A real-time material breakage detection for offshore wind turbines based
  on improved neural network algorithm
Yantong Liu
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.