Exploratory Study of Drone Data Stabilization with Implications in Vibration-based Structural Health Monitoring

This exploratory study presents stabilization approaches of drone-based videos with implications in vibration-structural health monitoring applications. Unlike other standalone vision-based systems such as cameras placed on a tripod, drone drifting may occur due to airborne missions especially when operated indoors or in lower altitudes. It affects the captured frames and even the slightest movement of the drone camera will reduce the data accuracy. Post-processing methods using Computer Vision (CV) and signal-processing algorithms are used in this study to explore their effectiveness and accuracy in measuring the dynamic vibration of a structure. The object study is an aluminum bar subjected to two sinusoidal vibrations then recorded using a camera embedded on a quadcopter. After video acquisition, the post-processing is started by image enhancement and scaling procedures, followed by Scale Invariant Transform (SIFT) feature detection, extraction, and matching. The scaling factor is used to convert the image coordinate to an object coordinate system before computing the displacement of the object. Data stabilization techniques are implemented in this study, first is the background subtraction method to eliminate signal drifting and second is cleaning the data from any trends. The accuracy of the proposed framework is tested by comparing the structural dynamic responses from drone measurement to a reference sensor. The Autoregressive (AR) model generated the Power Spectral Density of the signal is also compared to the measurement from a reference camera. The results show the high accuracy of the proposed method which is up to 97.52% on the dynamic response with less effect on the signal PSD. Overall, the exploratory study obtains satisfactory results and provides a new alternative to an intelligent system in the structural health monitoring field.