A two-stage surface defect segmentation method for wind turbine blades based on Deeplabv3+

Abstract

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.