Comparative Evaluation of Tracking Capability of Spatial Patterns on Defective Urban Solar Panels between Unmanned Aerial Vehicle Video Stream and Photomosaics

Abstract

Unmanned aerial vehicle (UAV) autopilot flight requires a specific altitude when inspecting defective urban solar panels to avoid obstacles such as high-rise buildings, trees, and telegraph poles. Therefore, autopilot-based thermal imaging has severe data redundancy because the nonsolar panel area occupies more than 99% of the ground target. We aim to explore the tracking capability of a UAV video stream for defective urban solar panels by comparing spatial and clustering patterns with autopilot-based photomosaics. The spatial patterns of distributions and clusters in defective solar panels have high similarity (80-100%) to those of autopilot-based photomosaics. The results of this study can serve as a valuable reference for video-stream-based thermal deficiency inspections of defective solar panels in urban areas.