ArticleScientific reports2025
A two-stage surface defect segmentation method for wind turbine blades based on Deeplabv3.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Cross-Sensor Consistency-Guided Dual-Spectrum Fusion for Offshore Wind Turbine Blade Defect Diagnosis and Risk Grading.Sensors (Basel, Switzerland) · 2026Article
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Authors and funding
6 authors.
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Abstract
A two-stage segmentation model based on improved Deeplabv3 + is proposed for segmenting Blade regions and defects in complex environments. Accurate segmentation is critical for timely maintenance and safe operation of wind turbines. The model consists of Blade-Deeplabv3 + and Defect-Deeplabv3+, collectively named BD-Deeplabv3+. In the first stage, Blade-Deeplabv3 + segments the Blade from the background using the Atrous Spatial Pyramid Pooling module to extract multi-scale features and suppress background interference. The resulting segmented Blade is then input to the second stage. In this stage, Defect-Deeplabv3 + extracts multi-scale features and refines boundaries of surface crack, hole, and spalling defects. DenseASPP replaces the original ASPP, employing densely connected dilated convolutions to enhance multi-scale feature fusion and improve semantic representation and boundary accuracy for minor defects. Experimental results show that the mean intersection over union for Blade segmentation reaches 98.97%, and for defect segmentation reaches 94.25%. Finally, Blade defect severity is quantified using the ratio of defect area to Blade area, enabling more reliable maintenance planning.
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