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<p>Corrosion of turbine blades is a critical reliability and safety concern in turboshaft engines, yet automated detection remains difficult due to the very limited availability of annotated data. This work develops a data-efficient deep-learning approach capable of identifying corrosion under such constraints. A lightweight synthetic data pipeline is introduced to generate simplified but realistic corrosion patches that are blended into clean blade images, forming a large training and validation set. Real corrosion samples are reserved exclusively for testing to avoid data leakage. A dual-head deep learning model architecture is proposed, combining a classification branch for corrosion detection with a segmentation branch that improves feature learning from synthetic patterns. Experiments on blades from 13 engines, comprising 39 source images and 7020 subimages, show that the proposed model achieves a classification accuracy of 90% despite extreme data scarcity. These results demonstrate that even simple synthetic augmentation, when combined with a joint classification-segmentation design, substantially improves generalisation and provides interpretable outputs suitable for turboshaft blade inspection workflows.</p>

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Segmentation-assisted corrosion classification on turboshaft blades using synthetic training data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月27日

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  • Katerina Kobrlova,
  • Jiri Pavlas,
  • Matous Cejnek,
  • Milan Dvorak,
  • Milan Ruzicka,
  • Svatomir Slavik,
  • Jan Vrba,
  • Cyril Oswald

摘要

Corrosion of turbine blades is a critical reliability and safety concern in turboshaft engines, yet automated detection remains difficult due to the very limited availability of annotated data. This work develops a data-efficient deep-learning approach capable of identifying corrosion under such constraints. A lightweight synthetic data pipeline is introduced to generate simplified but realistic corrosion patches that are blended into clean blade images, forming a large training and validation set. Real corrosion samples are reserved exclusively for testing to avoid data leakage. A dual-head deep learning model architecture is proposed, combining a classification branch for corrosion detection with a segmentation branch that improves feature learning from synthetic patterns. Experiments on blades from 13 engines, comprising 39 source images and 7020 subimages, show that the proposed model achieves a classification accuracy of 90% despite extreme data scarcity. These results demonstrate that even simple synthetic augmentation, when combined with a joint classification-segmentation design, substantially improves generalisation and provides interpretable outputs suitable for turboshaft blade inspection workflows.