###### Abstract  
To solve the problems of measurement errors led by mismatches of dense feature matching in machine vision structural deflection measurement, this paper proposes a dense feature extraction, matching, and dual-step mismatch-removal-based full-field structural dynamic deflection measurement method. First, the of dense feature detection and matching theory is introduced to extract the SIFT feature points on a structural surface in an image sequence and matched by FLANN to trace the structure movement, and the mechanisms and causes of mismatches are analyzed. Then, a dual-step mismatch removal method combining RANSAC and Structural Displacement Continuity Restriction (SDCR) is introduced to achieve full-field dynamic beam deflection measurement. The proposed method is validated through indoor cantilever beam experiments, and results show that the method can effectively eliminate a large number of SIFT feature mismatches (accounting for approximately 55% of the total matched feature points). The full-field dynamic displacement field of the beam can be measured with the correctly matched dense feature points by converting dense feature point displacements into continuous and uniform spatiotemporal deflections of the structure. A comparison with the GOM Correlate Professional DIC measurement system was conducted, and the maximum measurement error of the cantilever beam dynamic displacement of the proposed method is between 0.024 and 0.053 mm, the root mean squared error of displacement is approximately 0.01 mm, and the correlation coefficient between two deflection-time curves reaches 0.9964. The proposed algorithm is proven to be effective in full-field displacement measurement and has great potential in future structural health monitoring of bridges. machine vision; dense feature points; feature matching noise reduction; dynamic displacement measurement +
Footnote †: journal: Applied Sciences
Ye [9] reviewed the research and application of structural displacement detection methods by computer vision and pointed out that future research should focus on reducing the measurement error and improving the application efficiency. [PERSON] [10] proposed a machine-vision-based structural health monitoring method for bridges which detects and tracks the image sequence of the vehicles crossing the bridge and obtains the unit influence lines. [PERSON] [11] proposed a vision-based method for deflection measurement of structures by Digital Image Correlation methods. [PERSON] [12] analyzed the advantages and disadvantages of template recognition, edge detection, and Digital Image Correlation (DIC) in the measurement of the dynamic response of a vehicle passing over the bridge. [PERSON] [13] detected and monitored structural defects in infrastructure to explore intelligent recognition through dynamic and static measurements in displacement monitoring. [PERSON] [14] proposed an estimation method of structural displacement response based on the fusion concept of contact acceleration monitoring and non-contact displacement identification and obtained structural vibration response and modal information. [PERSON] [15] proposed a new multiple-camera machine vision bridge modal detection method and achieved the extraction of the structural displacement and modal of a bridge through indoor and outdoor experiments. [PERSON] [16] proposed high-precision structural top displacement measurement using drone photography technology and the optical flow method and achieved high-precision measurement of large-range structural top displacement in model vibration table tests. In traditional machine vision, structural displacements are achieved by tracing the preinstalled marker on the structure surface as a feature, but the monitoring locations are limited because of the cumbersome marker installation. Therefore, researchers have aimed to study a marker-free machine vision structural displacement measurement method.