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Training a Computer Vision Model for Commercial Bakeries with Primarily Synthetic Images

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dc.contributor.authorSchmitt, Thomas H.
dc.contributor.authorBundscherer, Maximilian
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.accessioned2024-10-21T18:24:16Z
dc.date.available2024-10-21T18:24:16Z
dc.date.issued2024
dc.description.abstractIn the food industry, reprocessing returned products is a vital step to increase resource efficiency. [SBB23] presented an AI application that automates the tracking of returned bread buns. We extend their work by creating an expanded dataset comprising 2432 images and a wider range of baked goods. To increase model robustness, we use generative models pix2pix and CycleGAN to create synthetic images. We train state-of-the-art object detection models YOLOv9 and YOLOv8 on our detection task. Our overall best-performing model achieved an average precision AP 0.5 of 90.3% on our test set.en
dc.identifier.doi10.18420/inf2024_150
dc.identifier.isbn978-3-88579-746-3
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/45126
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.subjectSchmitt
dc.subjectThomas H.
dc.subjectBundscherer
dc.subjectMaximilian
dc.subjectBocklet
dc.subjectTobias
dc.titleTraining a Computer Vision Model for Commercial Bakeries with Primarily Synthetic Imagesen
dc.typeText/Conference Paper
gi.citation.endPage1740
gi.citation.publisherPlaceBonn
gi.citation.startPage1731
gi.conference.date24.-26. September 2024
gi.conference.locationWiesbaden
gi.conference.sessiontitleKünstliche Intelligenz im Mittelstand / KI-KMU2024

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