基于热成像与RGB数据融合的光伏板无人机检测方法
Method of UAV Inspection of Photovoltaic Modules Using Thermal and RGB Data Fusion
AI总结：
本研究提出了一种基于热成像与RGB数据融合的无人机光伏板检测方法，通过多模态系统实现自动化检测，提升电站安全性和效率。
摘要：
本研究旨在开发一种智能集成框架，用于自动化检测光伏（PV）基础设施，以解决传统方法的关键缺陷，包括热成像偏色、数据冗余和高通信带宽需求。本研究的目标是设计、开发并验证一个综合的多模态系统，实现从数据采集到生成可操作的地理定位维护警报的全流程自动化，从而提高电站的安全性和运营效率。所采用的方法涉及一种协同架构，该架构首先通过强制表示一致性学习热成像不变嵌入，然后通过门控机制将该嵌入与对比归一化的RGB流融合。此外，还补充了一个闭环自适应重新获取控制器，利用Rodrigues基的更新方法对模糊异常进行定向确认，并利用DBSCAN在海曾距离上对冗余警报进行聚类处理。结论是，本研究建立了一种新的主动光伏检测范式，所提出的系统在公开的PVF-10基准上实现了平均平均精度（mAP@0.5）为0.903，比单模态基线提高了12-15%。实地验证确认了系统的可靠性，实现了96%的召回率，而去重过程将因重复导致的假阳性减少了15-20%，而仅相关遥测数据减少了空中数据传输量60-70%。
英文摘要：
The subject of this research is the development of an intelligent, integrated framework for the automated inspection of photovoltaic (PV) infrastructure that addresses the critical shortcomings of conventional methods, including thermal palette bias, data redundancy, and high communication bandwidth requirements. The goal of this study is to design, develop, and validate a comprehensive, multi-modal system that fully automates the monitoring workflow, from data acquisition to the generation of actionable, geo-located maintenance alerts, thereby enhancing plant safety and operational efficiency. The methods employed involve a synergistic architecture that begins with a palette-invariant thermal embedding, learned by enforcing representational consistency, which is fused with a contrast-normalized RGB stream via a gated mechanism. This is supplemented by a closed-loop, adaptive re-acquisition controller that uses Rodrigues-based updates for targeted confirmation of ambiguous anomalies and a geospatial deduplication module that clusters redundant alerts using DBSCAN over the haversine distance. In conclusion, this study establishes a powerful new paradigm for proactive PV inspection, with the proposed system achieving a mean Average Precision (mAP@0.5) of 0.903 on the public PVF-10 benchmark, a significant 12-15% improvement over single-modality baselines. Field validation confirmed the system's readiness, achieving 96% recall, while the de-duplication process reduced duplicate-induced false positives by 15-20%, and relevance-only telemetry cut airborne data transmission by 60-70%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
