Preprocessing of UAV Thermal Video for Solar Panel Fault Detection

Abstract

Previous reports have shown the validity of using thermal images taken by UAVs, or aerial drones, for solar panel fault detection. In order to use AI algorithms for automatic fault detection, it is necessary to convert the thermal video to still images. When converting the video to still images, if the images can be stitched together to create one large image, it will be easier to determine the exact location of the faulty panel. But since the glass surface of the solar panel is smooth and flat, light is reflected off the surface of the glass and the temperature reading of the thermal camera changes depending on the angle of incidence. Since standard stitching algorithms do not account for images which change color depending on angle, the connected thermal image does not show temperature readings correctly. In this research, a stitching algorithm to solve the above problem is proposed. By using this algorithm, a preprocessing method for preparing a thermal image file suited for AI fault detection is proposed.