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Revisión sistemática de las aplicaciones de vanguardia en el campo de la visión por computadora =

Systematic review of state-of-the-art applications in the field of computer vision

 


= 1

Paulo César Torres Abril <= /p>

https://orcid.org/0000-0002-4055-883= X

 

Magister en Gerencia de Sistemas de Información, Universidad Técnica de Ambato, Ambato, Ecuador.

pc.torres@= urta.edu.ec

= 2

Santiago David Jara Moya

https://orcid.org/0000-0002-4360-600= 8

 

Máster en Investigación e Innovación en Tecnologías de la Información y las Comunicaciones, Universidad Técnica de Ambato, Ambat= o, Ecuador.

sd.jara@ut= a.edu.ec

= 3

Leonardo David Torres Valverde

https://orcid.org/0000-0002-1996-324= 0

 

Máster Universitario en Investigación e Innovación en Tecnologías de la Información y las Comunicaciones, Universidad Técnica de Ambato, Ambato, Ecuador.

ld.torres@= uta.edu.ec

= 4

Darwin René Arias Martínez<= /p>

https://orcid.org/0000-00c03-4306-10= 33

 

Magister en Gerencia de Sistemas de Información, Instituto Tecnológico Universitario Vida Nueva, Quito, Ecuador

sistemas@i= stvidanueva.edu.ec

 

 

Artículo de Investigación Científica y Tecnológica

Enviado: 14/07/2023

Revisado: 09/08/2023

Aceptado: 11/09/2023

Publicado:19/10/2023

DOI: http= s://doi.org/10.33262/cienciadigital.v7i4.2710               =

 

 

&nb= sp;

 

 

Cítese:

 

 

Torres Abril = , P. C., Jara Moya, S. D., Torres Valverde, L. D., & Arias Martínez, D. R. (2023). Revisión sistemática de las aplicaciones de vanguardia en el camp= o de la visión por computadora . Ciencia Digital, 7(4), 26-53. https://doi.or= g/10.33262/cienciadigital.v7i4.2710

 

 

 

 

CIENCIA DIGITAL, es una revista multidisciplinaria, trimestral,= que se publicará en soporte electrónico tiene como misión contribuir a la   formación de profesionales competentes con visión humaníst= ica y crítica que sean capaces de exponer sus resultados investigativos y científicos en la misma medida que se promueva mediante su intervención cambios positivos en la sociedad. https://cienciadigital.org<= /a>

La revista es editada por la Editorial Ciencia Digital (Editorial de prestigio registra= da en la Cámara Ecuatoriana de Libro con No de Afiliación 663) www.celibro.org.ec

 

 

 

 

Esta revista está protegida bajo una licencia Creative Commons Atribución-NoComercial-CompartirIgual 4.0 International. Copia de la licencia: https://creativ= ecommons.org/licenses/by-nc-sa/4.0/deed.es

 

Palabras clav= es:

Algoritmos, Artificial, Deep Learning, Metodología SLR, Procesamiento de imágenes, Visión.

 

Resumen

Introducción:  La visión artificial combina inteligencia artificial y robótica pa= ra analizar imágenes capturadas por cámaras. Se basa en la teoría de la percepción del color RGB y considera factores como la iluminación y el ti= po de sensor. Utiliza modelos de color para modificar imágenes con precisión= . Se emplean OpenCV y Python en esta investigación sobre técnicas avanzadas en visión artificial, centrándose en la innovación y algoritmos para mejorar= la precisión en la clasificación de objetos mediante el aprendizaje automáti= co y redes neuronales. Objetivo:  <= /b>El objetivo principal de este estudio es llevar a cabo un examen exhaustivo = de la información disponible acerca de los avances recientes en visión artificial mediante metaanálisis o revisión sistemática, con el fin de abordar de manera más precisa la investigación en este ámbito. Metodol= ogía:  La investigación se enfoca en la = visión artificial, priorizando fuentes científicas recientes en inglés, aunque se incluyen libros y fuentes web confiables en menor medida. Se utiliza un enfoque cualitativo a través de la metodología de Revisión Sistemática de= la Literatura (SLR), que abarca la formulación de preguntas, exploración de documentos, selección rigurosa de obras y adquisición de datos relevantes= . Resultados: El análisis destaca que la visión por computadora es un campo avanzado con diversas aplicaciones en sistemas de dispositivos inteligentes; también se realizó un análisis de palabras clave para identificar tendencias clave en los artículos seleccionados. Conclusión: La mayoría de los estudios relevantes sobre el tema se hallaron en bases de datos en inglés como IEE= E y Springer, con limitadas referencias en Scopus debido a sus costos asociad= os; el enfoque de este estudio se centra en sistemas inteligentes y su aplica= ción en la detección de objetos en tiempo real mediante redes neuronales convolucionales. Área de estudio general: Tecnologías de la Información y Comunicación (Tic). Área de estudio específica: Inte= ligencia artificial.

 

 

Keywords:

Algorithms, Artificial, Deep Learning, = SLR Methodology, Image Processing, Vision.

 

Ab= stract

Introduction:=   Computer vision combines artificial intelligence and robotics to analyze images captured by cameras. It is based on the theory of RGB color perception and considers factors such as illumination and sensor type. It uses color models to accurately modify images. OpenCV and Python are used= in this research on advanced techniques in computer vision, focusing on innovation and algorithms to improve object classification accuracy using machine learning and neural networks. Objective:  The main objective of this study = is to conduct a comprehensive review of the available information on recent advances in machine vision by means of meta-analysis or systematic review= , to address research more accurately in this field. Methodology:  The research focuses on computer vision, prioritizing recent scientific sources in English, although relia= ble books and web sources are included to a lesser extent. A qualitative appr= oach is used through the Systematic Literature Review (SLR) methodology, which encompasses the formulation of questions, exploration of documents, rigor= ous selection of works and acquisition of relevant data. Results:  The analysis highlights that computer = vision is an advanced field with diverse applications in intelligent device syst= ems; a keyword analysis was also performed to identify key trends in the selec= ted articles. Conclusion:  Mos= t of the relevant studies on the subject were found in English databases such = as IEEE and Springer, with limited references in Scopus due to their associa= ted costs; the focus of this study is on intelligent systems and their application in real-time object detection using convolutional neural networks.

