Lutz B, Janisch L, Kißkalt D, Regulin D, Franke J (2023)
Publication Type: Conference contribution
Publication year: 2023
Publisher: Elsevier B.V.
Book Volume: 118
Pages Range: 459-464
Conference Proceedings Title: Procedia CIRP
Event location: Naples, ITA
DOI: 10.1016/j.procir.2023.06.079
The optical measurement of tool wear is commonly used to monitor machining processes. Recently, deep learning methods, in particular, have been applied for the identification and segmentation of the different wear defects. For such approaches, annotated training data is required, which includes the acquisition of cutting tool images and their pixel-wise annotations. As this process is time-consuming, we propose a novel interactive image annotation method for tool condition monitoring. This is achieved by combining superpixel segmentation, deep metric learning, and human corrections. By using the interactive image annotation, manual annotation effort is reduced and mask accuracy is improved.
APA:
Lutz, B., Janisch, L., Kißkalt, D., Regulin, D., & Franke, J. (2023). Interactive Image Segmentation Using Superpixels and Deep Metric Learning for Tool Condition Monitoring. In Roberto Teti, Doriana D'Addona (Eds.), Procedia CIRP (pp. 459-464). Naples, ITA: Elsevier B.V..
MLA:
Lutz, Benjamin, et al. "Interactive Image Segmentation Using Superpixels and Deep Metric Learning for Tool Condition Monitoring." Proceedings of the 16th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME 2022, Naples, ITA Ed. Roberto Teti, Doriana D'Addona, Elsevier B.V., 2023. 459-464.
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