Creating Digital Panoramic Photographs from Aerial Video of Large Scale Solar Photovoltaic Power Plants

Abstract

Previous reports have shown the validity of using thermal images taken by UAVs (Unmanned Aerial Vehicles), or aerial drones, for solar panel fault detection. In order to use AI (Artificial Intelligence) 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 created thermal image does not show temperature readings correctly for this reason. Furthermore, similar looking solar panels provide few feature points to distinguish between different images, which cause standard stitching algorithms to have a high stitching failure rate. In this research, a stitching algorithm to solve the above problem is proposed. By using the proposed algorithm, an aerial thermal video of photovoltaic power plants can be converted to a digital panoramic photograph, which can then be used for AI fault detection.