Desarrollo de un sistema para el reconocimiento de dígitos en las lecturas de las pantallas ("displays") de instrumentos de presión mediante técnicas de procesamiento de imágenes, visión e inteligencia artificial para almacenamiento en una base de datos

La calibración de los manómetros de presión constituye un proceso imprescindible que asegura la trazabilidad, la fiabilidad y la seguridad en el ámbito industrial. Sin embargo, en gran parte de la instrumentación de presión no existen interfaces digitales de acceso para poder obtener las lecturas, l...

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Autores:: Angulo Pineda, Jhon Alejandro

Tipo de recurso:

Fecha de publicación:: 2025

Institución:: Universidad Autónoma de Bucaramanga - UNAB

Repositorio:: Repositorio UNAB

Idioma:: spa

id	UNAB2_6ed9f833e2a31215f0a3b8e078d9f562
oai_identifier_str	oai:repository.unab.edu.co:20.500.12749/32357
network_acronym_str	UNAB2
network_name_str	Repositorio UNAB
repository_id_str
dc.title.spa.fl_str_mv	Desarrollo de un sistema para el reconocimiento de dígitos en las lecturas de las pantallas ("displays") de instrumentos de presión mediante técnicas de procesamiento de imágenes, visión e inteligencia artificial para almacenamiento en una base de datos
dc.title.translated.spa.fl_str_mv	Development of a system for digit recognition in pressure instrument display readings using image processing, vision, and artificial intelligence techniques for database storage
title	Desarrollo de un sistema para el reconocimiento de dígitos en las lecturas de las pantallas ("displays") de instrumentos de presión mediante técnicas de procesamiento de imágenes, visión e inteligencia artificial para almacenamiento en una base de datos
spellingShingle	Desarrollo de un sistema para el reconocimiento de dígitos en las lecturas de las pantallas ("displays") de instrumentos de presión mediante técnicas de procesamiento de imágenes, visión e inteligencia artificial para almacenamiento en una base de datos Digital metrology Computer vision CNN Pressure instruments Engineering systems Technological innovations Software development Image processing Optical data processing Ingeniería de sistemas Innovaciones tecnológicas Desarrollo de software Procesamiento de imágenes Procesamiento óptico de datos Metrología digital Visión por computador OCR YOLO Instrumentos de presión
title_short	Desarrollo de un sistema para el reconocimiento de dígitos en las lecturas de las pantallas ("displays") de instrumentos de presión mediante técnicas de procesamiento de imágenes, visión e inteligencia artificial para almacenamiento en una base de datos
title_full	Desarrollo de un sistema para el reconocimiento de dígitos en las lecturas de las pantallas ("displays") de instrumentos de presión mediante técnicas de procesamiento de imágenes, visión e inteligencia artificial para almacenamiento en una base de datos
title_fullStr	Desarrollo de un sistema para el reconocimiento de dígitos en las lecturas de las pantallas ("displays") de instrumentos de presión mediante técnicas de procesamiento de imágenes, visión e inteligencia artificial para almacenamiento en una base de datos
title_full_unstemmed	Desarrollo de un sistema para el reconocimiento de dígitos en las lecturas de las pantallas ("displays") de instrumentos de presión mediante técnicas de procesamiento de imágenes, visión e inteligencia artificial para almacenamiento en una base de datos
title_sort	Desarrollo de un sistema para el reconocimiento de dígitos en las lecturas de las pantallas ("displays") de instrumentos de presión mediante técnicas de procesamiento de imágenes, visión e inteligencia artificial para almacenamiento en una base de datos
dc.creator.fl_str_mv	Angulo Pineda, Jhon Alejandro
dc.contributor.advisor.none.fl_str_mv	Arizmendi Pereira, Carlos Julio
