2018 · 31 citations · 7 references
ComputingAerospace EngineeringComputer ScienceComputer VisionMachine LearningMachine VisionDigital Image ProcessingFault DetectionObject RecognitionSolar Energy SystemsSolar Photovoltaic SystemsSolar Thermal SystemsDeep Learning DetectionFault Detection Using Deep LearningMean Average PrecisionSingle ShotThermal Fault DetectionThermographic InspectionThermographic MonitoringUnmanned Aerial Vehicle Sensing
In this work, we compare several methods for detecting failed solar panels, and found that drone detection is the most efficient. We show thermographic images captured by drone and explain various types of panel failures. An examination by deep learning revealed that single shot detection (SSD) is an effective method of automatic detection. We ran tests with the SSD model using 4-MW solar plant data and achieved a mean average precision of 49.11%.
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Automatic solar photovoltaic panel detection in satellite imagery
Jordan M. Malof, Rui Hou, Leslie M. Collins et al. · 2015 · 99 citations