Complex image background segmentation for cable force estimation of urban bridges with drone‐captured video and deep learning

Drone-assisted structural health monitoring has aroused extensive attention due to its high mobility and low cost. However, drone motion and complex image backgrounds severely impede its application in the cable force measurement of urban bridges. To fill this research gap, this paper proposed a deep learning-based complex background segmentation approach for cable force estimation of urban bridges from the drone-captured video. The main contribution of this article includes two aspects: (1) A pre-trained fully convolutional network (FCN) model was first adopted to identify bridge cables from drone-captured video and further to extract sub-pixel-level displacement using line segment detection (LSD) algorithm, and (2) an empirical mode decomposition algorithm was employed for extracting the vibration signal of bridge cables by eliminating the effect of drone motion on measured dynamic displacement. Finally, natural frequencies of bridge cables were obtained by performing Fourier analysis on extracted cable vibration and further adopted for cable force estimation. The effectiveness and robustness of the proposed method have been successfully verified by field testing of an urban cable-stayed footbridge. Estimated cable forces using the proposed method are consistent with the traditional contact-type measurements and design values, demonstrating the potential of this method for applying into rapid cable force estimation of numerous urban bridges.