The inspection of wind turbine blades (WTBs) is crucial for ensuring their structural integrity and operational efficiency. Traditional inspection methods can be dangerous and inefficient, prompting the use of unmanned aerial vehicles (UAVs) that access hard-to-reach areas and capture high-resolution imagery. In this study, we address the challenge of enhancing defect detection on WTBs by integrating thermal and RGB images obtained from UAVs. We propose a multispectral image composition method that combines thermal and RGB imagery through spatial coordinate transformation, key point detection, binary descriptor creation, and weighted image overlay. Using a benchmark dataset of WTB images annotated for defects, we evaluated several state-of-the-art object detection models. Our results show that composite images significantly improve defect detection efficiency. Specifically, the YOLOv8 model's accuracy increased from 91% to 95%, precision from 89% to 94%, recall from 85% to 92%, and F1-score from 87% to 93%. The number of false positives decreased from 6 to 3, and missed defects reduced from 5 to 2. These findings demonstrate that integrating thermal and RGB imagery enhances defect detection on WTBs, contributing to improved maintenance and reliability.

翻译：风力涡轮机叶片（WTBs）的检测对于确保其结构完整性与运行效率至关重要。传统检测方法存在危险性高且效率低下的问题，因此采用无人机（UAVs）进入难以接近的区域并采集高分辨率图像成为趋势。本研究针对如何通过融合无人机获取的热成像与RGB图像来提升WTBs缺陷检测能力这一挑战展开研究。我们提出了一种多光谱图像合成方法，该方法通过空间坐标变换、关键点检测、二进制描述子生成以及加权图像叠加，将热成像与RGB图像进行融合。基于已标注缺陷的WTBs基准图像数据集，我们对多种先进的目标检测模型进行了评估。结果表明，合成图像能显著提升缺陷检测效率。具体而言，YOLOv8模型的准确率从91%提升至95%，精确率从89%提升至94%，召回率从85%提升至92%，F1分数从87%提升至93%。误报数量从6个减少至3个，漏检缺陷从5个减少至2个。这些发现证明，融合热成像与RGB图像能够有效增强WTBs的缺陷检测能力，从而有助于提升维护水平与运行可靠性。