 

<= o:p> 

<= o:p> 

Introducción

La visión artificial, también conocida como visión por computadora, se encuent= ra en la intersección de diversos campos, incluyendo la inteligencia artificia= l y la robótica (Lee et al., 2023). Esta disciplina integra y combina principio= s y conceptos de programación, informática, mecatrónica, álgebra lineal, estadísticas y probabilidad, entre otros, con el propósito de analizar, procesar y manipular imágenes capturadas por sensores ópticos, comúnmente c= onocidos como cámaras, que buscan replicar la capacidad visual humana. Esto posibili= ta que los sistemas de visión puedan identificar y comprender las característi= cas del entorno que están observando.

El procesamiento de imágenes se sustenta principalmente en teorías de la percepción del color, como la tricromía RGB. Esto se debe a que una imagen = se compone en realidad de una matriz de colores con tres capas: Rojo, Verde y = Azul (RGB). Cada píxel de la imagen puede ser interpretado como una combinación = de estos tres colores, y el cerebro humano, al procesar esta información, gene= ra la percepción de color que observamos (Shubham et al., 2022).

Para lograr una correcta interpretación de las características de una imagen, es esencial considerar variables físicas como la distancia focal y, por supues= to, las condiciones de iluminación, el entorno y, especialmente, el tipo de sen= sor utilizado. Esto se debe a que la composición matricial de las imágenes puede ser influenciada por factores naturales o físicos, como la reflexión de la = luz o el deslumbramiento, que tienden a causar distorsiones hacia tonos más cla= ros (Khaliluzzaman et al., 2018).

Asimismo, la temperatura y la humedad ambiental son condiciones para tener en cuenta,= ya que las condiciones extremas, como las olas de calor, pueden afectar la cal= idad de la imagen. En este contexto, el tratamiento óptico de las imágenes se ba= sa en la explotación consciente de esta teoría matricial o modelo RGB (Bhattacharya & Chatterjee, 2017).

Un modelo de color se utiliza con el propósito de intencionadamente alterar las características de una imagen, permitiendo la aplicación de diversas modificaciones como desenfoque, binarización, aplicación de texturas, así c= omo técnicas tales como el uso de filtros gaussianos, segmentación de color, y ajustes de profundidad, entre otros. De esta manera, se logra modificar la imagen de manera precisa y efectiva (Mostafi et al., 2022).

Este trabajo se basa en la información proporcionada por artículos científicos r= elacionados con el procesamiento de imágenes mediante visión artificial utilizando las herramientas OpenCV (Kulkarni et al., 2020; Sriratana et al., 2018). En consecuencia, se abordan las técnicas más destacadas, incluyendo los filtro= s de color basados en estadísticas, así como las aplicaciones más avanzadas relacionadas con el procesamiento de imágenes (Ho et al., 2022).=

Este estudio tiene como objetivo examinar la información disponible sobre los recientes avances en visión artificial. Para lograr esto, se optó por utili= zar un enfoque distinto en lugar de las revisiones bibliográficas subjetivas, q= ue a veces se denominan narrativas (Moreno et al., 2023). En su lugar, se empleó= un metaanálisis o revisión sistemática, que se considera una metodología más o= bjetiva y rigurosa para llevar a cabo la revisión de la investigación en este campo (Sánchez et al., 2010).

De acuerdo con la información examinada, la comunidad de investigadores se enf= oca en la innovación y la creación de aplicaciones que demandan una respuesta instantánea. En este contexto, resulta esencial el desarrollo de algoritmos diseñados para optimizar el procesamiento de imágenes, al mismo tiempo que perfeccionan la eficiencia y precisión en procesos considerados inteligente= s, como la clasificación e identificación de objetos. Esto se logra mediante la aplicación de técnicas como el aprendizaje automático, redes neuronales y, = por supuesto, la convolución.

Metodología

Dado que el tema de la visión artificial y sus aplicaciones se encuentra en la vanguardia de la investigación, se ha dado prioridad a los artículos científicos publicados en los últimos siete años (a partir de 2015) como fuentes principales de información, tanto en revistas como en conferencias, preferiblemente en inglés. Sin embargo, también se ha hecho referencia a li= bros y fuentes web confiables y actualizadas, aunque en menor cantidad. Este tra= bajo se basa en un enfoque cualitativo, ya que se utilizará la metodología de Revisión Sistemática de la Literatura (SLR), que comprende las siguientes etapas:

A.    Preguntas de investigación.=

B.     Exploración de documentos.<= /span>

C.     Selección rigurosa de obras.

= D.    Adquisición de datos significativos y contribuciones pertinentes.

A. Preguntas de investigación (RQ)

Basándonos en el enfoque de partida (Ho et al., 2022), se sugiere dirigir de manera estructurada la indagación hacia cuatro interrogantes que se detallan a continuación:

·         RQ1: ¿Cuáles son las técnicas de procesamiento de imágenes más avanzadas en la actualidad?=

·         RQ1-Objetivo: Identificar las técnicas= que tienen un mayor nivel de adopción en la actualidad.

·         RQ2: ¿Cuáles son las aplicaciones más destacadas en la actualidad?

·         RQ2-Objetivo: Resumir de manera concisa las áreas de aplicación más vanguardistas de la visión por computadora en l= os últimos años.

·         RQ3: ¿Cuáles son los ejemplos de aplicaciones que están influyendo en la investigación en el ámbito del procesamiento de imágenes?

·         RQ3-Objetivo: Evaluar cómo la visión artificial está aportando innovación a diversas aplicaciones.

·         RQ4: ¿Cuál es el panorama futuro de la visión artificial dentro de los Sistemas Inteligentes?

·      =    RQ4-Objetivo: Reconocer las áreas potenciales para la creación de sistemas inteligentes que aprovechen la vis= ión artificial.

B. Exploración de documentos

Para obtener información de vanguardia sobre las aplicaciones del procesamiento = de imágenes, hemos realizado una exhaustiva búsqueda y recopilación de datos procedentes de fuentes ampliamente respetadas en la comunidad de investigac= ión. Nuestro enfoque se basa en rigurosas prácticas científicas para garantizar = la calidad de la información recabada.

