# GNSS-Assisted Low-Cost Vision-Based Observation System for Deformation Monitoring
###### Abstract
Among the applications of the traditional terrestrial close-range photogrammetry for monitoring, it is worth mentioning the studies published recently: [25] proposes a kind of one-image photogrammetry and its integration with geodetic alignment measurements; [26; 27] explore photogrammetric deformation monitoring using low-cost cameras, particularly for bridges; [28; 29] explore photogrammetric deformation monitoring using a target tracking approach (the studies demonstrate one step toward measurement automation); [30] presents a pan-tilt-zoom camera-based displacement measurement system for the detection of building destruction; and [31] is a case of monitoring using the roving camera technique. Of course, the main advantage of photogrammetric technologies is the opportunity to measure as many points on the structure as necessary. However, just a simple monitoring task of a 100 m building creates an unsolvable problem for terrestrial photogrammetry due to the impossibility of capturing the building surface from the ground. Even if it were possible, the errors due to perspective distortions and resolution would downgrade the overall accuracy to an unacceptable level. Unlike terrestrial photogrammetry, UAV photogrammetry, thanks to its higher mobility, permits the collection and, consequently, the reconstruction of a more detailed building model. However, technically, UAV photogrammetry presents the same terrestrial photogrammetry case but with higher data redundancy [31]. Today, UAV photogrammetry has a versatile application for structural monitoring. A wide range of publications similar to [32] have been published, e.g., paper [33], where UAV photogrammetry was applied for vibrations monitoring, and [34], a presentation of a lab-scale test for a six-story building model where displacement determination was carried out using UAV image correlation. Once more, the automation of UAV data processing is not simple stuff. Besides spatial displacement determination, crack monitoring is a top-rated application of close-range photogrammetry, primarily due to the simplicity of the cracks' identification in images and their measurements. A comprehensive review of crack detection is given in [35; 36]. A feature of the crack measurements is the sufficiency of only one image for measurements. On the other hand, the data for crack detection are easy to process and automate. The papers [37; 38] present monitoring solutions for automated crack detection using machine learning. These papers are based on computer vision principles. The photogrammetric principles for crack monitoring are deployed in [38; 39]. Except for terrestrial-based monitoring of cracks, UAV-based technologies have become very popular recently [38; 40; 41]. The paper [40] studies a new computationally efficient vision-based crack inspection method implemented on a low-cost UAV with a new algorithm designed to extract useful information from the images. The disadvantage of UAV-based observations is the low accuracy that does not satisfy current requirements. So, UAV data can be used as a supplementary data source for structure deformation monitoring. Since we are on the way to developing a vision-based system, let us pay more attention to the photogrammetric methods and approaches. Considering terrestrial close-range photogrammetry, it is necessary to mention the classic books [42; 43] that provide the most comprehensive review of close-range photogrammetry. Despite the versatility of the presented photogrammetric instances, they are all based on a standard algorithm and math background. This means that, regardless of the structure, any study comprises a geodetic network creation or assignment of some reference base (coordinate system), target marking and coordinating, and compliance with the general requirements of the photogrammetric survey [43]. The knowledge that came from computer vision treats and handles images differently than classical photogrammetry.