## 5 Extraction Deformation and Discussion  
The MATLAB edge function and the Canny edge detector were adopted to extract the grayscale and contour from each image in the static image sequences of the test bridge under different damage conditions. Based on the extracted feature, the contours were marked on the original images. Then, the marked images and the subsequently taken images were compiled into a dataset. After that, the SIFT algorithm was applied to establish the spatial relationship between the fields of view, and the optical flow algorithm was used to track the displacement information of the lower edge contour of the main beam, based on the images collected by the NRS method (proposed in this paper). Since there are too many working conditions and the generated data to show well, the bridge tower consolidation point was used as the reference point to compare the nondestructive working condition (con. 1) obtained by the two methods and the working condition (con. 6) that shows the most obvious damage and deformation. In the figure, \"- 1\" represents the data obtained from finite element calculation, and \"- 2\" represents the data obtained from the method proposed in this paper, as shown in Figure 17.  
Figure 17: _Cont._As shown in Figure 17, the deformation curves of our method are less smooth than those of the finite-element method. There are two possible reasons for the lack of smoothness. Firstly, the lower edge of the deck slabs marked in the original images, which was considered as the contour of the bridge structure, is not smooth and even discrete in some places. Secondly, the positions of the marked pixels changed greatly after the bridge deformed, and were not captured accurately through deep learning. The first problem was solved through the contour stacking analysis in structural deformation monitoring, which is a method previously developed by our research team. This method treats the initial contours as known white noises of the system, and subtracts them from the contours acquired under different damage conditions. The second problem calls for improvement of the capture algorithm. Here, the improvement is realized through manual intervention. In this way, the bridge deformation data in the six fields of view were integrated into the global holographic deformation of the test bridge (Figure 18). The deformation map of the test bridge based on the 11 dial gauges is not presented here. Even if fitted, the data collected by these gauges were discrete to demonstrate the global deformation features  
Figure 17: Comparison of two methods for obtaining deformation value for different fields of views: (**a**) Comparison for field of view 1; (**b**) comparison for field of view 2; (**c**) comparison for field of view 3; (**d**) comparison for field of view 4; (**e**) comparison for field of view 5; (**f**) comparison for field of view 6. Figure 18: The global holographic deformation under different damage conditions. of the test bridge. Moreover, the initial state of the test bridge was not measured at the completion. Consequently, the actual stress state of the bridge structure at that moment is impossible to determine. However, the relative deformation of the test bridge in the monitoring period can be obtained from Figure 18. The obtained results were compared with the relative deformation recorded by the dial gauges to verify the accuracy of our method. Out of the many damage conditions, the greatest difference lies between damage conditions 1 (no damage) and damage condition 6 (suspension cables 20-24 are damaged). Thus, these two damage conditions were subjected to stacking analysis and compared in detail (Table 3). Table 3 shows that our NRS method accurately derives the deformation features of the bridge structure from those collected in local fields of view.