The use of ground control points (GCPs) for georeferencing is the most common strategy in unmanned aerial vehicle (UAV) photogrammetry, but at the same time their collection represents the most time-consuming and expensive part of UAV campaigns. Recently, deep learning has been rapidly developed in the field of small object detection. In this letter, to automatically extract coordinates information of ground control points (GCPs) by detecting GCP-markers in UAV images, we propose a solution that uses a deep learning-based architecture, YOLOv5-OBB, combined with a confidence threshold filtering algorithm and an optimal ranking algorithm. We applied our proposed method to a dataset collected by DJI Phantom 4 Pro drone and obtained good detection performance with the mean Average Precision (AP) of 0.832 and the highest AP of 0.982 for the cross-type GCP-markers. The proposed method can be a promising tool for future implementation of the end-to-end aerial triangulation process.

翻译：使用地面控制点（GCP）进行地理配准是无人机摄影测量中最常用的策略，但与此同时，采集这些控制点是无人机测量任务中耗时最长、成本最高的环节。近年来，深度学习在弱小目标检测领域取得了快速发展。为通过检测无人机图像中的GCP标志自动提取地面控制点（GCP）的坐标信息，本文提出了一种基于深度学习架构YOLOv5-OBB的解决方案，并融合了置信度阈值过滤算法与最优排序算法。我们将所提方法应用于大疆Phantom 4 Pro无人机采集的数据集，针对十字形GCP标志取得了良好的检测性能，其平均精度（AP）为0.832，最高AP达到0.982。该方法有望成为未来实现端到端空中三角测量流程的有力工具。