Ítem
Acceso Abierto

Tornidentifier: identificación y clasificación automática de tornillos con redes neuronales profundas

Mostrar el registro sencillo de la publicación
dc.contributor.advisorAndrade Lotero, Edgar José
dc.contributor.advisorAlférez Baquero, Edwin Santiago
dc.creatorGarcía Espitia, Luis Alejandro
dc.creatorRojas Gacha, Juan David
dc.creator.degreeMagíster en Matemáticas Aplicadas y Ciencias de la Computación
dc.creator.degreetypeFull time
dc.date.accessioned2023-03-27T17:33:04Z
dc.date.available2023-03-27T17:33:04Z
dc.date.created2023-01-05
dc.descriptionLa tarea de clasificación de tornillos hasta el momento es solo ejecutada por humanos. De hecho, las fotos no son aceptadas como insumo para la clasificación de tornillos debido a que existe información que no se puede determinar con las imágenes, como el diámetro del tornillo y el paso de la rosca. Con el avance de los modelos del aprendizaje automático de maquina y la inclusión de la clasificación automática de imágenes digitales con arquitecturas de redes neuronales profundas, no se ha explorado la solución de esta tarea, en gran parte, porque el factor trascendental para su entrenamiento es un conjunto de datos apropiado que no existe para este problema. En el presente proyecto se construyó un conjunto de imágenes inédito con el cual se pretende entrenar redes neuronales profundas para la clasificación de los tornillos. Además, se entrenó un modelo de detección de objetos especializados para tornillos el cual funcionará juntamente con el modelo de clasificación para aparte de dar una clasificación se identifique en que parte de la imagen este el tornillo. Por último, los modelos fueron puestos en producción dentro de una interfaz en la cual el objetivo es subir una imagen con tornillos y que los modelos sean capaces de detectar donde están y clasificar sus características
dc.description.abstractThe screw classification task so far is only performed by humans. In fact, the photos are not accepted as input for the screw classification because there is information that cannot be determined with the images, such as the diameter of the screw and the pitch of the thread. With the advancement of machine learning models and the inclusion of automatic classification of digital images with deep neural network architectures, the solution to this task has not been explored, largely because the transcendental factor for its training is an appropriate data set that does not exist for this problem. In the present project, a set of unpublished images was built with which it is intended to train deep neural networks for the classification of screws. In addition, a specialized object detection model for screws was trained, which will work together with the classification model so that, apart from giving a classification, it identifies which part of the image the screw is in. Finally, the models were put into production within an interface in which the objective is to upload an image with screws and for the models to be able to detect where they are and classify their features
dc.format.extent42 pp
dc.format.mimetypeapplication/pdf
dc.identifier.doihttps://doi.org/10.48713/10336_38281
dc.identifier.urihttps://repository.urosario.edu.co/handle/10336/38281
dc.language.isospa
dc.publisherUniversidad del Rosariospa
dc.publisher.departmentEscuela de Ingeniería, Ciencia y Tecnologíaspa
dc.publisher.programMaestría en Matemáticas Aplicadas y Ciencias de la Computaciónspa
dc.rightsAttribution-ShareAlike 4.0 International*
dc.rights.accesRightsinfo:eu-repo/semantics/openAccess
dc.rights.accesoAbierto (Texto Completo)
dc.rights.urihttp://creativecommons.org/licenses/by-sa/4.0/*
dc.source.bibliographicCitationS. Russel and P. Norvig, ((Artificial Intelligence: A Modern Approach)), Pearson Education Limited, Third Edition, 2016.
dc.source.bibliographicCitationMillán Gómez, Sim´on (2006). Procedimientos de Mecanizado. Madrid: Editorial Paraninfo, 19/05/2022.
dc.source.bibliographicCitationOberg, Erik, 1881-; McCauley, Christopher J. (2012). Machinery’s handbook : a reference book for the mechanical engineer, designer, manufacturing engineer, draftsman, toolmaker, and machinist (29th ed edici´on). Industrial Press
dc.source.bibliographicCitationAlex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton, ((ImageNet Classification with Deep Convolutional Neural Networks)), Comunications of the ACM, Vol 60 No 6 p´ags 84 - 90, 2017
dc.source.bibliographicCitationDan Ciresan, Ueli Meier and Jurguen Schmidhuber, ((Multi-Column Deep Neural Networks for Image Classification ))
dc.source.bibliographicCitationA Comprehensive Guide to Convolutional Neural Networks — the ELI5 way, https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neuralnetworks- the-eli5-way-3bd2b1164a53, 19/05/2022
dc.source.bibliographicCitationRoss Girshick, Jeff Donahue, Trevor Darrell and Jitendra Malik , ((Rich Feature hierarchies for accurate object the tension and semantic segmentation)) CVPR. 2014.
dc.source.bibliographicCitationAlbert Soto, ((YOLO object detector for onboard driving images)) Escola d´Enginyeria Universidad Autónoma de Barcelona.
dc.source.bibliographicCitationLeCun, Y., Huang, F., Bottou, L. , ((Learning methods for generic object recognition with invariance to pose and lighting)) In Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004, CVPR 2004. Volume 2 (2004). IEEE, II–97.
