ABFormer: Acoustic Boundary-aware Transformer for Spar Adhesive-layer Segmentation in Wind Turbine Blade Ultrasonic D-scans.

The adhesive bonding quality between the spar cap and shear web is critical to the structural reliability of wind turbine blades. Phased array ultrasonic testing (PAUT) can non-destructively acquire volumetric echo data from adhesive bonding regions. Pixel-level segmentation of PAUT-derived ultrasonic D-scan images provides a direct way to identify defect-related echo regions. However, these D-scans exhibit weak defect boundaries and strong speckle noise interference. To address these challenges, this paper proposes a new method, ABFormer. In the encoder, the boundary-guided deformable routing attention (BDRA) module leverages acoustic boundary priors to guide deformable sampling and window-level top-k routing, enabling sparse cross-region interactions. The acoustic-aware local block dual attention (A-LBDA) module adopts a dual-branch structure to extract channel and acoustic structural features within local windows, enhancing fine-grained local feature representation. In the decoder, an adaptive-frequency dynamic decoder (AFDD) is proposed to preserve effective high-frequency information through frequency selection while reducing random speckle noise interference. Experiments on a self-constructed D-scan dataset from real manufacturing scenarios show that the proposed method outperforms mainstream segmentation methods, especially in detecting defective regions.