dc.contributor.author.none.fl_str_mv	Angulo Pineda, Jhon Alejandro
dc.contributor.cvlac.spa.fl_str_mv	Angulo Pineda, Jhon Alejandro [0001357319] Arizmendi Pereira, Carlos Julio [1381550]
dc.contributor.googlescholar.spa.fl_str_mv	Angulo Pineda, Jhon Alejandro [Dhm18h4AAAAJ] Arizmendi Pereira, Carlos Julio [JgT_je0AAAAJ]
dc.contributor.orcid.spa.fl_str_mv	Angulo Pineda, Jhon Alejandro [0009-0007-6768-3554]
dc.contributor.researchgate.spa.fl_str_mv	Angulo Pineda, Jhon Alejandro [Jhon-Angulo-Pineda] Arizmendi Pereira, Carlos Julio [Carlos_Arizmendi2]
dc.contributor.apolounab.spa.fl_str_mv	Arizmendi Pereira, Carlos Julio [carlos-julio-arizmendi-pereira]
dc.contributor.linkedin.spa.fl_str_mv	Angulo Pineda, Jhon Alejandro [jhon-alejandro-a-a81118143]
dc.subject.keywords.spa.fl_str_mv	Digital metrology Computer vision CNN Pressure instruments Engineering systems Technological innovations Software development Image processing Optical data processing
topic	Digital metrology Computer vision CNN Pressure instruments Engineering systems Technological innovations Software development Image processing Optical data processing Ingeniería de sistemas Innovaciones tecnológicas Desarrollo de software Procesamiento de imágenes Procesamiento óptico de datos Metrología digital Visión por computador OCR YOLO Instrumentos de presión
dc.subject.lemb.spa.fl_str_mv	Ingeniería de sistemas Innovaciones tecnológicas Desarrollo de software Procesamiento de imágenes Procesamiento óptico de datos
dc.subject.proposal.spa.fl_str_mv	Metrología digital Visión por computador OCR YOLO Instrumentos de presión
description	La calibración de los manómetros de presión constituye un proceso imprescindible que asegura la trazabilidad, la fiabilidad y la seguridad en el ámbito industrial. Sin embargo, en gran parte de la instrumentación de presión no existen interfaces digitales de acceso para poder obtener las lecturas, las cuales dependen de métodos manuales, lentos y propenso a errores de transcripción. Ante esta problemática, en el presente trabajo se presenta un sistema de reconocimiento automático de los dígitos de las pantallas digitales de los instrumentos de presión, investigando para ello técnicas de procesamiento de imágenes, de visión por computador y de inteligencia artificial. La metodología utilizada en este estudio incluye las etapas de adquisición y preprocesamiento de imagen, segmentación de regiones de interés, y OCR (del inglés Optical Character Recognition), usando modelos de aprendizaje profundo soportados en CNN (del inglés Convolutional Neural Network) y detección soportada en YOLO. Se llevó a cabo la implementación de este sistema desarrollando una base de datos local donde estarían guardados los registros con un sello temporal y clave de dispositivo y una interfaz gráfica que permita capturar, validar y enviar imágenes. Finalmente, la experimentación concluyó que el modelo CNN alcanza métricas por encima del 95 % en precisión, recall y F1-Score en los conjuntos de validación y de prueba, mientras que la implementación de YOLO permite la detección robusta de la región de los dígitos (dígitos en condiciones de variabilidad de la iluminación). También se muestra la reducción del tiempo que requiere el procesamiento del registro si se compara contra el procesamiento manual del registro, lo que mejora la eficiencia y disminuye el riesgo de errores de transcripción. Los resultados demuestran la viabilidad de esta propuesta como la alternativa no intrusiva para digitalizar los instrumentos de presión sin interfaces de comunicación estándar, a la vez que están absolutamente ligados a la optimización de los procesos metrológicos en la transición hacia laboratorios inteligentes y trazables en la Industria 4.0.
publishDate	2025
dc.date.accessioned.none.fl_str_mv	2025-12-01T20:52:35Z
