Accurate Localization of Solar Modules in Utility-Scale Photovoltaic Plants Using Drone-Mounted Thermal Cameras

In the automated inspection of photovoltaic (PV) power plants, image acquisition is typically carried out using drones equipped with infrared cameras. However, each image usually covers only a small portion of the plant, and issues such as low resolution, poor contrast, and thermal noise make precise localization of PV modules challenging. To address these limitations, this paper proposes a precise PV module localization method. First, the open-source tool OpenDroneMap is employed to stitch and georeference the acquired images and to extract PV strings based on YOLOv8. Then we introduce the SAM-UDH method, which integrates the Segment Anything Model (SAM) with a unidirectional histogram. After background removal using SAM, a perspective transformation is applied for distortion correction, followed by unidirectional histogram analysis to extract module boundaries. Finally, inverse mapping is performed back to the stitched orthophoto to achieve accurate localization in complex environments. The proposed approach was validated on real infrared datasets covering 441 PV strings and 12,342 PV modules. Experimental results show that our method achieves an F1-score of 96.4% and an IoU of 93.0%, while maintaining both the standard deviation and confidence interval below 0.03, demonstrating superior accuracy and robustness.