## 3 Results
### Studies Using Image Databases (IDS)
#### 3.3.1 Overall Statistics  
This section reports and discusses general statistical information about the studies in Table 2. The first analysis concerns the type of infrastructure addressed in the study and the type of SHM implemented (Figures 5a and 5b, respectively).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Author** & **Target** & **SHM Category** & **Study Objectives** & **Methods** & **Database Features** & **Hardware** & **Main Results** \\ \hline \multirow{9}{*}{\begin{tabular}{} \end{tabular} } & \multirow{9}{*}{\begin{tabular}{} \end{tabular} } & \multirow{9}{*}{\begin{tabular}{} \end{tabular} } & \multirow{9}{*}{\begin{tabular}{} \end{tabular} } & \multirow{9}{*}{\
\begin{tabular}{} \end{tabular} } & \multirow{9}{*}{
\begin{tabular}{} \end{tabular} } \\  & & & & & &  
The analysis revealed that frameworks using image datasets were implemented on different types of infrastructure, explicitly addressing more general targets (such as bridges, buildings, and concrete surfaces), as well as specific examples (such as underwater structures or tunnels). Again, the item \"structures (generic)\" refers to solutions suitable for multiple types of infrastructures, while the item \"walls\" could be equated with concrete surfaces.  
Regarding the type of SHM, the analysis revealed more categories than the four that emerged for VSS. The two main ones (\"cracks detection\" and \"damage detection\") are more generalized and typically concern the presence or absence of structural damage. The other categories, with much lower percentages, concern the detection of specific deteriorations and refer to a much finer classification of the type of damage. Obviously, this fineness is highly dependent on the availability of images supporting this type of investigation.  
Additional relevant information about IDS concerns the hardware of the processing unit. The need for higher or lower performance hardware is closely related to the type of framework implemented. Deep learning-based solutions demand significant computational resources, particularly GPU, CPU, and RAM, to handle and process the images. Most studies have used very high-performance graphics cards (in particular, NVIDIA GeForce) and at least 8 GB of RAM (up to a maximum of 64 GB). In contrast, solutions using machine learning and computer vision are much less demanding in terms of computational resources, so much so that in most cases, the hardware configuration was not specified (3 out of 5 studies). The analysis also found that accuracy is the primary metric used to evaluate and compare performance among classification solutions (14 studies). More sporadically, some studies compared performance using MIoU (1 study), F1-score (3 studies), mean square error (1 study), segmentation indices (1 study), and AUC (1 study), whereas 3 studies did not expose a precise metric. Finally, although execution time is one of the crucial aspects of the classification process, only 5 studies have reported results in this regard.  
As just pointed out, accuracy is one of the most widely used metrics in automated classification (i.e., detection and recognition of specific damage conditions) to evaluate the performance of networks and predictive models. However, accuracy highly depends on the number of classes to be recognized. The following sub-section provides a detailed analysis of the studies shown in Table 2 in regards to this aspect.