| Citation: | SHI Guang, HE Fuqiang, SHI Hongyan, et al. Review of deep learning-based defect detection techniques for aero-engine blade[J]. Journal of Aerospace Power, 2026, 41（3）：20240411 doi: 10.13224/j.cnki.jasp.20240411
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A review of deep learning-based aero-engine blade defect detection technology was presented. Commencing from three core contents in total, including deep learning model and model evaluation index, blade type and blade defect type, and deep learning blade defect detection technology, it focused on deep learning blade defect detection technology. Furthermore, attention was paid to model, dataset, result evaluation, etc., and based on the dataset labeling requirements, the domestic and international deep learning blade defect detection techniques were reviewed by supervised learning, unsupervised learning and semi-supervised learning, the advantages and disadvantages, challenges and outlooks of blade defect detection under different deep learning methods were put forward. It was believed the lightweight model that can overcome dataset-related challenges could be used to build cost-effective, real-time blade defect detection systems in industrial sites.
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