CoastCor-Net: A Wind Turbine Blade Defect Detection Network for Coastal Environments

Coastal wind turbines operate under severe salt spray, high humidity, and wind-driven erosion, which accelerate coating degradation and corrosion-induced cracking. In such environments, corrosion defects exhibit blurred boundaries, weak textures, and significant scale variations, challenging object detectors in small-target localization and precise boundary regression. To address these limitations, this study proposes CoastCor-Net, an enhanced YOLOv11-based framework that improves spatial–semantic alignment, boundary representation, and channel–spatial dependency modeling. The architecture integrates three complementary modules to enhance boundary sensitivity, spatial–semantic consistency, and cross-channel interaction: a Decoding-Driven Enhancement Block, a Complementary Feature Alignment Module, and a Channel-Transposed Coordinate Attention module. Extensive experiments on the Wind Turbine Blade Damage Dataset show that CoastCor-Net achieves 84.7% mAP@0.5 and 54.1% mAP@0.5:0.95, surpassing YOLOv13n by 3.2 percentage points in mAP@0.5 and improving AP_damage by 5.2 percentage points. The framework also demonstrates strong robustness under composite coastal perturbations. These findings highlight the practical effectiveness of structured multi-level feature enhancement for reliable and high-precision blade inspection in complex coastal environments.