dc.source.bibliographicCitationLing Guan; Yifeng He; Sun-Yuan Kung (1 March 2012). Multimedia Image and Video Processing. CRC Press. pp. 331–. ISBN 978-1-4398-3087-1.
dc.source.bibliographicCitationGriffin, G., Holub, A., Perona, P, ((Caltech-256 object category dataset. )) Technical Report 7694, California Institute of Technology, 2007.
dc.source.bibliographicCitationYOLO architecture, https://www.researchgate.net/figure/YOLO-architecture-YOLOarchitecture- is-inspired-by-GooLeNet-model-for-image fig2 329038564, 20/05/2022.
dc.source.bibliographicCitationFei-Fei, L., Fergus, R., Perona, P., ((Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories)), Comput. Vision Image Understanding 106, 1 (2007), 59–70.
dc.source.bibliographicCitationKrizhevsky, A, ((Learning multiple layers of features from tiny images)). Master’s thesis, Department of Computer Science, University of Toronto, 2009
dc.source.bibliographicCitationSzegedy C., Liu W., Jia Y., Sermanet P., Reed S., Anguelov D., Erhan D., Vanhoucke V., Rabinovich A., ((Going deeper with convolutions)) 2014
dc.source.bibliographicCitationJ. Deng, W. Dong, R. Socher, L.- J. Li, Kai Li and Li Fei-Fei ((ImageNet: A large-scale hierarchical image database)). 2009. Conference on computer vision
dc.source.bibliographicCitationHyndman, Rob J.; Koehler, Anne B. (2006). .Another look at measures of forecast accuracy”. International Journal of Forecasting.
dc.source.bibliographicCitationJaccard index, https://www.researchgate.net/publication/239604848 The Probabilistic Basis of Jaccard%23/05/2022.
dc.source.bibliographicCitationTom Fawcett, An introduction to ROC analysis, Institute for the Study of Learning and Expertise, (2005) https://people.inf.elte.hu/kiss/11dwhdm/roc.pdf23/05/2022.
dc.source.bibliographicCitationPowers David, Evaluation: From Precision, Recall and FFactor to ROC, Informedness, Markedness & Correlation 2007, https://web.archive.org/web/20191114213255/https://www.flinders.edu.au/science engineering/fms/School- CSEM/publications/tech repsresearch artfcts/TRRA 2007.pdf 19/11/2022.
dc.source.bibliographicCitationInformation Retrieval, https://en.wikipedia.org/w/index.php?title=Information retrievaldirection=next&19/11/2022
dc.source.bibliographicCitationHastie, Trevor; Tibshirani, Robert; Friedman, Jerome (2009). The Elements of Statistical Learning.
dc.source.bibliographicCitationCross Entropy, The Mathematics of Information Coding, Extraction and Distribution, by George Cybenko, Dianne P. O’Leary, Jorma Rissanen, 1999.
dc.source.bibliographicCitationShapiro, L. G. & Stockman, G. C: C¸ omputer Vision”, page 137, 150. Prentice Hall, 2001.
dc.source.bibliographicCitationVaswani A., Shazeer N., Parmar N., Uskoreit J., Jones L., Gomez A., Lukasz K., Polosukhin I. ((Attention Is All You Need )) 31st Conference on Neural Information Processing Systems. 2017. Long Beach, CA, USA.
dc.source.bibliographicCitationDosovitskiy A., Beyer L., Kolesnikov A., Weissenborn D., Zhai X, Unterthiner T., Dehghani M., Minderer M., Heigold G., Gelly S., Uszkoreit J., Houlsby N. (( Image is Worth 16 × 16 Words: Transformers for Image Recognition at Scale)) . 2021. Google Research, Brain team.
dc.source.bibliographicCitationHe K., Zhang X., Ren S., Sun J. (( Deep Residual Learning fro image Recognition )) . 2015. Microsoft Research.
dc.source.bibliographicCitationLui Z., Mao H., Wu C.-Y., Feichtenhofer C., Darrell T., Xie S. ((A ConvNet for the 2020s )). 2022. Facebook AI Research
dc.source.bibliographicCitationUlrich M., Follmann P., Neudeck J. ((A comparison of shape-based matching with deep-learning-based object detection)) Technisches Messen. 2019. DOI 10.1515/teme- 2019-0076.
dc.source.bibliographicCitationRen, Shaoqing He, Kaiming Girshick, Ross Sun, Jian. ((Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks)) IEEE Transactions on Pattern Analysis and Machine Intelligence. 2015. DOI 10.1109/TPAMI.2016.2577031.
dc.source.bibliographicCitationDetection Evaluation, https://cocodataset.org/home, 19/11/2022.
dc.source.bibliographicCitationSandler, Mark and Howard, Andrew and Zhu, Menglong and Zhmoginov, Andrey and Chen, Liang-Chieh. ((MobileNetV2: Inverted Residuals and Linear Bottlenecks)), Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2018.
