融合热成像与可见光图像，提升风机叶片缺陷检测精度。
Thermal and RGB Images Work Better Together in Wind Turbine Damage Detection
- 通过坐标变换与特征匹配，融合无人机拍摄的热图与可见光图。
- YOLOv8检测模型准确率提升至95%，误报减少一半。
- 适合风电运维、智能巡检系统研发人员参考。
风力机叶片（WTBs）的检测对保障结构完整性和运行效率至关重要。传统检测方法危险且低效，因此采用无人机（UAV）进入难以到达区域并获取高分辨率图像成为新方案。本研究提出一种多光谱图像融合方法，通过空间坐标转换、关键点检测、二值描述符生成和加权图像叠加，将无人机采集的热成像与可见光图像融合。基于标注缺陷的基准数据集，评估了多种先进目标检测模型。结果表明，融合图像显著提升缺陷检测效率：YOLOv8模型的准确率从91%升至95%，精确率从89%升至94%，召回率从85%升至92%，F1分数从87%升至93%；误报数由6例降至3例，漏检数由5例降至2例。结果证明，热成像与可见光图像融合能有效增强风力机叶片缺陷检测能力，有助于提升维护水平与运行可靠性。
原文摘要 · Abstract (English)
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.
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