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Automated defect detection approach for production processes of patterned steel plates using computer vision and deep learning
dc.rights.license | Atribución-NoComercial-SinDerivadas 4.0 Internacional |
dc.contributor.advisor | Prieto Ortiz, Flavio Augusto |
dc.contributor.author | Kicker, Claudia |
dc.date.accessioned | 2022-08-26T20:21:33Z |
dc.date.available | 2022-08-26T20:21:33Z |
dc.date.issued | 2022 |
dc.identifier.uri | https://repositorio.unal.edu.co/handle/unal/82144 |
dc.description | ilustraciones, graficas |
dc.description.abstract | Anomaly detection is of great importance in the production of steel plates, in order to guarantee that the products are defect-free. Various deep-learning approaches for defect-detection in steel surfaces have emerged in the recent years, however, they are mainly limited to plain steel surfaces. Furthermore, deep-learning-based anomaly detection is still a challenging task if not enough training samples are available, which is often the case in real world scenarios. As for patterned steel plates, the availability anomalous samples is low, as productions are optimized to minimize the occurrence of defects. Hence, the main purpose of this work is the determination of a suitable deep learning-based method for the detection of surface anomalies in patterned steel plates. Several methods were trained and compared in terms of segmentation ability and classification accuracy. On the one hand, a convolutional neural network pretrained on artificial defects was adapted to images from a different production line, of which only anomaly-free data was available for training. On the other hand, an autoencoder was trained in a semi-supervised fashion to reconstruct anomaly-free images, in order to identify defective regions by measuring the reconstruction error. Moreover, an analysis of the frequency spectrum for images of patterned steel plates under the application of discrete fourier transform is provided. It was found out that a reconstructing autoencoder trained with a structural similarity loss provided the most accurate localizations of surface anomalies. |
dc.description.abstract | La detección de anomalías es de gran importancia en la producción de placas de acero para garantizar que los productos no tengan defectos. En los últimos años han surgido varios métodos de aprendizaje profundo para la detección de defectos en superficies de acero limitándose principalmente a superficies de acero planas. Además, la detección de anomalías basada en el aprendizaje profundo sigue siendo una tarea difícil si no se dispone de suficientes muestras de entrenamiento, lo que suele ocurrir en escenarios del mundo real. En cuanto a las placas de acero texturizadas, como las láminas alfajor, la disponibilidad de muestras anómalas es baja, ya que las producciones están optimizadas para minimizar la aparición de defectos. Por lo tanto, el objetivo principal de este trabajo es la determinación de un método adecuado basado en el aprendizaje profundo, para la detección de anomalías superficiales en placas de acero texturizadas. Se entrenaron varios modelos, los que se compararon en términos de capacidad de segmentación y precisión de clasificación. Por un lado, se adaptó una red neuronal convolucional pre-entrenada en defectos artificiales a imágenes procedentes de una línea de producción diferente, de la que solo se disponía de datos libres de anomalías para su entrenamiento. Por otro lado, se entrenó un autocodificador de forma semi-supervisada para reconstruir imágenes libres de anomalías, con el fin de identificar las regiones defectuosas midiendo el error de reconstrucción. Además, se realiza un análisis del espectro de frecuencias para las imágenes de placas de acero texturizadas bajo la aplicación de la transformada discreta de Fourier. Se descubrió que un autocodificador de reconstrucción entrenado con una función de pérdida que mide la similitud estructural, proporciona las localizaciones más precisas de las anomalías superficiales. (Texto tomado de la fuente) |
dc.format.extent | xvi, 48 páginas |
dc.format.mimetype | application/pdf |
dc.language.iso | eng |
dc.publisher | Universidad Nacional de Colombia |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ |
dc.subject.ddc | 670 - Manufactura::672 - Hierro, acero, otras aleaciones ferrosas |
dc.title | Automated defect detection approach for production processes of patterned steel plates using computer vision and deep learning |
dc.type | Trabajo de grado - Maestría |
dc.type.driver | info:eu-repo/semantics/masterThesis |
dc.type.version | info:eu-repo/semantics/acceptedVersion |
dc.publisher.program | Bogotá - Ingeniería - Maestría en Ingeniería - Ingeniería Mecánica |
dc.description.degreelevel | Maestría |
dc.description.degreename | Maestría en Ingeniería - Ingeniería Mecánica |
dc.description.researcharea | Automation, Control and Mechatronics |
dc.identifier.instname | Universidad Nacional de Colombia |
dc.identifier.reponame | Repositorio Institucional Universidad Nacional de Colombia |
dc.identifier.repourl | https://repositorio.unal.edu.co/ |
dc.publisher.department | Departamento de Ingeniería Mecánica y Mecatrónica |
dc.publisher.faculty | Facultad de Ingeniería |
dc.publisher.place | Bogotá, Colombia |
dc.publisher.branch | Universidad Nacional de Colombia - Sede Bogotá |
dc.relation.indexed | RedCol |
dc.relation.indexed | LaReferencia |
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dc.rights.accessrights | info:eu-repo/semantics/openAccess |
dc.subject.lemb | LAMINAS DE HIERRO Y ACERO |
dc.subject.lemb | Plates, iron and steel |
dc.subject.proposal | Deep learning |
dc.subject.proposal | Anomaly detection |
dc.subject.proposal | Autoencoders |
dc.subject.proposal | CNN |
dc.subject.proposal | Structural similarity |
dc.subject.proposal | Aprendizaje profundo |
dc.subject.proposal | Detección de anomalías |
dc.subject.proposal | Autocodificador |
dc.subject.proposal | Red neuronal convolucional |
dc.subject.proposal | Similitud estructural |
dc.title.translated | Método para la detección automatizada de defectos en la producción de láminas de acero alfajor mediante visión artificial y aprendizaje profundo |
dc.type.coar | http://purl.org/coar/resource_type/c_bdcc |
dc.type.coarversion | http://purl.org/coar/version/c_ab4af688f83e57aa |
dc.type.content | Text |
dc.type.redcol | http://purl.org/redcol/resource_type/TM |
oaire.accessrights | http://purl.org/coar/access_right/c_abf2 |
dcterms.audience.professionaldevelopment | Estudiantes |
dcterms.audience.professionaldevelopment | Investigadores |
dcterms.audience.professionaldevelopment | Maestros |
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