A continuación, se presentan las bases de datos científicas utilizadas para recopilar información en el estudio de los artículos que serán revisados en bases de datos en línea, tal como se detalla en la tabla 1.

Tabla 1

Base de Datos científicas en línea

Base de Datos

URLs

IEEE Xplorer

http://ieeexplore.ieee.org/

Scopus

https://www.scopus.com

Google scholar

https://scholar.google.es/

Springer

http://link.springer.com/

 

Figura 1

Exploración inicial de artículos

<= /o:p>

Según la ilustración 1 y tomando en consideración las bases de datos utilizadas, se obtuvieron los siguientes resultados: IEEE Xplore (50), ScienceDirect (20), Scopus (5), Google Académico (60) y Springer (10).

Realizando una búsqueda inicial de información, se evidencia un claro interés en la comuni= dad científica por el campo de la visión por computadora. En este sentido, se considera apropiado llevar a cabo un análisis exhaustivo de la literatura y documentación relevante, abarcando no solo textos en inglés, sino también en español. Sin embargo, es importante destacar que este estudio se centrará exclusivamente en artículos escritos en inglés.

C. Selección rigurosa de obras

El inicio de e= sta fase destaca la amplia gama de documentos y artículos disponibles para el investigador. Sin embargo, es esencial realizar un proceso de filtrado de la información y centrarse en la selección, teniendo en cuenta ciertos aspecto= s o criterios (Ho et al., 2022).

Tabla 2

Resultados de la búsqueda de información sobre criterios = de selección (SLR)

Fuente

CS1

CS2

CS3

IEEE Xplorer

50

40

18

Science Direct

10

5

0

Scopus

15

7

1

Google scholar

60

20

4

Springer

10

10

9

Total

145

82

32

 

La tabla 2 presenta los resultados de la búsqueda de información en varias bas= es de datos seleccionadas. Se identificaron un total de 145 papers que cumplen= con el Criterio de selección 1 “CS1”,= 82 papers que cumplen con el criterio de selección 2 “CS2” y 32 papers que cumplen con el criterio de selección 3 “CS3” en relación con el tema de estudio. L= os criterios usados fueron los siguientes:

·         C1: Criterios de Selección (CS1). - Se consideró la actualidad en función del período de publicación o indexación = del artículo científico o conferencia, limitándose a los años 2015-2022.

·         C2: Criterios de Selección (CS2). - Se aplicó un filtro de idioma, priorizando los artículos escritos en inglés.

·         C3: Criterios de Selección (CS3). - La selección se basó en la relevancia con respecto al tema. Siguiendo los criterios establecidos, se generó la tabla 3 de resultados:

En resumen, el enfoque que se usó para la selección de Artículos fue:

·         CS1: Actualidad (2015-2020).

·         CS2: Idioma (inglés).

·         CS3: Relevancia Temática.

Tabla 3

Resultados finales de la selecció= n de artículos

Fuente

IEEE Xplorer<= /span>

Scopus

Google scholar

Springer

Total

En la tabla 3 que se muestra previamente, al analizar la transición a la que se sometieron los documentos, se observa que la selección de material para esta revisión de investigación consta de 32 artículos, la mayoría de los cuales provienen de fuentes como IEEE y Springer. Además, a partir del gráfico ant= erior, se destaca la diversidad en el proceso de búsqueda de información, ya que, según el segundo criterio de filtrado, se aprecia que el investigador cuenta con una proporción casi equivalente de fuentes tanto en inglés como en espa= ñol.

D. Adquisición de datos significativos y contribuciones pertinentes=

Para llevar a cabo la aplicación de los criterios de selección, se dispone de un equipo compuesto por tres profesionales. Este equipo realiza un proceso de filtrado de la información, y como resultado de esta fase de revisión y análisis, se identificaron un total de 32 trabajos que serán sometidos a un análisis detallado en relación con las preguntas de investigación planteada= s. Estos análisis contribuirán a la formulación de las conclusiones del estudi= o. A continuación, se detallan las características más destacadas de cada una de= las fuentes utilizadas en esta revisión.

= ·&nb= sp;        Código: A1 (Berjon et al., 2020)

Título: FVV Live: Real-Time, Low-Cost, Free Viewpoint Video

Base de Datos: IEEE Xplorer

Año: 2020

Autores: Daniel Berjón; Pablo Carballeira; Julián Cabrera; Carlos Carmona; Daniel Corregidor; César Díaz<= o:p>

Objetivo: Se introduce un sist= ema de Flujo de Video Visual (FVV) en tiempo real de bajo costo que coordina múltiples nodos para las etapas de adquisición, transmisión, síntesis y presentación, con la capacidad de generar un modelo detallado de la profund= idad del fondo durante la calibración.

= ·&nb= sp;        Código: A2 (Swain, Dhariwal, & Kumar, 2018)<= /span>

Título: A Python (Open CV) based automatic to= ol for parasitemia calculation in peripheral blood smear.

Base de Datos: IEEE Xplo= rer

Año: 2018

Autores: Mahendra Swain; Sandeep Dhariwal; Gau= rav Kumar

Objetivo: Crear un proceso utilizando Python (OpenCV) para simplificar el procesamiento de imágenes, calcular el tamaño de las células, realizar transformaciones morfológicas y determinar la parasitemia.

 

= ·&nb= sp;        Código: A3 (Mohanasundaram et al., 2019)

Título: Vehicle Theft Tracking, Detecting And Locking System Using Open CV

Using Open CV

Base de Datos: IEEE Xplorer

Año: 2018

Autores: S. Mohanasundaram; V. Krishnan; V. Madhubala

Objetivo: Implementar un siste= ma innovador que permita la apertura de vehículos a través del reconocimiento facial, aprovechando la potencia y versatilidad de OpenCV en su desarrollo. Este enfoque promete ofrecer una mayor comodidad y seguridad en el acceso a= los vehículos.

