Autonomous damage assessment of structural columns using low-cost micro
  aerial vehicles and multi-view computer vision
Sina Tavasoli Xiao Pan T. Y. Yang Saudah Gazi Mohsen Azimi
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.