## 4 Discussion
The main limitations of VSS, which emerged during the analysis, concern the following: (i) improving robustness and accuracy through optimization algorithms [54, 63, 71, 73]; (ii) the lack of real-world scenarios (only 46% of the studies proposed in-field tests on real-world structures); (iii) the use of specific targets [67, 81, 82]; (iv) constraints on image and video sizes [60, 64, 76, 77, 83]; and (v) the effects of environmental and weather conditions that can affect performance (44% of the studies proposed only indoor and scaled-target experimental scenarios, with controlled lightning and environmental conditions). This last point is fundamental when using technologies and support aids such as UAVs and tripods. For example, wind can alter the camera stability (and consequently, the performance) if not suitably compensated for. At the same time, poor lighting (due to rain, fog, or evening hours) can reduce visibility and the accuracy of measurements [69, 75]. The analysis performed on IDS showed that only three studies implemented a framework based on supervised classifiers (machine learning approach) and two studies based on computer vision. In contrast, 76% of the studies investigated deep learning approaches. Almost 43% of the studies addressed only binary classification, thus determining whether an area is damaged or undamaged. Only 28.5% of the studies also addressed multi-class classification purposes, mainly related to identifying the type of damage, type of materials, and structural components. Finally, three studies investigated the potential of transfer learning, i.e., using a pre-trained model for a different task to overcome issues related to the time to train a network/model from scratch. This approach was used in [107, 110, 113], which use pre-trained neural networks on images from different sources, targets, materials, and infrastructures. The main limitations of IDS concern the following: (i) in general, the results are presented only on images from the original datasets (through training, validation, and testing phases), without testing trained models on different images, except in [110, 111, 113]; (ii) the use of training datasets of limited size, as in [95, 103, 106]; (iii) a lower agreement between labelers of damaged areas, as in [104]; (iv) the model performance was dependent on image resolution and quality [101, 107, 111]; (v) the need for high-performance hardware (especially for deep learning networks) to manage training phases and optimize execution time on larger image databases; and (vi) high accuracy in binary classification, but lower performance in multi-class classification. Despite the problems and challenges remaining, this review has highlighted the potential of computer vision and image processing in the context of SHM. There is undoubtedly ample room for improvement to overcome the major weaknesses highlighted by the analysis of selected VSS and IDS studies. Nevertheless, thanks to constant technological progress, the availability of increasingly high-performance resources, and the growing interest in the creation of large image databases (also exploiting techniques such as transfer learning and data augmentation), these methodologies will respond more and more comprehensively to the specific requirements of structural health monitoring, as is already happening in the field of human health [116, 117, 118, 119, 120, 121].