= ·&nb= sp;        Código: A4 (Jain et al., 2018)

Título: Visual Assistance for Blind Using Ima= ge Processing

Processing

Base de Datos: IEEE Xplorer

Año: 2018

Autor: B Deepthi Jain; Shwetha M Thakur; K V Suresh

Objetivo: Este artículo presen= ta una propuesta de sistema destinado a asistir a individuos con discapacidad visual. El propósito fundamental de este sistema es desarrollar una herrami= enta visual portátil que sea capaz de responder a comandos de voz emitidos por el usuario.

= ·&nb= sp;        Código: A5 (Pavithra & S= uresh, 2019)

Título: Fingerprint Image Identification for Crime Detection

Base de Datos: IEEE Xplorer

Año: 2019

Autor: Pavithra R.; K.V. Suresh

Objetivo: Se busca desarrollar= un sistema de aprendizaje automático profundo (CNN) para identificar huellas dactilares en escenas del crimen, incluso en imágenes difíciles. El objetiv= o es lograr una alta precisión (alrededor del 80%) en la identificación de huell= as, beneficiando la resolución de casos en serie en una base de datos criminal.=

= ·&nb= sp;        Código: A6 (Chandan et al., 2021)

Título: Real Time Object Detection and Tracki= ng Using Deep Learning and OpenCV.

Base de Datos: IEEE Xplorer

Año: 2018

Autor: Chandan G, Ayush Jain, Harsh Jain, Mo= hana

Objetivo: Implementar un algor= itmo de detección de objetos que combine características de varios enfoques de aprendizaje profundo, priorizando la eficiencia en la detección y seguimien= to sin comprometer la precisión, especialmente útil en situaciones donde se requiere velocidad.

= ·&nb= sp;        Código: A7 (Guo et al., 2019)

Título: Geosr: A Computer Vision Package for = Deep Learning Based Single-Frame Remote Sensing Imagery Super-Resolution

Base de Datos: IEEE Xplorer

Año: 2019

Autores: Zhiling Guo, Guangming W= u, Xiaodan Shi, Mingzhou Sui, Xiaoya Song, Yongwei Xu, Xiaowei Shao, Ryosuke Shibasaki.

Objetivo: Introducir GeoSR es un paquete de visión por computadora de código abierto que utiliza técnicas de aprendizaje profundo para mejorar la resolución de imágenes de teledetecció= n. Ofrece herramientas y modelos preentrenados para simplificar el desarrollo y evaluación de métodos de superresolución, lo que puede beneficiar a otras á= reas de procesamiento de imágenes.

= ·&nb= sp;        Código: A8 (Sasaki et al., 2017)

Título: A study on vision-based mobile robot learning by deep Q-network.

Base de Datos: IEEE Xplorer

Año: 2017

Autores: Hikaru Sasaki, Tadashi Horiuchi and Satoru Kato.

Objetivo: Se busca guiar a un robot móvil con comportamientos adecuados utilizando información visual compleja. El método aprovecha éxitos previos en situaciones de bajo rendimi= ento y acelera el aprendizaje mediante la técnica "Profit Sharing" en = DQN (D= eep Q-Network).

= ·&nb= sp;        Código: A9 (Bellemo et al., 2019)

Título: Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopath= y in Africa: a clinical validation study.

Base de Datos: IEEE Xplorer

Año: 2019

Autores: Valentina Bellemo, Zhan W Lim, Gilbert Lim, Quang D Nguyen, Yuchen Xie, Michelle Y T Yip, Haslina Ham= zah, Jinyi Ho, Xin Q Lee, Wynne Hsu, Mong L Lee, Lillian Musonda, Manju Chandran, Grace Chipalo-Mutati, Mulenga Muma, Gavin S W Tan, Sobha Sivaprasad, Geeta Menon, Tien Y Wong, Daniel S W Ting.

Objetivo: Examinar la exactitu= d de un modelo de inteligencia artificial (IA) basado en aprendizaje profundo en= un sistema de detección de retinopatía diabética dentro de un contexto poblaci= onal en Zambia, una nación con ingresos en el rango de media-baja.

= ·&nb= sp;        Código: A10 (Yudin et al., 2019)

Título: Detection of Big Animals on Images wi= th Road Scenes using Deep Learning

Base de Datos: IEEE Xplorer

Año: 2019

Autores: Dmitry Yudin Anton Sotnikov Andrey Krishtopik

Objetivo: Se desarrolla un software utilizando Keras, PyTorch y librerías de NVidia con CUDA para identificar animales grandes en imágenes, este enfoque eficaz tiene posibles aplicaciones en sistemas de visión para vehículos autónomos y asistencia al conductor.

= ·&nb= sp;        Código: A11 (Kusuma et al., 2019)

Título: Driver Distraction Detection using De= ep Learning and Computer Vision.

Base de Datos: IEEE Xplorer

Año: 2019

Autores: Kusuma.S, Divya Udayan.J, Aashay Sachdeva.

Objetivo: Fue creado un sistema que emplea aprendizaje profundo y visión por computadora para identificar la somnolencia del conductor. Se implementó un avanzado modelo que estima la posición de la cara y los ojos con el propósito de mejorar la precisión de = la detección y minimizar los errores de detección falsos tanto positivos como negativos.

= ·&nb= sp;        Código: A12 (Deep & Zheng, 2019)

Título: Leveraging CNN and Transfer Learning = for Vision-based Human Activity Recognition

Base de Datos: IEEE Xplorer

Año: 2019

Autores: Samundra Deep, Xi Zheng

Objetivo: Aplicar un modelo de aprendizaje profundo (CNN) para predecir actividades humanas utilizando el conjunto de datos Wiezmann. Los resultados demuestran una alta precisión del 96,95% con el modelo VGG-16, lo que sugiere su utilidad en aplicaciones de reconocimiento de actividad humana.

 

= ·&nb= sp;        Código: A13 (Akbar et al., 2019)

Título: Runway Detection and Localization in Aerial Images using Deep Learning

Base de Datos: IEEE Xplorer

Año: 2019

Autores: Javeria Akbar, Muhammad Shahzad, Muhammad Imran Malik, Adnan Ul-Hasan, Fasial Shafait.