dc.date.available.none.fl_str_mv	2025-12-01T20:52:35Z
dc.date.issued.none.fl_str_mv	2025-10-31
dc.type.eng.fl_str_mv	Thesis
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.local.spa.fl_str_mv	Tesis
dc.type.hasversion.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.redcol.none.fl_str_mv	http://purl.org/redcol/resource_type/TM
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	http://hdl.handle.net/20.500.12749/32357
dc.identifier.instname.spa.fl_str_mv	instname:Universidad Autónoma de Bucaramanga - UNAB
dc.identifier.reponame.spa.fl_str_mv	reponame:Repositorio Institucional UNAB
dc.identifier.repourl.spa.fl_str_mv	repourl:https://repository.unab.edu.co
url	http://hdl.handle.net/20.500.12749/32357
identifier_str_mv	instname:Universidad Autónoma de Bucaramanga - UNAB reponame:Repositorio Institucional UNAB repourl:https://repository.unab.edu.co
dc.language.iso.spa.fl_str_mv	spa
language	spa
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spelling	Arizmendi Pereira, Carlos Julio79e0125f-b191-4144-999b-281177ddaaf9Angulo Pineda, Jhon Alejandro16a9373c-8744-4b88-ac86-a2c7eb6fec87Angulo Pineda, Jhon Alejandro [0001357319]Arizmendi Pereira, Carlos Julio [1381550]Angulo Pineda, Jhon Alejandro [Dhm18h4AAAAJ]Arizmendi Pereira, Carlos Julio [JgT_je0AAAAJ]Angulo Pineda, Jhon Alejandro [0009-0007-6768-3554]Angulo Pineda, Jhon Alejandro [Jhon-Angulo-Pineda]Arizmendi Pereira, Carlos Julio [Carlos_Arizmendi2]Arizmendi Pereira, Carlos Julio [carlos-julio-arizmendi-pereira]Angulo Pineda, Jhon Alejandro [jhon-alejandro-a-a81118143]Bucaramanga (Santander, Colombia)6 mesesUNAB Campus Bucaramanga2025-12-01T20:52:35Z2025-12-01T20:52:35Z2025-10-31http://hdl.handle.net/20.500.12749/32357instname:Universidad Autónoma de Bucaramanga - UNABreponame:Repositorio Institucional UNABrepourl:https://repository.unab.edu.coLa calibración de los manómetros de presión constituye un proceso imprescindible que asegura la trazabilidad, la fiabilidad y la seguridad en el ámbito industrial. Sin embargo, en gran parte de la instrumentación de presión no existen interfaces digitales de acceso para poder obtener las lecturas, las cuales dependen de métodos manuales, lentos y propenso a errores de transcripción. Ante esta problemática, en el presente trabajo se presenta un sistema de reconocimiento automático de los dígitos de las pantallas digitales de los instrumentos de presión, investigando para ello técnicas de procesamiento de imágenes, de visión por computador y de inteligencia artificial. La metodología utilizada en este estudio incluye las etapas de adquisición y preprocesamiento de imagen, segmentación de regiones de interés, y OCR (del inglés Optical Character Recognition), usando modelos de aprendizaje profundo soportados en CNN (del inglés Convolutional Neural Network) y detección soportada en YOLO. Se llevó a cabo la implementación de este sistema desarrollando una base de datos local donde estarían guardados los registros con un sello temporal y clave de dispositivo y una interfaz gráfica que permita capturar, validar y enviar imágenes. Finalmente, la experimentación concluyó que el modelo CNN alcanza métricas por encima del 95 % en precisión, recall y F1-Score en los conjuntos de validación y de prueba, mientras que la implementación de YOLO permite la detección robusta de la región de los dígitos (dígitos en condiciones de variabilidad de la iluminación). También se muestra la reducción del tiempo que requiere el procesamiento del registro si se compara contra el procesamiento manual del registro, lo que mejora la eficiencia y disminuye el riesgo de errores de transcripción. Los resultados demuestran la viabilidad de esta propuesta como la alternativa no intrusiva para digitalizar los instrumentos de presión sin interfaces de comunicación estándar, a la vez que están absolutamente ligados a la