DF-DEIM: dual-focus DEIM with edge guidance and Adaptive Spatial Mixture of Experts for wind turbine blade surface defect detection

Surface defects on wind turbine blades often exhibit elongated morphologies and low-contrast boundaries, making accurate real-time detection difficult on resource-constrained edge devices. This paper proposes Dual-Focus DETR with Improved Matching (DF-DEIM), a lightweight framework integrating morphology-aware feature extraction, edge-guided fusion, and adaptive expert routing. The Dual-Focus Backbone Network (DF-Net) combines asymmetric cross-convolutions with standard convolutions to capture both elongated and clustered defects. The Edge-Guided Fusion Module (EGFM) introduces spatial-gradient priors into cross-level fusion to preserve weak and blurred boundaries. The Adaptive Spatial Mixture of Experts (AS-MoE) selectively activates multi-scale experts according to local feature complexity, improving computational allocation. On the ZCWD dataset, DF-DEIM achieves an mAP50 of 84.5%, outperforming the baseline by 4.5 percentage points, while maintaining 198.5 FPS. It also reaches 42.0 FPS on an RK3588 edge device. Compared with representative detectors, including YOLOv11n and RT-DETR, DF-DEIM provides a favorable balance between detection accuracy, computational efficiency, and edge-deployment capability for UAV-based wind turbine blade inspection.