna.v86n211.81639</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[EspiNet V2: a region based deep learning model for detecting motorcycles in urban scenarios]]></article-title>
<article-title xml:lang="es"><![CDATA[EspiNet V2: un modelo basado en regiones de aprendizaje profundo para detectar motocicletas en escenarios urbanos]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Espinosa-Oviedo]]></surname>
<given-names><![CDATA[Jorge Ernesto]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Velastín]]></surname>
<given-names><![CDATA[Sergio A.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Branch-Bedoya]]></surname>
<given-names><![CDATA[John William]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Politécnico Colombiano Jaime Isaza Cadavid Facultad de Ingeniería ]]></institution>
<addr-line><![CDATA[Medellín ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Carlos III de Madrid  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Spain</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad Nacional de Colombia Facultad de Minas ]]></institution>
<addr-line><![CDATA[Medellín ]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2019</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2019</year>
</pub-date>
<volume>86</volume>
<numero>211</numero>
<fpage>317</fpage>
<lpage>326</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0012-73532019000400317&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0012-73532019000400317&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0012-73532019000400317&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract This paper presents &#8220;EspiNet V2&#8221; a Deep Learning model, based on the region-based detector Faster R-CNN. The model is used for the detection of motorcycles in urban environments, where occlusion is likely. For training, two datasets are used: the Urban Motorbike Dataset (UMD-10K) of 10,000 annotated images, and the new SMMD (Secretaría de Movilidad Motorbike Dataset), of 5,000 images captured from the Traffic Control CCTV System in Medellín (Colombia). Results achieved on the UMD-10K dataset reach 88.8% in average precision (AP) even when 60% motorcycles were occluded, and the images were captured from a low angle and a moving camera. Meanwhile, an AP of 79.5% is reached for SSMD. EspiNet V2 outperforms popular models such as YOLO V3 and Faster R-CNN (VGG16 based) trained end-to-end for those datasets.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Este artículo presenta "EspiNet V2", un modelo de aprendizaje profundo, fundamentado en el detector basado regiones Faster R-CNN. El modelo es usado para la detección de motocicletas en entornos urbanos, donde se presenta algún nivel de oclusión. Para el entrenamiento de dicho modelo, se utilizaron dos conjuntos de datos: el conjunto de datos de motocicletas urbanas (UMD-10K) que cuenta con 10,000 imágenes anotadas, y el nuevo conjunto de datos de motos de la Secretaría de Movilidad (SMMD), con 5,000 imágenes capturadas obtenidas del Sistema CCTV de Control de Tráfico de la ciudad de Medellín (Colombia). Los resultados obtenidos en el conjunto de datos UMD-10K alcanzan el 88.8% en precisión promedio (AP), incluso con niveles de oclusión de un 60 %, utilizando imágenes capturadas desde un ángulo bajo y desde una cámara en movimiento. Por otro lado se alcanza un AP de 79.5 % para conjunto de datos de motos de la Secretaría de Movilidad (SMMD). EspiNet V2 supera modelos populares como YOLO V3 y Faster R-CNN (basado en VGG16), siendo estos entrenados de extremo a extremo utilizando los conjuntos de datos mencionados.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[vehicle detection]]></kwd>
<kwd lng="en"><![CDATA[motorcycle detection]]></kwd>
<kwd lng="en"><![CDATA[Faster R-CNN]]></kwd>
<kwd lng="en"><![CDATA[region-based detectors]]></kwd>
<kwd lng="en"><![CDATA[convolutional neural network]]></kwd>
<kwd lng="en"><![CDATA[deep learning]]></kwd>
<kwd lng="es"><![CDATA[detección de vehículos]]></kwd>
<kwd lng="es"><![CDATA[detección de motocicletas]]></kwd>
<kwd lng="es"><![CDATA[Faster R-CNN]]></kwd>
<kwd lng="es"><![CDATA[detectores basados en regiones]]></kwd>
<kwd lng="es"><![CDATA[redes neuronales convolucionales]]></kwd>
<kwd lng="es"><![CDATA[aprendizaje profundo]]></kwd>
</kwd-group>
</article-meta>
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