  The integrity of offshore wind turbines, pivotal for sustainable energy
generation, is often compromised by surface material defects. Despite the
availability of various detection techniques, limitations persist regarding
cost-effectiveness, efficiency, and applicability. Addressing these
shortcomings, this study introduces a novel approach leveraging an advanced
version of the YOLOv8 object detection model, supplemented with a Convolutional
Block Attention Module (CBAM) for improved feature recognition. The optimized
loss function further refines the learning process. Employing a dataset of
5,432 images from the Saemangeum offshore wind farm and a publicly available
dataset, our method underwent rigorous testing. The findings reveal a
substantial enhancement in defect detection stability, marking a significant
stride towards efficient turbine maintenance. This study's contributions
illuminate the path for future research, potentially revolutionizing
sustainable energy practices.
