Wanrun Li1,2,3,*, Wenhai Zhao1, Tongtong Wang1, Yongfeng Du1,2,3
Structural Durability & Health Monitoring, Vol.18, No.5, pp. 553-575, 2024, DOI:10.32604/sdhm.2024.050751
Abstract The accumulation of defects on wind turbine blade surfaces can lead to irreversible damage, impacting the aerodynamic performance of the blades. To address the challenge of detecting and quantifying surface defects on wind turbine blades, a blade surface defect detection and quantification method based on an improved Deeplabv3+ deep learning model is proposed. Firstly, an improved method for wind turbine blade surface defect detection, utilizing Mobilenetv2 as the backbone feature extraction network, is proposed based on an original Deeplabv3+ deep learning model to address the issue of limited robustness. Secondly, through integrating the concept of… More >
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