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January 1, 2026International Journal of Computational Science and Engineering

Deep transfer learning method for aero-engine blade encoded character detection and recognition based on YOLO-GE

Yifan Xiao Shuxia Wang Guanfeng Wang Shixin Wu Mingrui Zhang Jianghong Li

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Key Points

The aim is to enhance character detection and recognition for aero-engine blade labeling using a deep learning approach.
Utilized a deep transfer learning framework based on YOLO-GE.
Focused on character detection and recognition specific to aero-engine blades.
Implemented machine learning techniques to optimize accuracy.
Achieved significant improvement in detection accuracy compared to previous methods.
Demonstrated robustness in handling various blade characters.
Reduced processing time for character recognition tasks.

Implication

Deep transfer learning demonstrates effective character detection for aero-engine blades, indicating improved recognition accuracy.

Abstract

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Cite This Study

Xiao et al. (2026) studied this question.

synapsesocial.com/papers/6a34de7065a5b0777af2dd45 https://doi.org/10.1504/ijcse.2026.10079196

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