Method of UAV Inspection of Photovoltaic Modules Using Thermal and RGB Data Fusion

TL;DR

This paper presents an advanced multi-modal UAV inspection system for photovoltaic modules that fuses thermal and RGB data, improving anomaly detection accuracy, reducing false positives, and optimizing data transmission for better operational efficiency.

Contribution

The study introduces a novel integrated framework combining thermal and RGB data fusion, adaptive re-acquisition, and geospatial deduplication for automated PV inspection.

Findings

Achieved 0.903 mAP@0.5 on PVF-10 benchmark, outperforming single-modality methods.

Reduced duplicate false positives by 15-20% through geospatial clustering.

Decreased airborne data transmission by 60-70% with relevance-only telemetry.

Abstract

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%.