## 5 Conclusions  
Structural health monitoring has received significant attention in recent decades because of the importance of keeping infrastructure in good condition and in full working order. Non-contact solutions are gradually finding application in this area because they are easier to manage and more practical than contact-based approaches. Among these, solutions based on cameras, computer vision, imaging, and video processing are proving particularly effective because they provide the ability to carry out visual inspections with more continuity, even in hard-to-reach areas, thanks in part to the support of new technologies (drones and robots) that can operate remotely. A further impetus comes from the potential offered by automatic classification algorithms (machine and deep learning approaches) that, thanks to increasingly high-performance hardware, make it possible to detect and recognize the type and level of structural damage. This trend is expected to increase in the coming years, also benefiting from the data fusion of multiple sensors for the increasingly timely and comprehensive detection of structural damage and the activation of appropriate maintenance actions, thus ensuring the safety and efficiency of civil infrastructure.  
However, several challenges still need to be addressed to achieve the maximum benefits of these approaches, especially in real-world scenarios [26]. For example, when using optical approaches, environmental conditions (light changes, weather events, obstructive elements) can interfere with structural monitoring. Another factor concerns the effect of vibrations caused by the ground or wind, which can make the image captured by a fixed camera or drone blurry, thus altering the performance, especially over long distances. Another major challenge relates to the amount of data to be managed, stored, and/or transmitted; for example, in long-term monitoring, images and videos, particularly when high resolution is necessary, are large and require special attention for their use, especially in automated and remote damage assessment solutions. In addition, machine learning and deep learning approaches, which can support automatic damage recognition and localization, require huge datasets of images with well-labeled damage to be effective and accurate. Nevertheless, the availability of larger datasets could be addressed by merging smaller datasets, using transfer learning, or through data augmentation approaches. All these factors may affect the applicability of optical approaches in SHM or introduce uncertainties in measurements, so they need to be considered during data acquisition and processing. However, these are well-known problems in computer vision, and ad hoc solutions can be designed to solve and overcome these limitations, as has been done in other fields, benefiting from the use of these approaches for SHM as well.  
Conceptualization, C.F.; methodology, C.F., G.A. and G.P.; software, C.F.; validation, C.F., G.A. and G.P.; formal analysis, C.F., G.A. and G.P.; investigation, C.F., G.A. and G.P.; resources, C.F., G.A. and G.P.; data curation, C.F., G.A. and G.P.; writing--original draft preparation, C.F. and G.P.; writing--review and editing, C.F., G.A. and G.P.; visualization, C.F.; supervision, C.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.