Inspection systems utilizing unmanned aerial vehicles (UAVs) equipped with thermal cameras are increasingly popular for the maintenance of photovoltaic (PV) power plants. However, automation of the inspection task is a challenging problem as it requires precise navigation to capture images from optimal distances and viewing angles. This paper presents a novel localization pipeline that directly integrates PV module detection with UAV navigation, allowing precise positioning during inspection. The detections are used to identify the power plant structures in the image. These are associated with the power plant model and used to infer the UAV position relative to the inspected PV installation. We define visually recognizable anchor points for the initial association and use object tracking to discern global associations. Additionally, we present three different methods for visual segmentation of PV modules and evaluate their performance in relation to the proposed localization pipeline. The presented methods were verified and evaluated using custom aerial inspection data sets, demonstrating their robustness and applicability for real-time navigation. Additionally, we evaluate the influence of the power plant model precision on the localization methods.

翻译：利用配备热成像相机的无人机进行巡检的系统在光伏电站维护中日益普及。然而，巡检任务的自动化是一个具有挑战性的问题，因为它需要精确导航以从最佳距离和视角捕获图像。本文提出了一种新颖的定位流程，将光伏组件检测与无人机导航直接集成，实现了巡检过程中的精确定位。检测结果用于识别图像中的电站结构，这些结构与电站模型相关联，并用于推断无人机相对于被检光伏装置的位置。我们为初始关联定义了视觉可识别的锚点，并利用目标跟踪来辨识全局关联。此外，我们提出了三种不同的光伏组件视觉分割方法，并评估了它们在所提出定位流程中的性能。所提方法通过定制航空巡检数据集进行了验证与评估，证明了其对于实时导航的鲁棒性与适用性。同时，我们还评估了电站模型精度对定位方法的影响。