Automated defect detection approach for production processes of patterned steel plates using computer vision and deep learning

ilustraciones, graficas

Autores:: Kicker, Claudia

Tipo de recurso:

Fecha de publicación:: 2022

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: eng

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dc.title.eng.fl_str_mv	Automated defect detection approach for production processes of patterned steel plates using computer vision and deep learning
dc.title.translated.spa.fl_str_mv	Método para la detección automatizada de defectos en la producción de láminas de acero alfajor mediante visión artificial y aprendizaje profundo
title	Automated defect detection approach for production processes of patterned steel plates using computer vision and deep learning
spellingShingle	Automated defect detection approach for production processes of patterned steel plates using computer vision and deep learning 670 - Manufactura::672 - Hierro, acero, otras aleaciones ferrosas LAMINAS DE HIERRO Y ACERO Plates, iron and steel Deep learning Anomaly detection Autoencoders CNN Structural similarity Aprendizaje profundo Detección de anomalías Autocodificador Red neuronal convolucional Similitud estructural
title_short	Automated defect detection approach for production processes of patterned steel plates using computer vision and deep learning
title_full	Automated defect detection approach for production processes of patterned steel plates using computer vision and deep learning
title_fullStr	Automated defect detection approach for production processes of patterned steel plates using computer vision and deep learning
title_full_unstemmed	Automated defect detection approach for production processes of patterned steel plates using computer vision and deep learning
title_sort	Automated defect detection approach for production processes of patterned steel plates using computer vision and deep learning
dc.creator.fl_str_mv	Kicker, Claudia
dc.contributor.advisor.none.fl_str_mv	Prieto Ortiz, Flavio Augusto
dc.contributor.author.none.fl_str_mv	Kicker, Claudia
dc.subject.ddc.spa.fl_str_mv	670 - Manufactura::672 - Hierro, acero, otras aleaciones ferrosas
topic	670 - Manufactura::672 - Hierro, acero, otras aleaciones ferrosas LAMINAS DE HIERRO Y ACERO Plates, iron and steel Deep learning Anomaly detection Autoencoders CNN Structural similarity Aprendizaje profundo Detección de anomalías Autocodificador Red neuronal convolucional Similitud estructural
dc.subject.lemb.spa.fl_str_mv	LAMINAS DE HIERRO Y ACERO
dc.subject.lemb.eng.fl_str_mv	Plates, iron and steel
dc.subject.proposal.eng.fl_str_mv	Deep learning Anomaly detection Autoencoders CNN Structural similarity
dc.subject.proposal.spa.fl_str_mv	Aprendizaje profundo Detección de anomalías Autocodificador Red neuronal convolucional Similitud estructural
description	ilustraciones, graficas
publishDate	2022
dc.date.accessioned.none.fl_str_mv	2022-08-26T20:21:33Z
dc.date.available.none.fl_str_mv	2022-08-26T20:21:33Z
dc.date.issued.none.fl_str_mv	2022
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
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dc.type.content.spa.fl_str_mv	Text
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status_str	acceptedVersion
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dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/82144 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	eng
language	eng
dc.relation.indexed.spa.fl_str_mv	RedCol LaReferencia
dc.relation.references.spa.fl_str_mv	Atienza, Rowel: Advanced Deep Learning with Keras: Apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more. 1. Birmingham : Packt Publishing Limited, 2018. – ISBN 9781788624534 Bergmann, Paul ; Batzner, Kilian ; Fauser, Michael ; Sattlegger, David ; Steger, Carsten: The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection. In: International Journal of Computer Vision 129 (2021), No. 4, pp. 1038–1059. – ISSN 0920–5691 Bergmann, Paul ; Fauser, Michael ; Sattlegger, David ; Steger, Carsten: MVTec AD — A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2019. – ISBN 978–1–7281–3293–8, pp. 9584–9592 Bergmann, Paul ; Fauser, Michael ; Sattlegger, David ; Steger, Carsten: Uninformed Students: Student-Teacher Anomaly Detection With Discriminative Latent Embeddings. