A Deep Learning Based Detection of Bird Droppings and Cleaning Method for Photovoltaic Solar Panels

The accumulation of bird droppings on photovoltaic (PV) farms reduces power generation efficiency and necessitates manual cleaning on a regular basis, which is a challenge in large power plants. To solve this problem, this paper proposes an automatic Unmanned Aerial Vehicle (UAV) based bird droppings detection, localization, and cleaning method on large PV power plant. An automated flight route is first created, and use an UAV to fly over the solar farm to capture images of the solar panels. The captured images are then stitched together to create a high-resolution orthomosaic image of the solar farm, which enables to precisely locate the bird droppings on the solar farm. An improved YOLOv7-based model is proposed to detect the bird droppings because they are quite small in comparison to the stitched image. Then, using the ground sample distance, we calculate the distance between each of the bird droppings and the drone's takeoff point, which is used to clean the bird droppings from the solar panel. Last, the proposed model is verified by high-resolution orthomosaic images and the experimental outcomes unequivocally show that it is successful for detecting and cleaning of bird droppings on PV farms.