## 1 Introduction  
During the service life, engineering structures are subjected to various inherent deterioration processes of structure such as corrosion, fatigue, material creep, and so on. As a result, the deformation of the degraded structure will deviate from the original one. On this end, the structural deformation in eventually used as an important index in the structural health monitoring [1,2]. For instance, the external load and deformation of the structure system generally follows the below relation:  
\[\{d\}=[K]^{-1}[f]\]  
where \(\{d\}\) stands for the deformation state; \([K]\) is the stiffness matrix of the structure; \(\{f\}\) represent the effect induced by the external load. When any damage or deterioration occurs in the structure, the stiffness matrix \([K]\) will change correspondingly, which in turns lead to the inevitable change in the deformation state \(\{d\}\). Therefore, the change in the deformation state can be utilized to evaluate the health state of the structure. The present study focuses on the direct extraction of the overall deformation, rather than approximating the deformation through the data measured at several discrete points. The major advantage of the overall deformation data is to eliminate the error in structural health evaluation caused by insufficient measurement. Traditionally, the structural deformation can be measured by leveling, total station, GPS, vibration sensors, and other equipment. At present, these methods can accurately and rapidly measure the deformation information of structures. However, only the limited key points of the targeting structure can be measured using the above methods, which often lead to insufficient data and, moreover, the insensitivity to structural deterioration [3]. Obviously, the direct solution is to largely increase the number of sensors installed on the structure. However, it is both time and budget consuming, which is not applicable in engineering practices. Alternatively, the digital image full-field structural morphology measurement can be a very ideal solution, which can take the advantages of both the structural damage identification method and digital image processing technology. Therefore, it is very crucial to effectively utilize the structural image features to extract the full-field deformation information of the structure. In recent years, digital image processing technology is eventually employed to measure the overall deformation of the structure. As a kind of remote sensing technique, photogrammetry does not need any contact with the objects, and this can be a great advantage in the deformation monitoring of structures. \"Photogrammetry\" is to set up a base station in a stable area on the front of the target, and then shoot the target, so as to get the shape and motion state of the target according to the image [4]. According to different imaging distances, photogrammetry can be divided into \"space photogrammetry\", \"close-range photogrammetry\", and \"microscopic photogrammetry\" [5]. In structural deformation monitoring, close-range photogrammetry has broad prospects for development [6], and \"Close-range photogrammetry\" means that the distance between the base station and the measured structure is within 300 m [7]. [PERSON] et al. [8] presents a comprehensive review on the recent development of computer vision-based sensors for structural displacement response measurement and their applications for SHM. Importation issues critical to successful measurement are discussed in detail, including how to convert pixel displacements to physical displacements, how to achieve sub-pixel resolutions, and what to cause measurement errors and how to mitigate the errors. However, the article also clearly points out that in many respects, the vision-based sensor technology is still in its infancy. The majority studies have still been focused on measurements of small-scale laboratory structures or field measurements of large structures at a limited number of points for a short period of time. [PERSON] et al.