UAV-Based Automatic Detection, Localization, and Cleaning of Bird Excrement on Solar Panels
提出用无人机系统自动检测并清除太阳能电站电池板上的鸟粪，通过YOLOv7检测、GPS定位和路径优化清洁，实验显示检测精度93.91%，定位误差0.149米。
Bird excrement deposited on solar panels can lead to hotspots, significantly reducing the efficiency of solar power plants. This article 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 predefined 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. This article 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 m, demonstrating the efficacy of the proposed method in both geolocation and removal of bird excrement from solar panels.