Interactive Image Segmentation Using Superpixels and Deep Metric Learning for Tool Condition Monitoring

Lutz B, Janisch L, Kißkalt D, Regulin D, Franke J (2023)

Publication Type: Conference contribution

Publication year: 2023

Journal

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

Abstract

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.

Authors with CRIS profile

Involved external institutions

LMU Klinikum Germany (DE)

How to cite

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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