optimización de los procesos metrológicos en la transición hacia laboratorios inteligentes y trazables en la Industria 4.0.Corporación Centro de Desarrollo Tecnológico del GasÍndice de Figuras 8 Índice de Tablas 10 Resumen 12 Abstract 14 Marco Introductorio 15 Introducción 15 Antecedentes 16 Planteamiento del Problema 19 Justificación 21 Pregunta de Investigación 23 Objetivos 25 Objetivo General 25 Objetivos Específicos 25 Marco Teórico 26 Fundamentos de Metrología y Calibración de Presión 26 Conceptos Básicos de Metrología 26 Calibración y Verificación de Instrumentos 28 Incertidumbre, Repetibilidad y Reproducibilidad 29 Confiabilidad y Trazabilidad en Metrología 31 Metrología en la Era Digital 33 La Transformación Digital en la Práctica Metrológica 33 Limitaciones de los Instrumentos en Uso 34 Visión por Computador Aplicada a la Metrología 35 Detectores Basados en YOLO 36 Modelos CNN Aplicados a OCR de Dígitos 40 Métricas de Desempeño en Visión por Computador 43 Bases de Datos y Almacenamiento de Información 44 Interfaces Hombre–Máquina y Digitalización de Procesos 45 Estado del Arte 46 Reconocimiento Óptico de Caracteres en Displays LED y LCD 46 Aprendizaje Profundo en Reconocimiento de Caracteres 49 Reconocimiento Basado en IA y Deep Learning 50 Implementaciones Tecnológicas en Adquisición de Imágenes 53 Integración con Bases de Datos y Aplicaciones en Industria 4.0 56 Síntesis Crítica y Vacíos Identificados 59 Metodología y Resultados 62 Diseño Metodológico y Enfoque Experimental 62 Arquitectura General del Sistema 63 Construcción y Validación del Dataset 67 Entrenamiento y Resultados de los Modelos de Reconocimiento: YOLO, CNN 74 Resultados del Modelo YOLO 75 Red Neuronal Convolucional (CNN) 79 Comparación de Modelos CNN. 80 Análisis del desempeño del modelo cnn_s. 87 Análisis del desempeño del modelo cnn_m. 90 Análisis del desempeño del modelo cnn_l. 92 Comparación del Enfoque YOLO-CNN Frente a OCR Tradicionales 95 Evaluación de Eficiencia Frente al Proceso Manual 97 Integración del Sistema con la GUI y la Base de Datos 99 Validación estadística 102 Discusión 110 Conclusiones 113 Referencias 116 Apéndice A. Métricas de desempeño en visión por computador 145 Apéndice B. Tablas de Bases de Datos 147 Apéndice C. Fundamentos Matemáticos del Aprendizaje Profundo en OCR 149 Apéndice D. Componentes de Hardware y Software Utilizados 151 Apéndice E. Modelos CNN 156 Apéndice F. Funciones de Activación 160 Rectified Linear Unit (ReLU) 160 Leaky Rectified Linear Unit (Leaky ReLU) 161 Gaussian Error Linear Unit (GELU) 161 Apéndice G. Análisis Estadístico no Paramétrico 163 Test de Friedman 163 Estructura de los Datos 163 Cálculo del estadístico de Friedman 164 Corrección del Iman-Davenport 165 Criterio de Nemenyi 165MaestríaThe calibration of pressure gauges is an essential process that ensures traceability, reliability, and safety in the industrial sector. However, in much pressure instrumentation there are no digital interfaces for accessing readings, which rely on manual methods that are slow and prone to transcription errors. In response to this problem, the present work introduces an automatic digit recognition system for the digital displays of pressure instruments, exploring image processing, computer vision, and artificial intelligence techniques. The methodology used in this study includes image acquisition and preprocessing, region-of-interest segmentation, and OCR (Optical Character Recognition), using deep learning models based on CNNs (Convolutional Neural Networks) and detection powered by YOLO. The system was implemented by developing a local database to store records with a timestamp and device key, and a graphical interface to capture, validate, and send images. Finally, the experiments concluded that the CNN model achieves metrics above 95% in precision, recall, and