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.
@article{arxiv.2307.13765,
title = {A real-time material breakage detection for offshore wind turbines based on improved neural network algorithm},
author = {Yantong Liu},
journal= {arXiv preprint arXiv:2307.13765},
year = {2023}
}
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arXiv admin note: text overlap with arXiv:2306.16019