Objetivo: Mejorar el aterrizaje automático de plataformas aéreas, como drones, mediante la detección y localización precisa de pistas de aterrizaje en imágenes aéreas complejas. = Se emplea un enfoque innovador que combina arquitecturas de aprendizaje profun= do y métodos tradicionales de procesamiento de imágenes.

= ·&nb= sp;        Código: A14 (Nassif et al., 2019)

Título: Speech Recognition Using Deep Neural Networks: A Systematic Review

Base de Datos: IEEE Xplorer

Año: 2019

Autores: Ali Bou Nassif, Ismail Shahin, Imtinan Attili, Mohammad Azzeh, Khaled Shaalan.

Objetivo: Revisar y analizar exhaustivamente los avances en el uso del aprendizaje profundo en aplicacio= nes de procesamiento del habla desde 2006 hasta 2018. Se examinan 174 artículos para identificar tendencias de investigación y destacar posibles áreas de interés futuro en este campo en constante evolución.

= ·&nb= sp;        Código: A15 (Harikrishnan et al., 2019)

Título: Vision-face recognition attend= ance monitoring system for surveillance using deep learning technology and compu= ter vision

Base de Datos: IEEE Xplorer

Año: 2019

Autores: Harikrishnan J Arya, Suda= rsan Remya Ajai, A S Aravind Sadashiv.

Objetivo: Describir un sistema= de vigilancia y asistencia en tiempo real que utiliza redes neuronales artificiales para detectar rostros con aplicaciones en la asistencia universitaria y la seguridad laboral. Destaca una interfaz de usuario intui= tiva y logra una precisión del 74% en la detección de rostros en tiempo real, abordando la necesidad de un sistema fácil de usar para el reconocimiento facial.

= ·&nb= sp;        Código: A16 (Mantegazza et al., 2019)

Título: Learning Vision-Based Quadrotor Contr= ol in User Proximity.

Base de Datos: IEEE Xplorer

Año: 2019

Autores: Dario Mantegazza, Jerome Guzzi, Luca M. Gambardella, Alessandro Giusti.

Objetivo:= Describir el proceso de la capacitación de una red neuronal profunda para anticipar las instrucciones de vuelo del dron utilizando la información capturada por la cámara. Para lograr esto, se recopilan datos de entrenamiento al ejecutar un controlador básico creado manualmente, que se = basa en datos visuales de seguimiento de movimientos.

= ·&nb= sp;        Código: A17 (Yu et al., 2018)

Título:  The Design of Single Moving Object Detection and Recognition System Based on OpenCV.

Base de Datos: IEEE Xplorer

Año: 2018

Autores: Lijun Yu, Weijie Sun, Hui Wang, Qiang Wang and Chaoda Liu.

Objetivo: Proponer un algoritmo llamado FT para detectar y reconocer objetos en movimiento en visión por computadora, mejorando la precisión y eficiencia mediante métricas de distancia, gráficos de características y un clasificador en cascada Haar de baja complejidad. Los resultados experimentales indican su alta precisión y= su potencial en aplicaciones de ingeniería.

= ·&nb= sp;        Código: A18 (O’Mahony et al., 2020)

Título: Deep Learning vs. Traditional Computer Vision

Base de Datos: IEEE Xplorer.

Año: 2019

Autores: Niall O’Mahony, Sean Campbell, Anderson Carvalho, Suman Harapanahalli, Gustavo Velasco Hernández, Lenka Krpalkova, Daniel Riordan, Joseph Walsh.

Objetivo: Generar una discusió= n en torno a la pertinencia de preservar el conocimiento de las técnicas tradicionales de visión por computadora. Además, el documento examinará la = posibilidad de fusionar las dos corrientes de la visión por computadora.

= ·&nb= sp;        Código: A19 (Manju & Valarmathie, 2021)

Título: Video analytics for semantic substance extraction using OpenCV in python

Base de Datos: Scopus

Año: 2021

Autor: A. Manju. P. Valarmathie<= o:p>

Objetivo: Establecer un marco = para la identificación de objetos en datos de video. Se presenta un enfoque que emplea OpenCV para estructurar los recursos de video con el propósito de extraer información semántica.

= ·&nb= sp;        Código: A20 (Sravya et al., 2021)

Título: Automate the fingerprint identificati= on process by image processing with Otsu thresholding.

Base de Datos: Google Scholar<= /o:p>

Año: 2021

Autor: Luis, Barba-Guaman; Carlo= s, Calderon-Cordova; Pablo Alejandro, Quezada-Sarmiento

Objetivo: Explorar fundamentos= teóricos para reconocer objetos por color a través de la umbralización en los tonos rojo, amarillo y verde. Se utiliza Python y OpenCV para mejorar la precisió= n de la detección de objetos de forma gratuita.

= ·&nb= sp;        Código: A21 (Estarita et al., 2017)

Título: Sistema de Reconocimiento = de objetos en tiempo real

Base de Datos: Google Scholar<= /o:p>

Año: 2019

Autor: Jorge Estarita, Andrés Jiménez, Jaime Brochero, Hugo Escobar, Silvia Moreno

Objetivo: Utilizar la tecnolog= ía de visión artificial para llevar a cabo el reconocimiento de un objeto. Este proceso implica la detección del objeto a través de una cámara web y, posteriormente, el software del sistema realiza un análisis de patrones para determinar si coincide con alguno de los objetos almacenados previamente en= una base de datos.

= ·&nb= sp;        Código: A22 (Rodríguez et al., 2015)

Título: Detection of fishes in turbulent wate= rs based on image analysis.

Base de Datos: Google Scholar<= /o:p>

Año: 2018

Autor: Rodríguez, Alvaro; Rabuña= l, Juan R.; Bermudez, Maria; Puertas, Jeronimo

Objetivo: Abordar la cuestión = de la segmentación automática de peces en ambientes acuáticos agitados, se emp= lea una red neuronal de tipo SOM (Mapas Autoorganizados) con el objetivo de identificar peces en imágenes capturadas por un sistema de cámara submarina instalado en una ranura vertical utilizada para el paso de peces en estruct= uras hidráulicas construidas en ríos con el fin de facilitar la migración de los peces río arriba.

