## 4 Discussion  
This paper provides an overview of recent applications (2018-2022) in structural health monitoring using optical devices (in particular, standard color cameras), computer vision, and image and video signals. To this end, we queried two electronic reference databases (Scopus and the Web of Science) using the built-in functions to select studies with specific mandatory and optional keywords in their title and abstract. The automatically selected items were then manually screened against pre-established eligibility criteria, ultimately including 73 articles, which we subsequently analyzed in detail, in this review. Two interesting aspects emerge from the first, more general analysis of the selected studies. The first one is the preliminary breakdown of the studies into two macro-categories: those implementing vision systems (71%) and those using image databases (29%), in most cases, made available by other studies. While the first group was fairly expected, having used terms commonly used in the field of vision-based systems in the optional keywords, the second category, however significant, was somewhat unexpected, considering no related terms were used in the queries. This fact is explainable if we consider that these studies deal with one of the components of a vision-based system, namely image and video processing, regardless of the acquisition phase and data source. However, this group was undoubtedly underestimated here compared with what is available in the literature, and it has been analyzed in recent dedicated reviews [16, 114, 115]. Nevertheless, this review also considered these studies as resulting from the selection process. The second aspect is the sharply increasing trend (+61.5%) in the number of SHM applications over a relatively short observation period (only the last five years). This result confirms findings from papers over extended periods (fifteen years) that pointed out the rapid escalation of vision-based solutions and automatic learning approaches [8]. The in-depth analysis showed that VSS studies mainly focus on bridges (35.7%) and buildings (25.0%). Moreover, some solutions address specific targets (e.g., towers, retention basins). On the contrary, 19.6% of the studies report a more general target (category \"structure (generic)\"). Regarding IDS, the main target infrastructures are the same (bridges, buildings, and general structures). However, the highest percentage (25%) concerns concrete surfaces, probably because several databases containing thousands of images acquired for this purpose are available. In addition, as with vision-based systems, some studies address specific infrastructures (such as tunnels and underwater structures). Regarding the type of SHM, VSS are distributed into four clusters, with a predominance of studies focused on estimating displacements and vibrations (77.4%) rather than on detecting structural damage and cracks on the surface (22.6%). In contrast, IDS studies clearly dominate in structural damage and crack detection (50%). However, 47.5% of the remainder are focused on detecting and recognizing specific damage conditions; therefore, they are always attributable to the two predominant clusters. Unlike VSS, only 2.5% of the studies focused on vibration estimation. Going into more detail, the full-text analysis of VSS showed that 85% of the studies implemented a single-camera system, thus overcoming the problems related to the calibration of multiple cameras; 19% of the studies used smartphone cameras, trying to switch to more widespread and low-cost sensors; and about 15% of the studies took advantage from emerging technologies (UAVs, USVs, and robots) for support for reaching remote locations. In addition, 63% of the studies verified the accuracy of measurements through a validation procedure with other systems/sensors commonly used as gold standards in SHM applications.