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2020. – ISBN 978–1–7281–7168–5, pp. 4182–4191 Bergmann, Paul ; Löwe, Sindy ; Fauser, Michael ; Sattlegger, David ; Steger, Carsten: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. (2019), pp. 372–380 Božič, Jakob ; Tabernik, Domen ; Skočaj , Danijel: End-to-end training of a twostage neural network for defect detection. In: 2020 25th International Conference on Pattern Recognition (ICPR), 2021, pp. 5619–5626 Breiman, Leo: Random Forests. In: Machine Learning 45 (2001), No. 1, pp. 5–32. – ISSN 08856125 Chandola, Varun ; Banerjee, Arindam ; Kumar, Vipin: Anomaly detection. In: ACM Computing Surveys 41 (2009), No. 3, pp. 1–58. – ISSN 0360–0300 Szegedy, Christian ; Ioffe, Sergey ; Vanhoucke, Vincent ; Alemi, Alexander A. : Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17) (2017), pp. 4279–4284 Dawson-Howe, Kenneth: A practical introduction to computer vision with OpenCV. Online-edition. Chichester, England : Wiley, 2014. – ISBN 9781118848784 Deutsches Institut für Normung e.V. : DIN EN 10363:2016 D: Continuously hot-rolled patterned steel strip and plate/sheet cut from wide strip - Tolerances on dimensions and shape. October 2016 Di, He ; Ke, Xu ; Peng, Zhou ; Dongdong, Zhou: Surface defect classification of steels with a new semi-supervised learning method. In: Optics and Lasers in Engineering 117 (2019), pp. 40–48. – ISSN 01438166 Di He ; Xu, Ke ; Wang, Dadong: Design of multi-scale receptive field convolutional neural network for surface inspection of hot rolled steels. In: Image and Vision Computing 89 (2019), pp. 12–20. – ISSN 02628856 Glassner, Andrew: Deep Learning: A Visual Approach. No Starch Press, 2021. – ISBN 9781718500730 Gonzalez, Rafael C. ; Woods, Richard E.: Digital image processing. Fourth, global edition. New York, New York : Pearson Education, 2018. – ISBN 9781292223070 Han, Lin ; Chen, Yuanhao ; Li, Jiaming ; Zhong, Bowei ; Lei, Yuzhu ; Sun, Minghui: Liver segmentation with 2.5D perpendicular UNets. In: Computers & Electrical Engineering 91 (2021), p. 107118. – ISSN 00457906 Hanbay, Kazım ; Talu, Muhammed F. ; Özgüven , Ömer F.: Fabric defect detection systems and methods—A systematic literature review. In: Optik 127 (2016), No. 24, pp. 11960–11973. – ISSN 00304026 He, Yu ; Song, Kechen ; Meng, Qinggang ; Yan, Yunhui: An End-to-End Steel Surface Defect Detection Approach via Fusing Multiple Hierarchical Features. In: IEEE Transactions on Instrumentation and Measurement 69 (2020), No. 4, pp. 1493–1504. – ISSN 0018–9456 Huang, Yibin ; Qiu, Congying ; Wang, Xiaonan ; Wang, Shijun ; Yuan, Kui: A Compact Convolutional Neural Network for Surface Defect Inspection. In: Sensors (Basel, Switzerland) 20 (2020), No. 7 Huang, Zheng ; Wu, Jiajun ; Xie, Feng: Automatic surface defect segmentation for hot-rolled steel strip using depth-wise separable U-shape network. In: Materials Letters 301 (2021), p. 130271. – ISSN 0167577X Lappas, Demetris ; Argyriou, Vasileios ; Makris, Dimitrios: Fourier Transformation Autoencoders for Anomaly Detection. In: ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2021. – ISBN 978–1–7281–7605–5, pp. 1475–1479 Li, Yundong ; Zhao, Weigang ; Pan, Jiahao: Deformable Patterned Fabric Defect Detection With Fisher Criterion-Based Deep Learning. In: IEEE Transactions on Automation Science and Engineering 14 (2017), No. 2, pp. 1256–1264. – ISSN 1545– 5955 Li, Yuyuan ; Zhang, Dong ; Lee, Dah-Jye: Automatic fabric defect detection with a wide-and-compact network. In: Neurocomputing 329 (2019), pp. 329–338. – ISSN 09252312 Lin, Hui ; Li, Bin ; Wang, Xinggang ; Shu, Yufeng ; Niu, Shuanglong: Automated defect inspection of LED chip using deep convolutional neural network. In: Journal of Intelligent Manufacturing 30 (2019), No. 6, pp. 2525–2534. – ISSN 0956–5515 Liu, Jie ; Song, Kechen ; Feng, Mingzheng ; Yan, Yunhui ; Tu, Zhibiao ; Zhu, Liu: Semi-supervised anomaly detection with dual prototypes autoencoder for industrial surface inspection. In: Optics and Lasers in Engineering 136 (2021), p. 106324. – ISSN 01438166 Liu, Yang ; Xu, Ke ; Xu, Jinwu: Periodic Surface Defect Detection in Steel Plates Based on Deep Learning. In: Applied Sciences 9 (2019), No. 