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 APdamage 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.
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Jiawei Xiang
Xinyu Wan
Shoudong Ni
Coatings
Universiti Sains Malaysia
Nanjing Tech University
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Xiang et al. (Mon,) studied this question.
www.synapsesocial.com/papers/69ba434a4e9516ffd37a45ad — DOI: https://doi.org/10.3390/coatings16030373