## 1 Introduction
By comparing the top 20 topics covered by studies published in three five-year periods (2005-2009, 2010-2014, 2015-2019), it is evident, for example, that computer vision (CV), empowered by optical sensors, made its appearance only in the third period; while machine learning, which appeared in the top 20 in the second period, made a significant leaf forward in the third period, ranking eighth. In contrast, the \"damage type\" topic has been addressed regularly since 2005. However, its ranking has recently increased significantly (from 14 th to seventh place), probably due to the spread of automatic classification methods. The ranking related to 2015-2019 still does not mention deep learning (DL), likely because this is a methodology explored intensively in SHM only more recently, and it was ranked lower than other more established topics [16]. As previously pointed out, CV-based solutions are proving to be promising and powerful tools for SHM investigation in large-scale structures due to the recent developments in optical sensors (high-performance cameras), supporting devices (i.e., drones and robots), and enhanced image processing techniques that take advantage of machine and deep learning [17, 18, 19, 20, 21, 22, 23]. These solutions have been shown to be easily implemented as an alternative to manual visual inspections to estimate the properties and integrity of the structures in several SHM areas, including the assessment of specific local (i.e., cracking, spalling, corrosion, and delamination) and global (i.e., vibration, deformation, displacement) structural conditions [24, 25, 26, 27]. Moreover, CV-based solutions offer several advantages over conventional approaches, including non-contact and long-distance measurements, portability (when installed on vehicles), higher spatial information density, cost-effective installation, and automated assessment capability (when combined with artificial intelligence algorithms) crucial in long-term monitoring and the timely triggering of maintenance actions [16, 26, 28]. However, some constraints need to be considered, especially in real-world applications, including the impact of environmental and weather conditions; reference markers on the structure;measurement accuracy; the real-time processing, storage, and transmission of large amounts of data (images and videos); calibration procedures; and visual optimization techniques, especially over large distances [29]. On this line of research, this paper reviews the most recent studies (2018-2022) in the literature that have proposed solutions regarding the structural health monitoring of infrastructure using optical devices (specifically, standard color cameras), computer vision, and image processing approaches. Two electronic reference databases (Scopus and the Web of Science) were explored through ad hoc queries to select published articles based on well-defined keywords. Subsequently, the automatically selected articles were manually screened to include only those that met the established eligibility criteria. This review aims to provide a comprehensive overview of vision-based solutions for SHM and to analyze them from the perspective of target structures, monitoring types, instrumentation and data sources, methodological approaches, testing scenarios, and main results. Detailed information was collected from the full text to facilitate comparison between studies and to provide general statistical information concerning the resulting categories. In summary, the main contribution of this article the evaluation of recent work using optical sensors and image/video signals for SHM, focusing on solutions that can also be applied to real-world cement-based infrastructure.