Automatic detection of aerial survey ground control points based on Yolov5-OBB
This addresses the time-consuming and expensive collection of GCPs for UAV photogrammetry, though it is incremental as it adapts existing deep learning methods to a specific domain.
The paper tackled the problem of automatically detecting ground control points (GCPs) in UAV images to reduce time and cost in photogrammetry, achieving a mean Average Precision of 0.832 and a highest AP of 0.982 for cross-type markers.
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.