= ·&nb= sp;        Código: A23 (Cadena et al., 2019)

Título: Facial recognition techniques using S= VM: A comparative analysis

Base de Datos: Google scholar<= /o:p>

Año: 2019

Autor: José Augusto Cadena Morea= no, Nora Bertha La Serna Palomino, Alex Christian Llano Casa.

Objetivo: Revisar el reconocimiento facial en 2D, destacando su relevancia en la seguridad y el ámbito laboral. Se analizan los resultados de investigaciones que emplean técnicas de extracción de características, clasificación de patrones y base= s de datos, con el fin de determinar las técnicas más eficientes para un reconocimiento facial óptimo en 2D, considerando la calidad de las bases de datos y las herramientas utilizadas.

= ·&nb= sp;        Código: A24 (Gadi et al., 2020)

Título: A Novel Python Program to Automate So= il Colour Analysis and Interpret Surface Moisture Content

Base de Datos: Springer<= /span>

Año: 2020

Autor: Vinay Kumar Gadi, Dastan Alybaev, Priyanshu Raj, Akhil Garg, Guoxiong Mei, Sekharan Sreedeep, Lingar= aj Sahoo

Objetivo:= Crear un script Python adicional con el propósito de automati= zar el proceso de análisis de color del suelo, con el fin de comprender mejor el nivel de humedad presente en la superficie.

= ·&nb= sp;        Código: A25 (Xia et al., 2020)

Título: Design and implementation of tunnel i= mage mosaic system based on Open CV.

Base de Datos: Springer<= /span>

Año: 2019

Autor: Yanhui Xia, Baisheng Nie, Yanan Zhang, Zhengyou Wang, Zhiqiang Wang, Shibo Liu, Baoyue Zhang

Objetivo: Desarrollar un enfoq= ue de mosaico de imágenes utilizando el algoritmo ORB en el campo de la visión artificial para abordar la limitación de ángulo de adquisición de la cámara= en túneles estrechos.

= ·&nb= sp;        Código: A26 (Khuushi et al., 2018)

Título: Real Time Mixing Index Measurement of Microchannels Using OpenCV.

Base de Datos: Springer<= /span>

Año: 2019

Autor: Khuushi, Vanadana Jain, Rajendra Patrikar, and Raghavendra Deshmukh.

Objetivo: Lograr cálculos en tiempo real del Índice de Masa (IM) para optimizar el sistema de microfluid= os, mediante la utilización de una herramienta de procesamiento de imágenes de código abierto, como OpenCV, basada en visión por computadora.

= ·&nb= sp;        Código: A27 (De Lima et al., 2021)

Título: Parallel hashing-based matching for real-time aerial image mosaicing.

Base de Datos: Springer<= /span>

Año: 2021

Autor: Roberto de Lima, Aldrich = A. Cabrera Ponce, José Martínez Carranza.

Objetivo: Desarrollar un emparejador de características eficiente basado en el descriptor ORB (Orien= ted FAST and Rotated BRIEF) y su implementación en tablas hash. Este enfoque ti= ene como finalidad ampliar las aplicaciones de generación de mosaicos aéreos, permitiendo la creación de panorámicas de alta resolución en áreas extensas= y la recopilación de datos detallados de manera simultánea.=

= ·&nb= sp;        Código: A28 (Buzzin et al., 2019)

Título: Advances in Intelligent Systems and Computing.

Base de Datos: Springer<= /span>

Año: 2018

Autor: Alessio, Buzzin; Rita, Asquini; Domenico, Caputo; Giampiero, De

Objetivo: Respaldar y fomentar= investigaciones innovadoras realizadas por estudiantes, investigadores, académicos, científ= icos y profesionales de la industria de la próxima generación. Esto se llevó a c= abo en un entorno compartido con el fin de promover el beneficio mutuo y la colaboración en el intercambio de conocimientos.

= ·&nb= sp;        Código: A29 (Rao et al., 2021)

Título: Artificial intelligence a= nd robotics.

Base de Datos: Springer<= /span>

Año: 2018

Autor: Javier Andreu Perez, Fani Deligianni, Daniele Ravi and Guang-Zhong Yang

Objetivo: Definir una máquina = con inteligencia, es esencial considerar sus implicaciones tanto en el ámbito operativo como en el social. Dado que se estima que el mercado de la Inteligencia Artificial (IA) llegará a los 3 billones de dólares en 2024, t= anto la industria como los organismos gubernamentales de financiación están realizando inversiones significativas en IA y robótica. <= /p>

 

= ·&nb= sp;        Código: A30 (Díaz-Toro et al., 2018)

Título: Dense tracking, mapping and scene labeling using a depth camera.

Base de Datos: Springer<= /span>

Año: 2018

Autor: Andrés Alejandro Díaz-Tor= o; Lina María Paz-Pérez; Pedro Piniés-Rodríguez; Eduardo Francisco Caicedo-Bra= vo

Objetivo: Introducir un sistema que emplea una cámara de profundidad, específicamente el sensor Kinect, con= el propósito de realizar el seguimiento detallado, la reconstrucción tridimensional y la detección de objetos en ambientes que se asemejan a entornos de escritorio.

= ·&nb= sp;        Código: A31 (Auysakul et al., 2019)

Título: Development of Multi-process for Video Stitching in the AVM Applications Based on OpenCV.

Base de Datos: Springer<= /span>

Año: 2019

Autor: Jutamanee Auysakul, He Xu, and Vishwa= nath Pooneeth.

Objetivo: Desarrollar un algor= itmo de multiproceso que permita unir vistas panorámicas completas capturadas de= sde múltiples cámaras de visión periférica en tiempo real, el propósito es mejo= rar la eficiencia y la calidad de la transmisión en vivo en aplicaciones de monitorización automovilística, evitando la necesidad de recalcular parámet= ros constantemente.

= ·&nb= sp;        Código: A32 (Johnston & Chazal, 2018)

Título: A review of image-based automatic fac= ial landmark identification techniques.

Base de Datos: Springer<= /span>

Año: 2018

Autor: Benjamin Johnston, Philip= de Chazal.

