UAV-Based Automatic Detection, Localization, and Cleaning of Bird Excrement on Solar Panels
Original version
IEEE Transactions on Systems, Man & Cybernetics. Systems. 2024, . https://doi.org/10.1109/TSMC.2024.3506533Abstract
Bird excrement deposited on solar panels can lead to hotspots, significantly reducing the efficiency of solar power plants. This paper presents a novel solution to this problem leveraging unmanned aerial vehicle (UAV) systems for the automated geolocation and removal of bird excrement across large-scale solar power facilities. First, a UAV executes a pre-defined flight path to capture sequential aerial images of the plant. These images are subsequently stitched to produce a high-definition orthomosaic of the entire facility. An advanced detection framework based on YOLOv7, enhanced with an attention module, is employed to accurately detect bird excrement by reducing background noise and highlighting key features. An additional prediction head is integrated to improve detection of smaller bird excrements. To compute precise geolocation of the detected excrement, the midpoint pixel coordinates of the excrement along with the azimuth angle and actual ground distance (AGD) relative to a ground control point (GCP) is used. The paper further proposes a cleaning technique that employs a traveling salesman problem (TSP) approximation algorithm to efficiently optimize flight path of the cleaning UAV. Experimental results indicate the system achieves an average detection precision (AP) of 93.91% and GPS coordinate accuracy with an average error of 0.149 meters, demonstrating the efficacy of the proposed method in both geolocation and removal of bird excrement from solar panels.