  Increasing deployment of photovoltaics (PV) plants demands for cheap and fast
inspection. A viable tool for this task is thermographic imaging by unmanned
aerial vehicles (UAV). In this work, we develop a computer vision tool for the
semi-automatic extraction of PV modules from thermographic UAV videos. We use
it to curate a dataset containing 4.3 million IR images of 107842 PV modules
from thermographic videos of seven different PV plants. To demonstrate its use
for automated PV plant inspection, we train a ResNet-50 to classify ten common
module anomalies with more than 90 % test accuracy. Experiments show that our
tool generalizes well to different PV plants. It successfully extracts PV
modules from 512 out of 561 plant rows. Failures are mostly due to an
inappropriate UAV trajectory and erroneous module segmentation. Including all
manual steps our tool enables inspection of 3.5 MW p to 9 MW p of PV
installations per day, potentially scaling to multi-gigawatt plants due to its
parallel nature. While we present an effective method for automated PV plant
inspection, we are also confident that our approach helps to meet the growing
demand for large thermographic datasets for machine learning tasks, such as
power prediction or unsupervised defect identification.
