  To identify abnormal photovoltaic (PV) modules in large-scale PV plants
economically, drone-mounted infrared (IR) cameras and automated video
processing algorithms are frequently used. While most related works focus on
the detection of abnormal modules, little has been done to automatically
localize those modules within the plant. In this work, we use incremental
structure-from-motion to automatically obtain geocoordinates of all PV modules
in a plant based on visual cues and the measured GPS trajectory of the drone.
In addition, we extract multiple IR images of each PV module. Using our method,
we successfully map 99.3 % of the 35084 modules in four large-scale and one
rooftop plant and extract over 2.2 million module images. As compared to our
previous work, extraction misses 18 times less modules (one in 140 modules as
compared to one in eight). Furthermore, two or three plant rows can be
processed simultaneously, increasing module throughput and reducing flight
duration by a factor of 2.1 and 3.7, respectively. Comparison with an accurate
orthophoto of one of the large-scale plants yields a root mean square error of
the estimated module geocoordinates of 5.87 m and a relative error within each
plant row of 0.22 m to 0.82 m. Finally, we use the module geocoordinates and
extracted IR images to visualize distributions of module temperatures and
anomaly predictions of a deep learning classifier on a map. While the
temperature distribution helps to identify disconnected strings, we also find
that its detection accuracy for module anomalies reaches, or even exceeds, that
of a deep learning classifier for seven out of ten common anomaly types. The
software is published at https://github.com/LukasBommes/PV-Hawk.
