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

arXiv: https://arxiv.org/abs/2512.06504 · HTML: https://mlsanity.com/paper/2512.06504/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 45 references that are in the map.
- First posted on arXiv 6 Dec 2025.

## Builds on (most cited first, 4 of 4)

- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [Computer Vision Tool for Detection, Mapping and Fault Classification of PV Modules in Aerial IR Videos](https://mlsanity.com/paper/2106.07314/) (2021)
- [Thermal and Visual Tracking of Photovoltaic Plants for Autonomous UAV inspection](https://mlsanity.com/paper/2202.01003/) (2022)
- [Thermal and RGB Images Work Better Together in Wind Turbine Damage Detection](https://mlsanity.com/paper/2412.04114/) (2024)

## Abstract (arXiv)

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%.