TY - GEN
T1 - FCU2C:Engine Blade Crack Detection Method Based on U2-Net and Canny
AU - Zhang, Anqi
AU - Zhao, Yue
AU - Chu, Hongwei
AU - Feng, Yujin
AU - Fu, Li
AU - Liu, Yuwei
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - The aircraft engine blades working in extreme conditions may develop cracks, posing serious safety risks. The performance of deep learning methods is poor in engine blade crack detection due to lim-ited number of crack surface images and small sizes. Regarding this, this paper introduces a small-sample engine blade surface crack detection method FCU2C, which integrates U2-Net and Canny. Firstly, the U2-Net is enhanced with the integration of the coordinate attention mech-anism for better detection precision, called CA-U2Net. Secondly, CA-U2Net is used as feed-forward of FCU2C to improve noise resistance and robustness. The Canny operator segments the feed-forward outputs. Finally, the proposed method is trained on dataset CFD and transferred to predict aircraft engine blade cracks. Experiments demonstrate that our technique enhances the F1-score by 55.65%, 28.98%, and 9.94% when benchmarked against deeplabV3+, U-Net, and U2-Net, respectively.
AB - The aircraft engine blades working in extreme conditions may develop cracks, posing serious safety risks. The performance of deep learning methods is poor in engine blade crack detection due to lim-ited number of crack surface images and small sizes. Regarding this, this paper introduces a small-sample engine blade surface crack detection method FCU2C, which integrates U2-Net and Canny. Firstly, the U2-Net is enhanced with the integration of the coordinate attention mech-anism for better detection precision, called CA-U2Net. Secondly, CA-U2Net is used as feed-forward of FCU2C to improve noise resistance and robustness. The Canny operator segments the feed-forward outputs. Finally, the proposed method is trained on dataset CFD and transferred to predict aircraft engine blade cracks. Experiments demonstrate that our technique enhances the F1-score by 55.65%, 28.98%, and 9.94% when benchmarked against deeplabV3+, U-Net, and U2-Net, respectively.
KW - CA-U2Net
KW - Canny with Wiener filter
KW - Engine blade crack detection
UR - https://www.scopus.com/pages/publications/105006569767
U2 - 10.1007/978-981-96-2228-3_39
DO - 10.1007/978-981-96-2228-3_39
M3 - 会议稿件
AN - SCOPUS:105006569767
SN - 9789819622276
T3 - Lecture Notes in Electrical Engineering
SP - 416
EP - 426
BT - Advances in Guidance, Navigation and Control - Proceedings of 2024 International Conference on Guidance, Navigation and Control Volume 8
A2 - Yan, Liang
A2 - Duan, Haibin
A2 - Deng, Yimin
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Guidance, Navigation and Control, ICGNC 2024
Y2 - 9 August 2024 through 11 August 2024
ER -