Structural columns are the crucial load-carrying components of buildings and
bridges. Early detection of column damage is important for the assessment of
the residual performance and the prevention of system-level collapse. This
research proposes an innovative end-to-end micro aerial vehicles (MAVs)-based
approach to automatically scan and inspect columns. First, an MAV-based
automatic image collection method is proposed. The MAV is programmed to sense
the structural columns and their surrounding environment. During the
navigation, the MAV first detects and approaches the structural columns. Then,
it starts to collect image data at multiple viewpoints around every detected
column. Second, the collected images will be used to assess the damage types
and damage locations. Third, the damage state of the structural column will be
determined by fusing the evaluation outcomes from multiple camera views. In
this study, reinforced concrete (RC) columns are selected to demonstrate the
effectiveness of the approach. Experimental results indicate that the proposed
MAV-based inspection approach can effectively collect images from multiple
viewing angles, and accurately assess critical RC column damages. The approach
improves the level of autonomy during the inspection. In addition, the
evaluation outcomes are more comprehensive than the existing 2D vision methods.
The concept of the proposed inspection approach can be extended to other
structural columns such as bridge piers.

通过创新的无人机（MAVs）为基础的自动扫描和检查柱，本研究提出一种自动图像采集方法，利用收集的图像数据对结构柱的损坏类型和位置进行评估，并通过多个相机视角的综合评估结果确定结构柱的损害状态。实验结果表明，该方法能有效地从多个视角收集图像，并准确评估重要的钢筋混凝土柱损伤，提高了检查的自主性和评估结果的全面性。