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Segmenting Wood Rot using Computer Vision Models

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dc.contributor.authorKammerbauer, Roland
dc.contributor.authorSchmitt, Thomas H.
dc.contributor.authorBocklet, Tobias
dc.contributor.editorKlein, Maike
dc.contributor.editorKrupka, Daniel
dc.contributor.editorWinter, Cornelia
dc.contributor.editorGergeleit, Martin
dc.contributor.editorMartin, Ludger
dc.date.accessioned2024-10-21T18:24:15Z
dc.date.available2024-10-21T18:24:15Z
dc.date.issued2024
dc.description.abstractIn the woodworking industry, a huge amount of effort has to be invested into the initial quality assessment of the raw material. In this study, we present an AI model to detect, quantify, and localize defects on wooden logs. This model aims to both automate the quality control process and provide a more consistent and reliable quality assessment. For this purpose, a dataset of 1424 sample images of wood logs is created. A total of 5 annotators possessing different levels of expertise are involved in dataset creation. An inter-annotator agreement analysis is conducted to analyze the impact of expertise on the annotation task and to highlight subjective differences in annotator judgment. We explore, train, and fine-tune the state-of-the-art InternImage and ONE-PEACE architectures for semantic segmentation. The best model created achieves an average IoU of 0.71 and shows detection and quantification capabilities close to the human annotators.en
dc.identifier.doi10.18420/inf2024_149
dc.identifier.isbn978-3-88579-746-3
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/45124
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofINFORMATIK 2024
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-352
dc.subjectmachine learning
dc.subjectimage segmentation
dc.subjectsemantic segmentation
dc.subjectInternImage
dc.subjectONE-PEACE
dc.subjectlumbering
dc.subjectindustrial quality control
dc.subjectindustrial automation
dc.titleSegmenting Wood Rot using Computer Vision Modelsen
dc.typeText/Conference Paper
gi.citation.endPage1730
gi.citation.publisherPlaceBonn
gi.citation.startPage1717
gi.conference.date24.-26. September 2024
gi.conference.locationWiesbaden
gi.conference.sessiontitleKünstliche Intelligenz im Mittelstand / KI-KMU2024

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