用多光谱融合的YOLO集成模型，提升风机部件缺陷检测精度
YOLO Ensemble for UAV-based Multispectral Defect Detection in Wind Turbine Components
- 集成通用YOLOv8与专用热成像模型，融合可见光与热力数据
- [email protected]达0.93，F1-score为0.90，优于单模型的0.91
- 适合风电巡检、工业视觉等需要多模态检测的场景
搭载先进传感器的无人机为风力发电厂（包括叶片、塔筒等关键部件）监测提供了新机遇。然而，可靠缺陷检测需高分辨率数据及高效处理多光谱图像的方法。本研究通过构建基于YOLO的深度学习模型集成系统，融合可见光与热成像通道，提升缺陷检测准确率。提出将通用型YOLOv8模型与专用热成像模型结合，并采用先进的边界框融合算法整合预测结果。实验表明，该方法在[email protected]上达到0.93，F1-score为0.90，优于单一YOLOv8模型的0.91。结果证明，结合多架构YOLO与融合多光谱数据，能更可靠地识别视觉与热学缺陷。
原文摘要 · Abstract (English)
Unmanned aerial vehicles (UAVs) equipped with advanced sensors have opened up new opportunities for monitoring wind power plants, including blades, towers, and other critical components. However, reliable defect detection requires high-resolution data and efficient methods to process multispectral imagery. In this research, we aim to enhance defect detection accuracy through the development of an ensemble of YOLO-based deep learning models that integrate both visible and thermal channels. We propose an ensemble approach that integrates a general-purpose YOLOv8 model with a specialized thermal model, using a sophisticated bounding box fusion algorithm to combine their predictions. Our experiments show this approach achieves a mean Average Precision ([email protected]) of 0.93 and an F1-score of 0.90, outperforming a standalone YOLOv8 model, which scored an [email protected] of 0.91. These findings demonstrate that combining multiple YOLO architectures with fused multispectral data provides a more reliable solution, improving the detection of both visual and thermal defects.
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