## 1 Introduction  
Traditional damage detection primarily involves installing sensors on the structure, such as laser ranging sensors, resistive strain sensors, and acceleration sensors. Employing these sensors incurs substantial labor costs and installation time [1, 2]. Additionally, different sensor types have their own limitations. For example, they may have a limited measurement response range, and their accuracy can be affected by temperature and humidity changes. Moreover, the pre-installation wiring layout of sensors, as well as subsequent maintenance and replacement, are also expensive undertakings. Therefore, many scholars have explored indirect non-contact measurement methods from several angles. For example, [PERSON] et al. [3] utilized a total station to measure the vertical settlement and horizontal displacement before tunnel construction, but the application of total station was limited by the deployment points, such as a large number of equipments, e.g., cables and lighting equipment. [PERSON] et al. [4] proposed an improved Variable Mode Decomposition (VMD) method to obtain accurate displacement trends from the initial GNSS signals. [PERSON] et al. [5] combined the GNSS and Inertial Measurement Unit (IMU)methods and conducted simulation experiments on a shaking table using a three-antenna GNSS/IMU integrated device. However, the GNSS-based method is affected by factors such as signal strength and transmission frequency during satellite transmission, thus affecting the positioning accuracy. Some scholars have investigated the use of Laser Doppler Vibrometer (LDV) for detection. [PERSON] et al. [6] utilized LDV to monitor hydroelectric power plant structures under dynamic conditions and determined the degree of damage of each structural component through the change of eigenfrequency. [PERSON] et al. [7] validated a drone-mounted LDV inspection system and successfully detected surface defects in cylindrical concrete specimens. [PERSON] et al. [8] utilized LDV to scan concrete beams in order to obtain multiple test point data and detect the location of structural damage. [PERSON] et al. [9] placed metal blocks at structural offsets and used LDV to measure the free vibration response of a building model under different offset loads to obtain the material parameters of the building model. However, the limitation of this LDV equipment is the reflection of light through the test points, which is not suitable for comprehensive measurements of large-scale structures. [PERSON] et al. [10] scanned and obtained the modal vibration patterns of a footbridge using a device with a radar participation system (IBIS-FS). [PERSON] et al. [11] used Ground Penetrating Radar (GPR) to survey a footbridge and successfully detected defects within the concrete. Although radar systems are suitable for non-contact, high-frequency measurements, the vibration signals are weak and prone to distortion, making accurate measurements challenging. In recent years, computer vision-related programs and algorithms have been developing rapidly, and more and more scholars have begun to explore the intelligent recognition method that combines image processing technology with deep learning models, and the mainstream methods currently applied to vibration displacement testing include Digital Image Correlation (DIC) method, template matching method, and optical flow method. The DIC method is to process the image data and use the related processing software to calculate the displacement and deformation of the selected area of the object under test, etc. [PERSON] et al. [12] combined the template matching method with the sub-pixel method to realize the high-precision identification of multi-point damage of the components, and [PERSON] et al. [13] verified a method that relies on a high-speed camera to measure the unidirectional displacement of the curved flat plate. [PERSON] et al. [13] validated a method to measure the unidirectional displacement of a curved flat plate by a high-speed camera. [PERSON] et al.