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The integrity of offshore wind turbines, pivotal for sustainable energygeneration, is often compromised by surface material defects. Despite theavailability of various detection techniques, limitations persist regardingcost-effectiveness, efficiency, and applicability. Addressing theseshortcomings, this study introduces a novel approach leveraging an advancedversion of the YOLOv8 object detection model, supplemented with a ConvolutionalBlock Attention Module (CBAM) for improved feature recognition. The optimizedloss function further refines the learning process. Employing a dataset of5,432 images from the Saemangeum offshore wind farm and a publicly availabledataset, our method underwent rigorous testing. The findings reveal asubstantial enhancement in defect detection stability, marking a significantstride towards efficient turbine maintenance. This study's contributionsilluminate the path for future research, potentially revolutionizingsustainable energy practices.

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