  Utility-scale solar arrays require specialized inspection methods for
detecting faulty panels. Photovoltaic (PV) panel faults caused by weather,
ground leakage, circuit issues, temperature, environment, age, and other damage
can take many forms but often symptomatically exhibit temperature differences.
Included is a mini survey to review these common faults and PV array fault
detection approaches. Among these, infrared thermography cameras are a powerful
tool for improving solar panel inspection in the field. These can be combined
with other technologies, including image processing and machine learning. This
position paper examines several computer vision algorithms that automate
thermal anomaly detection in infrared imagery. We demonstrate our infrared
thermography data collection approach, the PV thermal imagery benchmark
dataset, and the measured performance of image processing transformations,
including the Hough Transform for PV segmentation. The results of this
implementation are presented with a discussion of future work.
