The Effect of Annotation Quality on Wear Semantic Segmentation by CNN

In this work, we investigate the impact of annotation quality and domain expertise on the performance of Convolutional Neural Networks (CNNs) for semantic segmentation of wear on titanium nitride (TiN) and titanium carbonitride (TiCN) coated end mills. Using an innovative measurement system and cust...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2024-08, Vol.24 (15), p.4777
Main Authors: Bilal, Mühenad, Podishetti, Ranadheer, Koval, Leonid, Gaafar, Mahmoud A, Grossmann, Daniel, Bregulla, Markus
Format: Article
Language:English
Subjects:
Artificial intelligence
Datasets
domain expertise
image annotation
machining tools
Manufacturing
Medical imaging equipment
Neural networks
Nitrides
semantic segmentation
U-Net model
wear detection
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Summary:In this work, we investigate the impact of annotation quality and domain expertise on the performance of Convolutional Neural Networks (CNNs) for semantic segmentation of wear on titanium nitride (TiN) and titanium carbonitride (TiCN) coated end mills. Using an innovative measurement system and customized CNN architecture, we found that domain expertise significantly affects model performance. Annotator 1 achieved maximum mIoU scores of 0.8153 for abnormal wear and 0.7120 for normal wear on TiN datasets, whereas Annotator 3 with the lowest expertise achieved significantly lower scores. Sensitivity to annotation inconsistencies and model hyperparameters were examined, revealing that models for TiCN datasets showed a higher coefficient of variation (CV) of 16.32% compared to 8.6% for TiN due to the subtle wear characteristics, highlighting the need for optimized annotation policies and high-quality images to improve wear segmentation.
ISSN:1424-8220
1424-8220
DOI:10.3390/s24154777