Streaming Machine Learning and Online Active Learning for Automated Visual Inspection.

Rožanec, Jože M.; Trajkova, Elena; Dam, Paulien; Fortuna, Blaž; Mladenić, Dunja

doi:10.1016/j.ifacol.2022.04.206

“…For a detailed review of active learning, the reader may be interested in some high-quality surveys of this domain. In particular, the works by Settles [95] and Rožanec et al [87] can serve as an introduction to this topic. Furthermore, the surveys by Fu et al [38] and Kumar et al [58] provide an overview of querying strategies in a batch setting; the survey by Lughofer [69] give an overview of active learning in online settings, and the study by Ren et al [85] describes active learning approaches related to deep learning models.…”

Section: Realizing Human-machine Collaboration In Visual Inspectionmentioning

confidence: 99%

Human in the AI Loop via xAI and Active Learning for Visual Inspection

Rožanec,

Montini,

Cutrona

et al. 2023

Artificial Intelligence in Manufacturing

0

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Industrial revolutions have historically disrupted manufacturing by introducing automation into production. Increasing automation reshapes the role of the human worker. Advances in robotics and artificial intelligence open new frontiers of human-machine collaboration. Such collaboration can be realized considering two sub-fields of artificial intelligence: active learning and explainable artificial intelligence. Active learning aims to devise strategies that help obtain data that allows machine learning algorithms to learn better. On the other hand, explainable artificial intelligence aims to make the machine learning models intelligible to the human person. The present work first describes Industry 5.0, human-machine collaboration, and state-of-the-art regarding quality inspection, emphasizing visual inspection. Then it outlines how human-machine collaboration could be realized and enhanced in visual inspection. Finally, some of the results obtained in the EU H2020 STAR project regarding visual inspection are shared, considering artificial intelligence, human-digital twins, and cybersecurity.

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“…For a detailed review of active learning, the reader may be interested in some high-quality surveys of this domain. In particular, the works by Settles [95] and Rožanec et al [87] can serve as an introduction to this topic. Furthermore, the surveys by Fu et al [38] and Kumar et al [58] provide an overview of querying strategies in a batch setting; the survey by Lughofer [69] give an overview of active learning in online settings, and the study by Ren et al [85] describes active learning approaches related to deep learning models.…”

Section: Realizing Human-machine Collaboration In Visual Inspectionmentioning

confidence: 99%

“…Nevertheless, the effort saved depends on the pool of unlabeled images, the use case, and the active learning strategy. Data augmentation techniques at an image or embedding level have increased the models' discriminative performance [89]. Furthermore, complementing images with anomaly maps as input to supervised classification models has substantially improved discriminative capabilities [88].…”

Section: Machine Learning and Visual Inspectionmentioning

confidence: 99%

Artificial Intelligence in Manufacturing

Chen

¹

,

Wang

²

2022

Artificial Intelligence and Lean Manufacturing

6

0

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use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this book are included in the book's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the book's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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“…Nevertheless, the effort saved depends on the pool of unlabeled images, the use case, and the active learning strategy. Data augmentation techniques at an image or embedding level have increased the models' discriminative performance [89]. Furthermore, complementing images with anomaly maps as input to supervised classification models has substantially improved discriminative capabilities [88].…”

Section: Machine Learning and Visual Inspectionmentioning

confidence: 99%

Artificial Intelligence in Manufacturing

Harish,

Krishnaveni,

Mansurali

2021

Reinventing Manufacturing and Business Processes Through Artificial Intelligence

0

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use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this book are included in the book's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the book's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Streaming Machine Learning and Online Active Learning for Automated Visual Inspection.

Cited by 20 publications

References 31 publications

Human in the AI Loop via xAI and Active Learning for Visual Inspection

Human in the AI Loop via xAI and Active Learning for Visual Inspection

Artificial Intelligence in Manufacturing

Artificial Intelligence in Manufacturing

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