Objetivo: Este artículo tiene = como propósito realizar una revisión de la literatura actual relacionada con la = señalización facial, resaltando los notables progresos alcanzados en este ámbito. <= /o:p>

Resultados

Siguiendo la metodología sugerida, que involucra la implementa= ción de cuatro etapas específicas, se aplicaron tres criterios o filtros para la selección de artículos académicos. En la fase inicial, se identificaron aproximadamente 145 trabajos en español e inglés. En esta etapa, se observó= que la mayoría de los trabajos se encontraban en Google Scholar (41%) y en IEEE (34%), como se puede apreciar en la figura 2.

Figura 2

Resultados de la búsqueda según los criterios de selecció= n

Posteriormente, al aplicar el primer filtro que abarcó el perí= odo de publicación de 2015 a 2022, se logró descartar aproximadamente el 40% de= los artículos (63 en total), resultando en 82 artículos relacionados. De estos,= el 49% pertenecen a IEEE Xplorer, el 24% a Google Académico y el 12% a Springe= r.

Tras aplicar los criterios de selección, se obtuvo un total de= 32 artículos. Es relevante mencionar que el 56% de estos artículos se recupera= ron de la base de datos IEEE Xplorer, mientras que el 28% proviene de Springer.=

Figura 3

Total de artículos encontrados según la base de datos

<= /o:p>

 

 

Figura 4

Análisis de los artículos publicados por año

<= /o:p>

Después de organizar la documentación de acuerdo con la metodología SLR (Kulkarni et al., 2020), procedimos a realizar una revisión sistemática de los contenido= s y contribuciones de los 32 artículos, centrándonos en las preguntas de investigación definidas. El filtro utilizado se demostró eficiente al seleccionar los artículos relevantes, como se muestra en la ilustración 3, = que indica que la mayoría de estos artículos provienen de fuentes publicadas en IEEE Xplore. También se realizó un análisis breve basado en el año de publicación, como se presenta en la ilustración 4, donde se observa un aume= nto en la cantidad de publicaciones en el año 2019, y se nota que la mayoría de ellas son relevantes para los últimos cuatro años. La siguiente fase involu= cra el análisis de los resultados obtenidos en respuesta a las cuatro preguntas orientadoras, y a continuación, enumeramos los resultados más destacados.

RQ1: ¿Cuáles son las técnicas de procesamiento de imágenes más avanzadas en la actualidad?

La visión por computadora se basa en técnicas fundamentales como la inversión, umbralización, binarización, transformaciones, filtrado, histogramas, segmentación, entre otras no obstante, en= los últimos cinco años, han surgido nuevas técnicas en el campo de la visión por computadora o visión artificial, ejemplificadas por:

·      =    Aprendizaje profundo:= El aprendizaje profundo representa una técnica de vanguardia en el ámbito del procesamiento de imágenes, como se puede observar en los artículos que abar= can desde Estarita et al. (2017) hasta Rao et al. (2021= ). Esta disciplina del aprendizaje automático desempeña un papel fundamental y sirve como base para diversos campos, incluyendo la Inteligencia Artificial= , la Minería de Datos y la Visión por Computador, tal como se menciona en Rao et al., (2021). Su capacidad para aprender funciones complejas, especialmente cuando la información es intrincada, es notoria. L= os sistemas neuronales simulados, a menudo denominados aproximadores universal= es, tienen la capacidad de modelar cualquier función, independientemente de su complejidad, con tan solo una capa oculta, como se describe en (Khuushi et al., 2018). Además, el aprendizaje profundo= ha experimentado mejoras significativas gracias al aumento en la capacidad de cómputo de los dispositivos, lo que incluye un mayor poder de procesamiento= y memoria.

·      =    Redes neuronales de convolución: = En un total de 7 artículos, se resalta el uso de la detección de imágenes a tr= avés de una red neuronal convolucional conocida como CNN, como se describe en los artículos (Khuushi et al., 2018; De Lima et al., 2021). Además, en el artículo (Xia et al= ., 2020), se menciona la arquitectura de una red neuronal denominada YOLOv3, que se utiliza para el reconocimiento de animales con un alto grado= de certeza.

·      =    Vectores de máquinas de soporte (SM= V): Esta técnica de clasificación de vectores se utiliza para el reconocimiento faci= al (Khuushi et al., 2018; Yu et al., 2018).

RQ2: ¿Cuáles son las aplicaciones más destacadas en la actualidad?

Según los datos recopilados, es posible resaltar diversas áreas de aplicación, siendo la Inteligencia Artificial una de las más relevantes. Es= ta se encuentra estrechamente ligada al aprendizaje profundo y las redes neuronales. Por ejemplo, en el estudio de Cadena et al. (2019), se emplea la Inteligencia Artificial en el proceso de detección de la evolución de la diabetes en niños. En este contexto, la inteligencia artificial se utiliza = para analizar imágenes y se aplican dos métodos de redes convolucionales, como la arquitectura VGGNET y ResNet. Estas redes se emplean para la extracción de características de las imágenes oculares y para establecer conexiones residuales en el proceso (Johnston & Chazal, 2018).

·         Redes Neuronales:= Según los autores Xia et al. (2020), De Lima et al. (2021) y Díaz-Toro et al. (2= 018), se sugiere la implementación de sistemas de clasificación y reconocimiento de imágenes en tiempo real con alta confiabilidad utilizando algoritmos neuronales, como e= n el caso del reconocimiento de actividad humana (HAR). Esto es relevante dado q= ue los métodos convencionales requieren sensores corporales para registrar la actividad humana (Auysakul et al., 2019). En esta líne= a, se aprovecha la información de color RGB y se aplican tres redes neuronales convolucionales (CNN) basadas en aprendizaje profundo (De Lim= a et al., 2021). Se enfrenta el desafío del sobreajuste, que ocurre cuando el modelo se ajus= ta demasiado a los datos de entrenamiento, disminuyendo su capacidad de generalización en datos nuevos y afectando el rendimiento predictivo. Para abordar esto, se exploran estrategias como la agrupación, que regula las re= des neuronales para evitar la selección de valores extremos o mínimos (Rao et al., 2021).