F1-score on the validation and test sets, while the YOLO implementation enables robust detection of the digit region (digits under varying lighting conditions). It also demonstrates the reduction in the time required to process the record compared to manual processing, thereby improving efficiency and reducing the risk of transcription errors. The results demonstrate the viability of this proposal as a non-intrusive alternative for digitizing pressure instruments without standard communication interfaces, while being fully aligned with the optimization of metrological processes in the transition toward smart, traceable laboratories in Industry 4.0.Modalidad Presencialapplication/pdfspahttp://creativecommons.org/licenses/by-nc-nd/2.5/co/Abierto (Texto Completo)Atribución-NoComercial-SinDerivadas 2.5 Colombiahttp://purl.org/coar/access_right/c_abf2Desarrollo de un sistema para el reconocimiento de dígitos en las lecturas de las pantallas ("displays") de instrumentos de presión mediante técnicas de procesamiento de imágenes, visión e inteligencia artificial para almacenamiento en una base de datosDevelopment of a system for digit recognition in pressure instrument display readings using image processing, vision, and artificial intelligence techniques for database storageThesisinfo:eu-repo/semantics/masterThesisTesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/redcol/resource_type/TMMagíster en Automatización Industrial y MecatrónicaUniversidad Autónoma de Bucaramanga UNABFacultad IngenieríaMaestría en Automatización Industrial y MecatrónicaMAI-2384Digital metrologyComputer visionCNNPressure instrumentsEngineering systemsTechnological innovationsSoftware developmentImage processingOptical data processingIngeniería de sistemasInnovaciones tecnológicasDesarrollo de softwareProcesamiento de imágenesProcesamiento óptico de datosMetrología digitalVisión por computadorOCRYOLOInstrumentos de presiónAkkoyun, F. 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IEEE Transactions on Industrial Informatics, 17(6), 4115–4126. https://doi.org/10.1109/TII.2020.3029786ORIGINALTesis.pdfTesis.pdfTesisapplication/pdf3519488https://repository.unab.edu.co/bitstream/20.500.12749/32357/1/Tesis.pdf4cfdd2ab4b7cee521228d3935d4cde11MD51open accessproyecto_ocr_presion.zipproyecto_ocr_presion.zipMaterial suplementario: Código fuente, datasets y resultados experimentalesapplication/zip2710782634https://repository.unab.edu.co/bitstream/20.500.12749/32357/2/proyecto_ocr_presion.zip390a715be6efd0078fc7149ae1d32f26MD52open accessLicencia.pdfLicencia.pdfLicenciaapplication/pdf328010https://repository.unab.edu.co/bitstream/20.500.12749/32357/6/Licencia.pdf8556926f43f406f5bae31dae41f8535dMD56metadata only accessLICENSElicense.txtlicense.txttext/plain; charset=utf-8829https://repository.unab.edu.co/bitstream/20.500.12749/32357/5/license.txt3755c0cfdb77e29f2b9125d7a45dd316MD55open accessTHUMBNAILTesis.pdf.jpgTesis.pdf.jpgIM Thumbnailimage/jpeg4787https://repository.unab.edu.co/bitstream/20.500.12749/32357/7/Tesis.pdf.jpgde94c9442887770d99054e59691fe00fMD57open accessLicencia.pdf.jpgLicencia.pdf.jpgIM Thumbnailimage/jpeg10709https://repository.unab.edu.co/bitstream/20.500.12749/32357/8/Licencia.pdf.jpgffcefbb7f07ff8caa5a87fdd8426058dMD58metadata only access20.500.12749/32357oai:repository.unab.edu.co:20.500.12749/323572025-12-01 22:02:12.96open accessRepositorio Institucional \| Universidad Autónoma de Bucaramanga - UNABrepositorio@unab.edu.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

Desarrollo de un sistema para el reconocimiento de dígitos en las lecturas de las pantallas ("displays") de instrumentos de presión mediante técnicas de procesamiento de imágenes, visión e inteligencia artificial para almacenamiento en una base de datos

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