dc.source.bibliographicCitationMudumbi, Terry Bian, Naizheng Zhang, Yiyi Hazoume, Florian. ((An Approach Combined the Faster RCNN and Mobilenet for Logo Detection)), Journal of Physics: Conference Series. 2019. 10.1088/1742-6596/1284/1/012072.
dc.source.bibliographicCitationSrivastava, Animesh and Dalvi, Anuj and Britto, Cyrus and Rai, Harshit and Shelke, Kavita ((Explicit Content Detection using Faster R-CNN and SSD MobileNet v2.)) 2020. Int. Res. J. Eng. Technol, 7, 5572–5577.
dc.source.bibliographicCitationTan L, Huangfu T, Wu L, Chen W. ((Comparison of YOLO v3, Faster R-CNN, and SSD for Real-Time Pill Identification)) Research Square. 2021. DOI: 10.21203/rs.3.rs- 668895/v1.
dc.source.bibliographicCitationAhmed, Khaled R. ((Smart Pothole Detection Using Deep Learning Based on Dilated Convolution)) Sensors. 2021. DOI: 10.3390/s21248406.
dc.source.bibliographicCitationGirshick, Ross. ((Fast R-CNN)). Proceedings of the IEEE international conference on computer vision. 2015. p. 1440-1448.
dc.source.bibliographicCitationKarim, Shahid Zhang, Ye Yin, Shoulin Bibi, Irfana Brohi, Ali. ((A brief review and challenges of object detection in optical remote sensing imagery. Multiagent and Grid Systems)) 2020. DOI: 10.3233/MGS-200330.
dc.source.bibliographicCitationShort-Term Load Forecasting based on ResNet and LSTM - Scientific Figure on ResearchGate. Available from: https://www.researchgate.net/figure/The-structure-of- ResNet-12 fig1 329954455 [accessed 23 Nov, 2022]
dc.source.bibliographicCitationEveringham, M., Eslami, S., Gool, L. V., Williams, C., Winn, J., Zisserman, A. ((The pascal visual object classes challenge: A retrospective )) IJCV. 2015.
dc.source.bibliographicCitationRussakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., et al. ((ImageNet large scale visual recognition challenge)) IJCV. 2015.
dc.source.bibliographicCitationLin, T., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll´ar, P., Zitnick, L. ((Microsoft COCO: Common objects in context)) In ECCV. 2015.
dc.source.bibliographicCitationZhou, B., Lapedriza, A., Khosla, A., Oliva, A., Torralba, A. ((Places: A 10 million image database for scene recognition)) IEEE Transactions on Pattern Analysis and Machine Intelligence. 2017.
dc.source.bibliographicCitationKuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., PontTuset, J., et al. ((The open images dataset v4: Unified image classi- fication, object detection, and visual relationship detection at scale)) arXiv:1811.00982. 2018.
dc.source.bibliographicCitationLi, Y., Xie, S., Chen, X., Dollar, P., He, K., Girshick, R. ((Benchmarking detection transfer learning with vision transformers)) arXiv preprint arXiv:2111.11429. 2021.
dc.source.bibliographicCitationLiu, L., Ouyang, W., Wang, X. et al. ((Deep Learning for Generic Object Detection: A Survey)) Int J Comput Vis. 2020. https://doi.org/10.1007/s11263-019-01247-4
dc.source.bibliographicCitationChuanqi T., Fuchun S., Tao K.,Wenchang Z., Chao Y. Chunfang L.(( A Survey on Deep Transfer Learning)) The 27th International Conference on Artificial Neural Networks. 2018
dc.source.bibliographicCitationZe L., Yutong L., Yue C., Han H., Yixuan W., Zheng Z., Stephen L. Baining G.((Swin Transformer: Hierarchical Vision Transformer using Shifted Windows)) 2021
dc.source.instnameinstname:Universidad del Rosario
dc.source.reponamereponame:Repositorio Institucional EdocURspa
dc.subjectClasificación y separación de tornillos
dc.subjectDetección de imágenes
dc.subjectClasificación de imágenes
dc.subjectRedes neuronales
dc.subjectAprendijaze profundo
dc.subjectAutomatización de procesos
dc.subject.keywordBolt classification and Screws
dc.subject.keywordImage classification
dc.subject.keywordImage detection
dc.subject.keywordNeuronal networks
dc.subject.keywordDeep learning
dc.subject.keywordProcess automation
dc.titleTornidentifier: identificación y clasificación automática de tornillos con redes neuronales profundas
dc.title.TranslatedTitleTornidentifier: automatic screw identification and classification with deep neuronal networks
dc.typebachelorThesis
dc.type.documentTesis
dc.type.spaTesis
local.department.reportEscuela de Ciencias e Ingeniería
Archivos
Bloque original
Mostrando1 - 1 de 1
Miniatura
Nombre:
Tornidentifier-identificacion-y-clasificacion-automatica-de-tornillos-con-redes-neuronales-profundas.pdf
Tamaño:
5.47 MB
Formato:
Adobe Portable Document Format
Descripción:
Descargar
Colecciones
Maestría en Matemáticas Aplicadas y Ciencias de la Computación