·      =    Visión en robótica: En el campo de la robótica, las técnicas de visión artificial de última genera= ción están transformando la percepción y la interacción de los robots con su entorno. Estas técnicas permiten a los robots reconocer objetos, sortear obstáculos y colaborar de manera segura con humanos (Harikrishnan et al., 2019). Su integración está revolucionando aplicaciones como = la navegación autónoma de vehículos y drones, así como la asistencia en cirugí= as precisas. Esta convergencia de robótica y visión artificial representa una frontera tecnológica en constante evolución con vastas aplicaciones y poten= cial para mejorar nuestra vida cotidiana.

·         Aplicaciones relacionadas con la Medicina: En el estudio de Cadena et al. (2019), se propone = la aplicación de deep learning para diagnosticar la retinopatía diabética en pacientes mediante la evaluación de imágenes de sus ojos. Además, en Swain = et al. (2018), se presenta una herramienta automatizada que utiliza Python y OpenCV para calcular la parasitemia en frotis de sangre periférica, emplean= do técnicas de procesamiento de imágenes como el filtro de Gauss y el análisis= de histogramas de color.

RQ3: ¿Cuáles son los ejemplos de aplicaciones que están influyendo en la investigación en el ámbito del procesamiento de imágenes?=

·      =    El aprendizaje profundo a través de re= des neuronales ofrece un vasto abanico de posibilidades en el ámbito de la investigación, = un ejemplo concreto de esto es el desarrollo de redes neuronales pre entrenadas como YOLO (Cadena et al., 2019; Xia et al., 2020; De Lima et al., 2021), las cuales permiten a los investigadores enfocarse en los resultados específico= s de sus estudios correspondientes.<= /span>

·         CNN, que significa "red neuronal convolucional", es el pionero entre los métodos de aprendizaje profund= o y consta de tres componentes fundamentales. Por otro lado, IF-CNN se refiere = a un método diseñado para permitir una inferencia rápida al reducir la carga computacional. En este marco, se inicia construyendo un conjunto de modelos= que incluyen diferentes niveles de complejidad en las redes neuronales convoluc= ionales (CNN). Este enfoque se describe en detalle en Rao et al. (2021)= .

RQ4: ¿Cuál es el panorama futuro de la visión artificial dentro de los Sistemas Inteligentes?

·      =    La visión por computadora y el aprendi= zaje automático han generado un nuevo panorama tecnológico que abarca diversas industrias, desde la salud hasta la realidad virtual y la automatización de vehículos, con aplicaciones que incluyen la identificación de enfermedades y experiencias de inversión. IEC se ha interesado en cuestiones de seguridad = y rendimiento en robots aspiradores y cortacésped para el hogar y se utiliza en industrias como la informática y la automatización industrial (Pavithra & Suresh, 2019).

·         La visión artificial se ha vuelto efic= az en la detección de retinopatía diabética en países con recursos limitados, = lo que complementa la estrategia VISIÓN 2020 para mejorar la atención oftalmológica, los sistemas de inteligencia artificial pueden identificar riesgos sistémicos y ofrecen resultados más rápidos que los evaluadores humanos. Esto se refleja en áreas verdes en imágenes de fondo de retina, que indican las contribuciones del modelo de IA en casos de retinopatía diabéti= ca referible (Rao et al., 2021).=

Durante la selección de los 32 artículos, se observó que la mayoría de las fuentes = sobre técnicas de procesamiento de imágenes provienen de IEEE y Springer. Aunque = hay una abundancia de información sobre visión artificial y redes neuronales, la investigación se enfoca en artículos en inglés seleccionados de fuentes confiables, siendo más del 50% de IEEE y el resto en su mayoría disponibles= en Springer,= además, la mayo= ría de los artículos o congresos se han indexado en 2019, con un análisis leve = que incluye datos hasta el último trimestre de 2021.

El análisis de las dos fuentes, IEEE y Springer revela que las aplicaciones de visión por computadora se consideran un campo de vanguardia; el examen de l= as preguntas de investigación también destaca la diversidad de aplicaciones en visión por computadora y la estrecha relación entre las redes neuronales y = el aprendizaje profundo, especialmente en sistemas de dispositivos inteligentes utilizados para clasificación, predicción y detección de imágenes procesada= s.

Finalmente, se llevó a cabo un análisis en relación con las palabras clave empleadas en= la revisión, lo que implicó la creación de una tabla que muestra la frecuencia= de repetición de estas palabras clave en las obras seleccionadas durante la fa= se final del proceso, como se muestra a continuación:

Figura 5

Resultados de las palabras usadas en la búsqueda

<= /o:p>

 

Conclusiones

·      =    La mayoría de los artículos relevantes relacionados con el tema en cuestión fueron escritos en inglés y se localiz= aron en bases de datos como IEEE y Springer. No se logró obtener una cantidad significativa de información de Scopus debido a los costos asociados a su utilización.

·      =    Los sistemas inteligentes tienen aplicaciones en una amplia variedad de campos, pero en este estudio en particular, se enfocan principalmente en la implementación de una técnica q= ue involucra la clasificación y detección de objetos en tiempo real mediante el uso de redes neuronales convolucionales.

·      =    Se puede concluir que la mayoría de las aplicaciones de vanguardia en visión artificial han sido concebidas, desarrolladas y registradas principalmente a partir de 2017, esto sugiere q= ue los investigadores ecuatorianos podrían encontrar oportunidades valiosas pa= ra explorar y contribuir en este ámbito, ya que existen numerosas aplicaciones potenciales que podrían ser desarrolladas en beneficio de Ecuador.

·      =    Como dato relevante para futuros traba= jos, es importante destacar que una parte significativa de los algoritmos de pun= ta en el campo de la visión artificial se crean utilizando Python y OpenCV como sus principales herramientas de desarrollo.

Conflicto de intereses

Los autores declararan que no existe c= onflicto de intereses en relación con el artículo presentado.

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El artículo queda en propiedad de la revista y, por tanto, su publicación parcial y/o total en otro medio tiene que ser autoriz= ado por el director de la Revista Cien= cia Digital.

 

 

 

 

 


 

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