Deep Learning Strategies for Industrial Surface Defect Detection Systems Martin, Dominik Heinzel, Simon Kunze Von Bischhoffshausen, Johannes Kühl, Niklas 2021-12-24T17:27:08Z 2021-12-24T17:27:08Z 2022-01-04
dc.description.abstract Deep learning methods have proven to outperform traditional computer vision methods in various areas of image processing. However, the application of deep learning in industrial surface defect detection systems is challenging due to the insufficient amount of training data, the expensive data generation process, the small size, and the rare occurrence of surface defects. From literature and a polymer products manufacturing use case, we identify design requirements which reflect the aforementioned challenges. Addressing these, we conceptualize design principles and features informed by deep learning research. Finally, we instantiate and evaluate the gained design knowledge in the form of actionable guidelines and strategies based on an industrial surface defect detection use case. This article, therefore, contributes to academia as well as practice by (1) systematically identifying challenges for the industrial application of deep learning-based surface defect detection, (2) strategies to overcome these, and (3) an experimental case study assessing the strategies' applicability and usefulness.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2022.146
dc.identifier.isbn 978-0-9981331-5-7
dc.language.iso eng
dc.relation.ispartof Proceedings of the 55th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.subject Big Data and Analytics: Pathways to Maturity
dc.subject deep learning
dc.subject design science research
dc.subject industry 4.0
dc.subject surface defect detection
dc.title Deep Learning Strategies for Industrial Surface Defect Detection Systems
dc.type.dcmi text
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