15, p. 3127 Luo, Qiwu ; Liu, Kexin ; Su, Jiaojiao ; Yang, Chunhua ; Gui, Weihua ; Liu, Li ; Silven, Olli: Waterdrop Removal From Hot-Rolled Steel Strip Surfaces Based on Progressive Recurrent Generative Adversarial Networks. In: IEEE Transactions on Instrumentation and Measurement 70 (2021), pp. 1–11. – ISSN 0018–9456 Mei, Shuang ; Wang, Yudan ; Wen, Guojun: Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model. In: Sensors (Basel, Switzerland) 18 (2018), No. 4 Mueller, John P. ; Massaron, Luca: Deep learning for dummies. Hoboken, NJ : John Wiley & Sons Inc, 2019 (For dummies). – ISBN 9781119543039 Napoletano, Paolo ; Piccoli, Flavio ; Schettini, Raimondo: Anomaly Detection in Nanofibrous Materials by CNN-Based Self-Similarity. In: Sensors (Basel, Switzerland) 18 (2018), No. 1 Napoletano, Paolo ; Piccoli, Flavio ; Schettini, Raimondo: Semi-supervised anomaly detection for visual quality inspection. In: Expert Systems with Applications 183 (2021), p. 115275. – ISSN 09574174 Neogi, Nirbhar ; Mohanta, Dusmanta K. ; Dutta, Pranab K.: Review of vision-based steel surface inspection systems. In: EURASIP Journal on Image and Video Processing 2014 (2014), No. 1 Ngan, Henry Y. ; Pang, Grantham K. ; Yung, Nelson H.: Automated fabric defect detection—A review. In: Image and Vision Computing 29 (2011), No. 7, pp. 442–458. – ISSN 02628856 Paulraj, M. P. ; Shukry, A. M. M. ; Yaacob, S. ; Adom, A. H. ; Krishnan, R. P.: Structural steel plate damage detection using DFT spectral energy and artificial neural network. In: 2010 6th International Colloquium on Signal Processing & its Applications, IEEE, 2010. – ISBN 978–1–4244–7121–8, pp. 1–6 Planche, Benjamin: Hands-On Computer Vision with TensorFlow 2: Leverage deep learning to create powerful image processing apps with TensorFlow 2.0 and Keras. 1. Birmingham : Packt Publishing Limited, 2019. – ISBN 9781788839266 Racki, Domen ; Tomazevic, Dejan ; Skocaj, Danijel: A Compact Convolutional Neural Network for Textured Surface Anomaly Detection. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), IEEE, 32018. – ISBN 978– 1–5386–4886–5, pp. 1331–1339 Redmon, Joseph ; Divvala, Santosh ; Girshick, Ross ; Farhadi, Ali: You Only Look Once: Unified, Real-Time Object Detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2016 Ren, Ruoxu ; Hung, Terence ; Tan, Kay C.: A Generic Deep-Learning-Based Approach for Automated Surface Inspection. In: IEEE transactions on cybernetics 48 (2018), No. 3, pp. 929–940 Ronneberger, Olaf ; Fischer, Philipp ; Brox, Thomas: U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Navab, Nassir (Ed. ) ; Hornegger, Joachim (Ed. ) ; Wells, William M. (Ed. ) ; Frangi, Alejandro F. (Ed. ): Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. Cham : Springer International Publishing, 2015. – ISBN 978–3–319–24574–4, pp. 234–241 Schlegl, Thomas ; Seeböck , Philipp ; Waldstein, Sebastian M. ; Langs, Georg ; Schmidt-Erfurth, Ursula: f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks. In: Medical image analysis 54 (2019), pp. 30–44 Schlegl, Thomas ; Seeböck, Philipp ; Waldstein, Sebastian M. ; Schmidt-Erfurth, Ursula ; Langs, Georg: Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery, Springer, Cham, 2017, pp. 146–157 See, Judi E. ; Drury, Colin G. ; Speed, Ann ; Williams, Allison ; Khalandi, Negar: The Role of Visual Inspection in the 21 st Century. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting 61 (2017), No. 1, pp. 262–266. – ISSN 2169–5067 Song, Kechen ; Yan, Yunhui: A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects. In: Applied Surface Science 285 (2013), pp. 858–864. – ISSN 01694332 Soulami, Khaoula B. ; Kaabouch, Naima ; Saidi, Mohamed N. ; Tamtaoui, Ahmed: Breast cancer: One-stage automated detection, segmentation, and classification of digital mammograms using UNet model based-semantic segmentation. In: Biomedical Signal Processing and Control 66 (2021), p. 102481. – ISSN 17468094 Steinwart, Ingo ; Christmann, Andreas: Support vector machines. New York, NY : Springer, 2008 (Information science and statistics). – ISBN 978–0–387–77241–7 Tabernik, Domen ; Šela, Samo ; Skvarč, Jure ; Skočaj , Danijel: Segmentation- based deep-learning approach for surface-defect detection. In: Journal of Intelligent Manufacturing 31 (2020), No. 3, pp. 759–776. – ISSN 0956–5515 Tsai, Du-Ming ; Jen, Po-Hao: Autoencoder-based anomaly detection for surface defect inspection. In: Advanced Engineering Informatics 48 (2021), p. 101272. – ISSN 14740346 Wang, Lu ; Zhang, Dongkai ; Guo, Jiahao ; Han, Yuexing: Image Anomaly Detection Using Normal Data Only by Latent Space Resampling. In: Applied Sciences 10 (2020), No. 23, p. 8660 Wang, Tian ; Chen, Yang ; Qiao, Meina ; Snoussi, Hichem: A fast and robust convolutional neural network-based defect detection model in product quality control. 