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.

中文翻译：

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基于YOLO算法的单个ArUco标记在遮挡下的角点定位与识别。

ArUco标记器是最受欢迎的平方基准标记器之一，可用于在自动无人飞行器（UAV）着陆期间进行精确的位置获取。本文提出了一种基于卷积神经网络（CNN）的检测，识别和提取单个ArUco标记的定位点的新方法。YOLOv3和YOLOv4网络用于遮挡下ArUco标记的端到端检测和识别。使用定制的轻量级网络来提高处理速度。修改了YOLO算法的包围盒回归机制，以定位每个ArUco标记的四个角，并对标记进行分类，而与方向无关。深度学习方法在无覆盖测试集中的平均平均精度达到了0.9以上，在角落覆盖范围内则达到了0.4以上，而传统算法在遮挡条件下会失败。自定义轻量级网络显着加快了预测过程，但性能却出现了可接受的下降。所提出的边界框回归机制可以定位标记角，每个角的平均距离误差小于3％，而没有覆盖，并且在角遮挡下，平均距离误差小于8％。