Structural displacement monitoring using deep learning-based full field optical flow methods

Current vision-based displacement measurement methods have limitations such as being in need of manual targets and parameter adjustment, and significant user involvement to reach the desired result. This study proposes a novel structural displacement measurement method using deep learning-based full field optical flow methods. The performance of the proposed method is verified via a laboratory experiment conducted on a grandstand structure with a comparative study, where the same data samples are analysed with a commonly used vision-based method, and a displacement sensor measurement is used as the ground truth. Statistical analysis of the comparative results show that the proposed method gives higher accuracy than the traditional optical flow algorithm and shows consistent results in compliance with displacement sensor measurements. Image collection, tracking, and non-uniform sampling are investigated in the experimental data and suggestions are made to obtain more accurate displacement measurements. A field-validation on a footbridge showed that the measurement error induced by the camera motion is mitigated by a camera motion subtraction procedure. The proposed method has good potential to be applied by structural engineers, who have little or no experience in computer vision and image processing, to do vision-based displacement measurements.