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dc.format.extent.spa.fl_str_mv	xvi, 48 páginas
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Bogotá - Ingeniería - Maestría en Ingeniería - Ingeniería Mecánica
dc.publisher.department.spa.fl_str_mv	Departamento de Ingeniería Mecánica y Mecatrónica
dc.publisher.faculty.spa.fl_str_mv	Facultad de Ingeniería
dc.publisher.place.spa.fl_str_mv	Bogotá, Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Bogotá
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spelling	Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Prieto Ortiz, Flavio Augustoe5e0629d29d9b754bf18e0f0017122daKicker, Claudiaa10bd2d05a40a3c07954a622425f77572022-08-26T20:21:33Z2022-08-26T20:21:33Z2022https://repositorio.unal.edu.co/handle/unal/82144Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, graficasAnomaly 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.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)MaestríaMaestría en Ingeniería - Ingeniería MecánicaAutomation, Control and Mechatronicsxvi, 48 páginasapplication/pdfengUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería MecánicaDepartamento de Ingeniería Mecánica y MecatrónicaFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá670 - Manufactura::672 - Hierro, acero, otras aleaciones ferrosasLAMINAS DE HIERRO Y ACEROPlates, iron and steelDeep learningAnomaly detectionAutoencodersCNNStructural similarityAprendizaje profundoDetección de anomalíasAutocodificadorRed neuronal convolucionalSimilitud estructuralAutomated defect detection approach for production processes of patterned steel plates using computer vision and deep learningMétodo para la detección automatizada de defectos en la producción de láminas de acero alfajor mediante visión artificial y aprendizaje profundoTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMRedColLaReferenciaAtienza, Rowel: Advanced Deep Learning with Keras: Apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more. 1. Birmingham : Packt Publishing Limited, 2018. – ISBN 9781788624534Bergmann, Paul ; Batzner, Kilian ; Fauser, Michael ; Sattlegger, David ; Steger, Carsten: The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection. In: International Journal of Computer Vision 129 (2021), No. 4, pp. 1038–1059. – ISSN 0920–5691Bergmann, Paul ; Fauser, Michael ; Sattlegger, David ; Steger, Carsten: MVTec AD — A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2019. – ISBN 978–1–7281–3293–8, pp. 9584–9592Bergmann, Paul ; Fauser, Michael ; Sattlegger, David ; Steger, Carsten: Uninformed Students: Student-Teacher Anomaly Detection With Discriminative Latent Embeddings. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2020. – ISBN 978–1–7281–7168–5, pp. 4182–4191Bergmann, Paul ; Löwe, Sindy ; Fauser, Michael ; Sattlegger, David ; Steger, Carsten: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. (2019), pp. 372–380Božič, Jakob ; Tabernik, Domen ; Skočaj , Danijel: End-to-end training of a twostage neural network for defect detection. In: 2020 25th International Conference on Pattern Recognition (ICPR), 2021, pp. 5619–5626Breiman, Leo: Random Forests. In: Machine Learning 45 (2001), No. 1, pp. 5–32. – ISSN 08856125Chandola, Varun ; Banerjee, Arindam ; Kumar, Vipin: Anomaly detection. In: ACM Computing Surveys 41 (2009), No. 3, pp. 1–58. – ISSN 0360–0300Szegedy, Christian ; Ioffe, Sergey ; Vanhoucke, Vincent ; Alemi, Alexander A. : Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. 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Automated defect detection approach for production processes of patterned steel plates using computer vision and deep learning

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