Advancements in aircraft controller design, paired with increasingly flexible aircraft concepts, create the need for the development of novel (smart) adaptive sensing methods suitable for aeroelastic state estimation. A potentially universal and noninvasive approach is visual tracking. However, many tracking methods require manual selection of initial marker locations at the start of a tracking sequence. This study aims to address the gap by investigating a robust machine learning approach for unsupervised automatic labeling of visual markers. The method uses fast DBSCAN and adaptive image segmentation pipeline with hue-saturation-value color filter to extract and label the marker centers under the presence of marker failure. In a comparative study, the DBSCAN clustering performance is assessed against an alternative clustering method, the disjoint-set data structure. The segmentation-clustering pipeline with DBSCAN is capable of running real-time at 250 FPS on a single camera image sequence with a resolution of 1088×600 pixels. To increase robustness against noise, a novel formulation (the inverse DBSCAN, ${\mathrm{DBSCAN}}^{-1}$) is introduced. This approach is validated on an experimental dataset collected from camera observations of a flexible wing undergoing gust excitations in a wind tunnel, demonstrating an excellent match with the ground truth obtained with a laser vibrometer measurement system.

飞行器控制器设计的进步，以及日益灵活的飞行器概念，导致需要开发适用于气动弹性状态估计的新型（智能）自适应传感方法。潜在的普遍且非侵入性的方法是视觉跟踪。但是，许多跟踪方法要求在跟踪序列开始时手动选择初始标记位置。这项研究旨在通过研究一种健壮的机器学习方法来解决这一空白，该方法用于无监督地自动标记视觉标记。该方法使用快速DBSCAN和带有色相饱和度值滤色器的自适应图像分割管线来提取和标记出现标记故障的标记中心。在一项比较研究中，DBSCAN群集性能是根据另一种群集方法进行评估的，不相交的数据结构。使用DBSCAN的分段聚类管线能够在单个相机图像序列上以250 FPS的速度实时运行，分辨率为1088×600像素。为了提高抗噪声的鲁棒性，采用了一种新颖的格式（逆DBSCAN，${\mathrm{数据库扫描}}^{-\mathrm{1个}}$）的介绍。该方法在从相机观察到的在风洞中经历阵风激发的柔性机翼的相机观测数据收集的实验数据集上得到了验证，这证明了与激光测振仪测量系统获得的地面真相的极佳匹配。