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

Lutz, Benjamin; Janisch, Lucas; Kißkalt, Dominik; Regulin, Daniel; Franke, Jörg

doi:10.1016/j.procir.2023.06.079

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“…According to different application scenarios, TCM methods can be categorized into direct and indirect methods [5]. Direct methods mainly utilize optical sensors, cameras, and other specialized equipment to directly observe and quantify changes in the material composition or geometric profile of the cutting tool [6]. You et al [7].…”

Section: Introductionmentioning

confidence: 99%

Vibration Signal-Based Tool Condition Monitoring Using Regularized Sensor Data Modeling and Model Frequency Analysis

Liu,

Lang,

Gui

et al. 2024

IEEE Trans. Instrum. Meas.

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Section: Introductionmentioning

confidence: 99%

Vibration Signal-Based Tool Condition Monitoring Using Regularized Sensor Data Modeling and Model Frequency Analysis

Liu,

Lang,

Gui

et al. 2024

IEEE Trans. Instrum. Meas.

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A Weakly Supervised Semi-Automatic Image Labeling Approach for Deformable Linear Objects

Caporali

Pantano

Janisch

et al. 2023

IEEE Robot. Autom. Lett.

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The presence of Deformable Linear Objects (DLOs) such as wires, cables or ropes in our everyday life is massive. However, the applicability of robotic solutions to DLOs is still marginal due to the many challenges involved in their perception. In this letter, a methodology to generate datasets from a mixture of synthetic and real samples for the training of DLOs segmentation approaches is thus presented. The method is composed of two steps. First, key-points along a real-world DLO are labeled by employing a VR tracker operated by a user. Second, synthetic and real-world datasets are mixed for the training of semantic and instance segmentation deep learning algorithms to study the benefit of real-world data in DLOs segmentation. To validate this method a user study and a parameter study are conducted. The results show that the VR tracker labeling is usable as other labeling techniques but reduces the number of clicks. Moreover, mixing real-world and synthetic DLOs data can improve the IoU score of a semantic segmentation algorithm by circa 5%. Therefore, this work demonstrates that labeling real-world data via a VR tracker can be done quickly and, if the real-world data are mixed with synthetic data, the performances of segmentation algorithms for DLOs can be improved.

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

Cited by 2 publications

References 12 publications

Vibration Signal-Based Tool Condition Monitoring Using Regularized Sensor Data Modeling and Model Frequency Analysis

Vibration Signal-Based Tool Condition Monitoring Using Regularized Sensor Data Modeling and Model Frequency Analysis

A Weakly Supervised Semi-Automatic Image Labeling Approach for Deformable Linear Objects

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