Automated and Lightweight Feature Detection and Matching towards Real-time SHM of Large Structures

An automatic lightweight feature detection algorithm is developed to perform real-time structural health monitoring (SHM) of large structures. The algorithm works on the specified region of interest (ROI) and applies canny edge detection with k-means clustering for identifying the displaced pixel location in an image sequence. The location of detected edges (white pixels) in the selected ROI is first validated and then given as input to the k-means clustering algorithm for centroid calculation. The pixel movement tracing method is validated by image simulation, indoor digital micrometer experiment and then an outdoor field experiment on wind turbine. The image simulation experiment was performed to generate sample data and ground truth values. In this experiment, the algorithm was able to detect the defined pixel translations. With this validation, other two experiments were conducted. The indoor experiment was implemented for experimental verification where it successfully identifies the moving bar's 20mm displacement. Likewise, it also accurately measures the natural frequency of the tower of a utility-scale wind turbine. Hence, the algorithm was built on parallel processing with multi-ROI selection to optimize the space and time complexity for real-time vibration analysis. The present study proclaims that the developed algorithm can be used to perform real-time SHM of large-scale structures.