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The ArUco marker is one of the most popular squared fiducial markers using for precise location acquisition during autonomous unmanned aerial vehicle (UAV) landings. This paper presents a novel method to detect, recognize, and extract the location points of single ArUco marker based on convolutional neural networks (CNN). YOLOv3 and YOLOv4 networks are applied for end-to-end detection and recognition of ArUco markers under occlusion. A custom lightweight network is employed to increase the processing speed. The bounding box regression mechanism of the YOLO algorithm is modified to locate four corners of each ArUco marker and classify markers irrespective of the orientation. The deep-learning method achieves a high mean average precision exceeding 0.9 in the coverless test set and over 0.4 under corner coverage, whereas traditional algorithm fails under the occlusion condition. The custom lightweight network notably speeds up the prediction process with acceptable decline in performance. The proposed bounding box regression mechanism can locate marker corners with less than 3% average distance error for each corner without coverage and less than 8% average distance error under corner occlusion.