SAIT: Harnessing Sparse Annotations and Intrinsic Tasks for Semisupervised Aeroengine Defect Segmentation-Reference-Cited by-同舟云学术

SAIT: Harnessing Sparse Annotations and Intrinsic Tasks for Semisupervised Aeroengine Defect Segmentation

Published:2024-08 Issue:8 Volume:20 Page:10463-10472
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Qi Haochen¹^ORCID,Kong Xiangwei¹^ORCID,Liu Zhitong¹^ORCID,Gu Jianyi¹^ORCID,Cheng Liu¹^ORCID

Affiliation:

1. School of Mechanical Engineering and Automation, Key Laboratory of Vibration and Control of Aero-Propulsion Systems, Ministry of Education of China, Northeastern University, Shenyang, China

Funder

National Key Research and Development Program of China

State Ministry of Science and Technology Innovation Fund of China

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/9424/10623532/10528905.pdf?arnumber=10528905

Reference26 articles.

1. Surface Defect Detection of Aeroengine Blades Based on Cross-Layer Semantic Guidance

2. Global Prior Transformer Network in Intelligent Borescope Inspection for Surface Damage Detection of Aero-Engine Blade

3. Steel Surface Defect Recognition: A Survey

4. Deep learning-based defects detection of certain aero-engine blades and vanes with DDSC-YOLOv5s

5. Damages Detection of Aeroengine Blades via Deep Learning Algorithms

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