In this study, we present an approach to exploit the keypoint R-convolutional neural network (CNN) structure for automated detection of ground control points (GCPs) in images acquired by optical sensors. Our deep learning methodology employs fine-tuning on three distinct datasets, thereby enabling the specific addressing of inherent variations in GCP types, including shape, size, and color. Our approach uses an end-to-end artificial intelligence (AI)-based approach that does not require any postprocessing of the data or the use of specific GCPs. Performance metrics are evaluated to compare the AI results with manually labeled data, coordinates obtained by an ellipse fitting algorithm for circular targets, and a semi-automatic optical flow algorithm for arbitrary targets (e.g., crosses). The study highlights the importance of a dataset-specific fine-tuning and data augmentation strategy to enhance the model's accuracy in locating GCPs. The results demonstrate the effectiveness of the R-CNN keypoint approach in successfully identifying GCPs with subpixel accuracy compared to manual labeling in all datasets. Our AI-based approach outperformed semi-automatic methods based on computer vision algorithms, identifying GCPs in images taken under adverse conditions and in cases where GCPs were partially obscured. Furthermore, the results demonstrate robust performance in a transferability test, that is, identifying GCPs in different images obtained at different locations and with different cameras than those used for training.