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Connor Clarkson, Mike Edwards Orcid Logo, Xianghua Xie Orcid Logo

Companion Proceedings of the 2023 ACM SIGCHI Symposium on Engineering Interactive Computing Systems

Swansea University Authors: Connor Clarkson, Mike Edwards Orcid Logo, Xianghua Xie Orcid Logo

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DOI (Published version): 10.1145/3596454.3597185

Abstract

Defect detection in steel manufacturing has achieved state-of-the art results in both localisation and classification of various types of defects, however, this assumes very high-quality datasets that havebeen verified by domain experts. Labelling such data has become a time-consuming and interactio...

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Published in: Companion Proceedings of the 2023 ACM SIGCHI Symposium on Engineering Interactive Computing Systems
ISBN: 9798400702068
Published: New York, NY, USA ACM 2023
Online Access: http://dx.doi.org/10.1145/3596454.3597185
URI: https://cronfa.swan.ac.uk/Record/cronfa63777
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first_indexed 2023-07-05T08:48:08Z
last_indexed 2023-07-05T08:48:08Z
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recordtype SURis
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spelling v2 63777 2023-07-05 Active Anchors e1a00716a3866cd4d8bb0ade1bada119 Connor Clarkson Connor Clarkson true false 684864a1ce01c3d774e83ed55e41770e 0000-0003-3367-969X Mike Edwards Mike Edwards true false b334d40963c7a2f435f06d2c26c74e11 0000-0002-2701-8660 Xianghua Xie Xianghua Xie true false 2023-07-05 SCS Defect detection in steel manufacturing has achieved state-of-the art results in both localisation and classification of various types of defects, however, this assumes very high-quality datasets that havebeen verified by domain experts. Labelling such data has become a time-consuming and interaction-heavy task with a great amount of user effort, this is due to variability in the defect characteristics andcomposite nature. We propose a new acquisition function based on the similarity of defects for refining labels over time by showing the user only the most required to be labelled. We also explore different ways in which to feed these new refinements back into the model to utilize the new knowledge in an effortful way. We achieve this with a graphical interface that provides additional information to the domain expert as the data gets refined, allowing for decision-making with uncertain areas of the steel. Conference Paper/Proceeding/Abstract Companion Proceedings of the 2023 ACM SIGCHI Symposium on Engineering Interactive Computing Systems ACM New York, NY, USA 9798400702068 Human-Centered AI, Active Learning, Data Refinement, Interactive 27 6 2023 2023-06-27 10.1145/3596454.3597185 http://dx.doi.org/10.1145/3596454.3597185 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University EPSRC Centre for Doctoral Training in Enhancing Human Interactions and Collaborations with Data and Intelligence Driven Systems EP/S021892/1 2023-11-20T14:51:25.5027808 2023-07-05T09:35:26.2448012 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Connor Clarkson 1 Mike Edwards 0000-0003-3367-969X 2 Xianghua Xie 0000-0002-2701-8660 3
title Active Anchors
spellingShingle Active Anchors
Connor Clarkson
Mike Edwards
Xianghua Xie
title_short Active Anchors
title_full Active Anchors
title_fullStr Active Anchors
title_full_unstemmed Active Anchors
title_sort Active Anchors
author_id_str_mv e1a00716a3866cd4d8bb0ade1bada119
684864a1ce01c3d774e83ed55e41770e
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author_id_fullname_str_mv e1a00716a3866cd4d8bb0ade1bada119_***_Connor Clarkson
684864a1ce01c3d774e83ed55e41770e_***_Mike Edwards
b334d40963c7a2f435f06d2c26c74e11_***_Xianghua Xie
author Connor Clarkson
Mike Edwards
Xianghua Xie
author2 Connor Clarkson
Mike Edwards
Xianghua Xie
format Conference Paper/Proceeding/Abstract
container_title Companion Proceedings of the 2023 ACM SIGCHI Symposium on Engineering Interactive Computing Systems
publishDate 2023
institution Swansea University
isbn 9798400702068
doi_str_mv 10.1145/3596454.3597185
publisher ACM
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
url http://dx.doi.org/10.1145/3596454.3597185
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description Defect detection in steel manufacturing has achieved state-of-the art results in both localisation and classification of various types of defects, however, this assumes very high-quality datasets that havebeen verified by domain experts. Labelling such data has become a time-consuming and interaction-heavy task with a great amount of user effort, this is due to variability in the defect characteristics andcomposite nature. We propose a new acquisition function based on the similarity of defects for refining labels over time by showing the user only the most required to be labelled. We also explore different ways in which to feed these new refinements back into the model to utilize the new knowledge in an effortful way. We achieve this with a graphical interface that provides additional information to the domain expert as the data gets refined, allowing for decision-making with uncertain areas of the steel.
published_date 2023-06-27T14:51:26Z
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score 11.016235

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