无人机搭载多模态传感器，实现风机叶片毫米级近距检测
A UAV-Mounted Sensor Network for Close-Range Inspection of Wind Turbine Rotor Blades
- 无人机集成可见光、红外与自研3D扫描仪，共校准至统一坐标系
- 实验室验证了多传感器同步采集与初步点云重建效果
- 适合风电运维团队开展高精度缺陷自动识别与巡检
海上风力发电机叶片的检测对预测性维护至关重要，可提升效率并延长使用寿命。然而，由于地理位置偏远、结构尺寸大以及现有无人机兼容传感器系统的局限性，检测仍具挑战。现有方法虽能识别部分表面异常，但缺陷类型的可靠分类通常依赖人工，易出错。本文提出一种机载多模态传感器网络，集成工业级RGB相机、被动式热红外相机及自研3D扫描仪。所有传感器经共校准进入同一坐标系，实现几何、颜色与热成像数据的空间叠加。系统设计用于近距离作业，解决平台运动、大视场与毫米级测量精度三大传感难题。初步实验室结果表明，系统可实现多传感器同步采集与初始点云重建，为后续飞行检测试验奠定基础。
原文摘要 · Abstract (English)
Inspection of offshore wind turbine rotor blades is critical for predictive maintenance to maximise efficiency and extend operational lifetime. However, it remains a challenging task due to remote locations, large structural dimensions, and the limitations of current UAV-compatible sensor systems. While existing approaches can detect certain types of surface anomalies, reliable classification of defect types often remains a manual and error-prone process. This paper presents the design of a UAV-mounted multimodal sensor network combining an industrial RGB camera, a passive thermal infrared camera, and an in-house developed 3D scanner. All sensors are co-calibrated into a common coordinate frame, enabling spatial superimposition of geometric, colour, and thermal data. The system is designed to operate at close range, addressing three fundamental sensing challenges: platform motion, large field of view, and millimetre-level measurement accuracy. Preliminary laboratory results demonstrate synchronised multi-sensor acquisition and initial point cloud reconstructions, forming the basis for future airborne inspection trials.
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